skill-creator

Skill Creator

A skill for creating new skills and iteratively improving them.

At a high level, the process of creating a skill goes like this:

• Decide what you want the skill to do and roughly how it should do it
• Write a draft of the skill
• Create a few test prompts and run claude-with-access-to-the-skill on them
• Help the user evaluate the results both qualitatively and quantitatively
  • While the runs happen in the background, draft some quantitative evals if there aren't any (if there are some, you can either use as is or modify if you feel something needs to change about them). Then explain them to the user (or if they already existed, explain the ones that already exist)
  • Use the eval-viewer/generate_review.py script to show the user the results for them to look at, and also let them look at the quantitative metrics
• Rewrite the skill based on feedback from the user's evaluation of the results (and also if there are any glaring flaws that become apparent from the quantitative benchmarks)
• Repeat until you're satisfied
• Expand the test set and try again at larger scale

Your job when using this skill is to figure out where the user is in this process and then jump in and help them progress through these stages. So for instance, maybe they're like "I want to make a skill for X". You can help narrow down what they mean, write a draft, write the test cases, figure out how they want to evaluate, run all the prompts, and repeat.

Six standing disciplines apply throughout, because these failure modes ship convincing-looking skills that are wrong:

1. Verify before you write. Every technical assertion that enters the skill (endpoint, parameter, command, version, behavior) must trace to something you executed and observed — in this session or an explicitly approved mined one. Can't verify it right now? Either go verify it, or mark it explicitly ("unverified — from memory"). A skill multiplies whatever it contains: verified knowledge compounds, and so do confidently-stated errors. For knowledge skills (content is mostly facts about an external system — API endpoints, parameters, fields, platform behavior), read references/knowledge-skill-grounding.md for the operational version: the authority ladder (observed behavior > machine-readable contract > exercised production code > official docs > memory), evidence-scope annotation, pre-ship doc-example smoke runs, and the audience/Windows portability checklist. A source-grounding audit once found multiple confident contract claims that contradicted evidence already available to the author (methodology Case 9).

2. Treat "impossible / not supported" as a hypothesis, not a conclusion. When a capability seems blocked (an API error wall, a tool that won't connect, a format that won't open), exhaust the observation paths — the UI's own network traffic, an alternative channel, a different documented identifier — before writing "the platform doesn't support this" into a skill. Observed behavior outranks speculative request shapes.

3. Stand on the field's shoulders — retrieve the domain's established best-practices into context BY DEFAULT, before authoring or optimizing a skill's methodology. A skill's methodology is only as good as the knowledge in your context window, not the knowledge latent in your weights: pretraining is lossy, goes stale, and often is not even activated unless the canonical sources are actually pulled in. So the quality ceiling of what you write is your training data + the user's input — unless you deliberately retrieve the subject domain's real prior art. Do it: WebSearch the field's canonical theory / standards / methods, and read any bundled or installed skill in that domain, then fold the load-bearing principles into the skill with attribution. This is a different axis from "Prior Art Research" below — that finds tools/infrastructure to reuse; this grounds the quality of the methodology itself in the discipline's accumulated science. Make it the default action, not something you wait to be asked for: briefly tell the user which field you're pulling from and let them say "skip," but never ship a methodology capped by your memory plus their prompt when 40 years of the field's public work is one search away. Examples: a data-visualization skill must absorb Cleveland & McGill's graphical-perception ranking and Bertin's visual variables (position/length beat color beat text — measured, not aesthetic); a date/time skill must surface the mature libraries and their canonical pitfalls; a persuasion/negotiation skill must retrieve the established frameworks rather than reinvent them from memory. If the canonical knowledge lives only in your weights and never enters context, you are guessing where you could be citing.

4. Preserve before you compress an existing skill. Updating an existing skill is a migration, not a blank-page rewrite. Before the first edit, capture the complete old bundle with the audit tool's snapshot command, or reconstruct it from an explicit Git ref; an arbitrary copy plus a provenance label is not a baseline. Inventory runtime capabilities, trigger contexts, interfaces, references, and eval coverage. Progressive disclosure and concision authorize moving or deduplicating content; they do not authorize silently deleting behavior. After editing, run scripts/audit_skill_regression.py and classify every unmatched old unit. A runtime contract that survives only in evals/, tests, or an unlinked reference is still lost. Do not call the update complete while any candidate is unclassified or any true gap remains unfixed. The same logic governs reversals, not just deletions, and covers any prior commitment — not only the ones carrying a date and a name: overturning a decision already made is a proposal, never a side effect. Say it out loud and get it accepted. A silent rewrite is worse than a silent deletion, because it destroys the artifact and the evidence that could have caught it in one move — and it blinds every downstream reviewer (see #5).

5. Nothing ships on self-review alone — an independent, fresh-context adversarial pass is a standing step, not a special case. Your "it's good now" judgment runs on the same model that produced the artifact, so it shares the exact blind spots that produced the defect. Measured, not folklore: intrinsic self-correction without external feedback does not reliably improve output and sometimes degrades it (Huang et al., ICLR 2024), and when generator and evaluator share error modes, iterating raises confidence without adding information. More self-checks cannot escape that; only an outside view can. Full procedure — prompt templates, anchor selection, the findings table, worked cases — in references/independent-review-protocol.md; read it before your first pass. The load-bearing rules:

   • Independence has two faces, and the second one is the one authors miss. The reviewer's context must not be a fork of yours — a fork inherits your blind spots and hands back a "reviewed" stamp. But the evidence it measures against must also sit outside the change's blast radius, or it inherits your conclusion: it reads an artifact and a spec that already agree, and reports agreement. Note "not edited this session" is too weak a line — in the reference's controlled case the poisoned record was edited the previous day, and three reviewers went blind to a defect they caught in its untouched twin. Rank anchors by how hard they are for you to have touched: the user's own transcript words > a git ref predating the work (git log --oneline <ref>..HEAD -- <path>; any commit of yours disqualifies it) > an append-only log (a convention, not an enforcement). Greenfield has no anchor — say so and ask for one rather than reporting a pass you did not run.
   • Give it exactly two things: the artifact, and the reader spec. Nothing else — no design rationale, no project background, no "just confirm X is fine," which converts an independent reviewer into a rubber stamp. The reader spec is a specification, not rationale, so it cannot rubber-stamp anything; omitting it wastes half the pass, because the reviewer then measures against itself. For a SKILL.md the reader is another agent executing it, and its failure mode is "I don't know which tool to call," not "I don't know this word" — ask which instructions it could not act on. State the spec before the run and never use it afterwards to explain findings away.
   • It is ground truth for comprehensibility and for completeness-against-a-corpus; it has no authority over taste. Apply those two directly. Treat "this might be a bug / I'd suggest Y" as a hypothesis and reproduce it yourself first. Never delegate AI-slop or aesthetic judgment — same-model blind spot.
   • Enumerate failure axes, not content areas — that also decides how many reviewers you run. The axis is the question you ask; the area is the material you read. Three reviewers covering scenarios, arithmetic, and the diff but all asking "is this coherent?" is one reviewer billed three times. One is the default and frequently sufficient; add one only for an additional axis. The axis self-review is worst at is fidelity — "is this still faithful to commitments already made?" — because the author is the one who moved the commitment, and coherence and fidelity are orthogonal: an artifact can be flawlessly self-consistent while being completely unfaithful to what was decided.
   • Leave an artifact, or this is just a warning. Discipline #6 says a check yielding an opinion loses to one yielding an artifact — so this one produces a file too, or it loses to completion-drive exactly when it matters. Write independent-review.md under skill-reviews/<skill-name>/ in your private, git-tracked knowledge repo: the reviewer prompt verbatim (so a later reader can see whether it was leading), the findings with a disposition and reason each (which is what separates legitimate filtering from discarding what hurts), and what could not be checked. If you don't know which repo is your private knowledge repo (or don't have one), say so and ask the user — do not guess a location that lands in either forbidden zone. Two forbidden locations: NOT in <skill-name>-workspace/ (gitignored scratch dirs that get wiped — this file is cross-session review evidence and must survive them) and NOT in any repo that is or may become public or distributed — which normally rules out the reviewed skill's own repo (review content inherently quotes private paths, real names, and project details). Re-review with a new agent after a substantive edit — a rule, contract, or number changed, not a typo. From Step 5 onward this file is the evidence the pass happened; its absence means it did not.
   • Discipline #4's regression gate is mechanical and complementary: it proves you did not delete behavior. It says nothing about whether what you wrote can actually be followed.

6. Design the checks you write so they cannot self-certify green. Skills are largely made of checks — gates, checklists, "before you ship" steps — and a check that the executing context can pass while violating the very rule it encodes is worse than no check, because it manufactures confidence. Four rules, borrowed from fields that solved this before software:

   • The verification must cover every clause of its rule. If the rule says "A + B + C," the evidence must demand proof of A, and B, and C separately. One confirm line bolted onto a three-clause rule gets satisfied by whichever clause the author already did; the others are invisible. Real case: a report-authoring skill carried a delivery gate whose rule read "options as side-by-side chips + recommendation highlighted + background written as complete, self-sufficient sentences a stranger could follow" — but the evidence line under it asked only for "N decision items, all rendered as chips." The author ran the gate, wrote that evidence, self-certified green, and shipped a page whose labels were single characters with all the context deleted. The rule sat in the file the entire time; the check simply never measured that clause.

   • A check that misfires on healthy input is worse than no check. The failure above is a check that passes when it should fail; this is its mirror — a check that fails when it should pass. It is the more expensive one, because it teaches the operator to bypass reflexively (--no-verify, SKIP=1, --force), and once that reflex exists the gate is off for every input, including the ones it was built for. So when authoring a fail-closed check, false positives outrank false negatives: missing one real problem costs you that instance, while killing one healthy input costs you the entire gate. Watch for the tell: the frustration of having hit the same trap repeatedly is itself the risk signal — it is exactly the state in which an author ships a defense that was never calibrated against healthy input. Real case: after stepping on one formatting trap three times in a day, the author added a regex check to a linter; it killed 33 healthy inputs on the project's own corpus and was reverted the same hour. Calibrate before you arm it — run any fail-closed check across real, known-good material and confirm zero false positives; prefer loosening it until it occasionally misses over letting it ever misfire.

   • Make each item a falsifiable observation, not a self-assessment. "Background is self-sufficient" cannot be failed by the person who wrote it; "cover the rest of the page, read one card alone, and state what it is deciding" can. Prefer checks that yield an artifact — a command's output, a quoted line, a screenshot — over checks that yield an opinion.

   • The same suspicion applies to the checks you run, not just the ones you write. The four rules above govern checks that ship inside a skill. But the greps, finds and one-off scripts you use to verify your own work are instruments too, and a wrong instrument reports a clean result just as confidently as a right one. In one 2026-07 session five separate verification commands lied in both directions: a find without -L reported an installed skill's files missing (they were behind a symlink); a grep --exclude-dir=<name> hid a second copy of the very thing being audited; an inverted shell condition raised a false alarm that a removal had not happened; a regex spanning newlines invented 55 "lost quotations"; and a search over two of five files reported two rules missing that were present in the third. Every one of them was believed at first, and every one was caught only by re-running a differently-shaped check.

     The fix is the oldest one in experimental practice: run the instrument on a case whose answer you already know before trusting it on the case you don't. Grepping for a string you expect to be absent? First grep for one you know is present, in the same command shape — if that returns 0 too, the command is broken, not the file. This costs one line and converts "I checked" into "I checked with an instrument I calibrated."

     Two specific shapes worth memorizing, because both appeared above and both fail silently: find does not follow symlinks without -L (and skill installs are frequently symlinks into a source repo), and --exclude-dir matches by basename everywhere in the tree, not just at the path you had in mind.

     And there is a second half to this rule that only bites when the check SHIPS: calibrate against the standard implementation, not the one on your machine. The instrument rule above keeps your conclusion honest; this keeps the reader's working. A tool-behavior claim written into a skill — a flag, a recursion mode, an option that "follows symlinks" — is executed on machines whose binaries you have never seen, and the divergence is silent on both ends: it works when you test it, and it quietly does nothing for them. Two mechanisms produce this, and both are invisible from inside a session: the same command name resolves to a different program (a shell alias or function shadowing the binary — note \tool only escapes an alias, so command tool or an absolute path is the only deterministic form), and the same program behaves differently across implementations (BSD vs GNU vs a drop-in replacement). Real case (2026-07): an author verified that grep -R follows symlinks, wrote it into a skill as the fix for a symlink trap, and shipped it to a 1200-star public repo — their grep was ugrep via a shell function; on macOS's own /usr/bin/grep the same -R matches nothing (it needs -RS), so the prescribed fix failed silently for most readers, inside the very section warning that validators fail silently.

     So: before a tool-behavior assertion enters a shipped artifact, re-run it against the standard binary (/usr/bin/<tool>), not the one your shell hands you. If it does not survive that, do not write the flag — prefer the implementation-independent formulation: resolve the path yourself (readlink -f) instead of betting on a recursion flag, do a substring test in a script instead of a line-oriented match, name the behavior you need instead of the option you happen to know. A prescription that only works in your environment is worse than no prescription, because the reader has no way to discover that it silently did nothing.

   • Use what mature checklist practice already settled. Decide whether a list is READ-DO (execute while reading — for low-frequency or unfamiliar procedures) or DO-CONFIRM (work from expertise, then stop at a defined pause point and confirm — for experienced operators under time pressure), and anchor it at a real pause point rather than "somewhere in the workflow." Keep it to the killer items — critical and commonly missed under pressure, roughly five to nine; everything beyond that dilutes compliance (Gawande, The Checklist Manifesto). And prefer Shingo's control over warning (poka-yoke): a prose reminder depends on vigilance and loses to completion-drive, while a step that blocks progress or forces an artifact needs no vigilance at all. Where a skill can only warn, at least put the warning where the decision gets made — a rule filed in a reference the executing context never opens is not, in practice, a rule.

On the other hand, maybe they already have a draft of the skill. In this case you can go straight to the eval/iterate part of the loop.

Of course, you should always be flexible and if the user is like "I don't need to run a bunch of evaluations, just vibe with me", you can do that instead.

Then after the skill is done (but again, the order is flexible), you can also run the skill description improver, which we have a whole separate script for, to optimize the triggering of the skill.

Cool? Cool.

First: coexistence check (official skill-creator plugin)

Before anything else, run one quick check (a single grep, no output needed on the common path): does ${CLAUDE_CONFIG_DIR:-~/.claude}/plugins/installed_plugins.json contain "skill-creator@claude-plugins-official"?

• Not present (the common case): do nothing — do not install anything, do not mention this section to the user. Proceed with the engagement.
• Present: the official plugin's skill-creator and this edition now sit in the skill list with near-identical descriptions, so future sessions will route between them at random. Tell the user this in one or two sentences, then offer (never act without their consent):
  1. Recommended — run scripts/setup_supersede_hook.sh install. It copies a small self-checking SessionStart hook into their Claude config and registers it in settings.json (with a backup), so every future session deterministically routes skill work to this edition. Reversible with scripts/setup_supersede_hook.sh uninstall; the official plugin stays fully usable when asked for by name. On machines without the official plugin the installer refuses to install anything, so it can never leave a useless hook behind.
  2. Alternative — claude plugin disable skill-creator@claude-plugins-official (reversible with enable), which removes the ambiguity by taking the official entry out of the skill list entirely.

If the hook is already installed (scripts/setup_supersede_hook.sh status shows the SessionStart entry as present), skip all of this silently.

The same machinery is available for skills the user creates: when their skill deliberately overlaps an installed one, generate them a kit with scripts/generate_supersede_kit.py — see "Coexistence & Precedence" under Prior Art Research and references/skill-precedence-and-coexistence.md.

Communicating with the user

The skill creator is liable to be used by people across a wide range of familiarity with coding jargon. If you haven't heard (and how could you, it's only very recently that it started), there's a trend now where the power of Claude is inspiring plumbers to open up their terminals, parents and grandparents to google "how to install npm". On the other hand, the bulk of users are probably fairly computer-literate.

So please pay attention to context cues to understand how to phrase your communication! In the default case, just to give you some idea:

• "evaluation" and "benchmark" are borderline, but OK
• for "JSON" and "assertion" you want to see serious cues from the user that they know what those things are before using them without explaining them

It's OK to briefly explain terms if you're in doubt, and feel free to clarify terms with a short definition if you're unsure if the user will get it.

Using AskUserQuestion (Critical — Read This)

Use the AskUserQuestion tool aggressively at every decision point. Do not ask open-ended text questions in conversation when structured choices exist. This is the single biggest UX improvement you can make — users juggle multiple windows and may not have looked at this conversation in 20 minutes.

Every AskUserQuestion MUST follow this structure:

1. Re-ground: State the skill name, current phase, and what just happened (1-2 sentences). The user may have context-switched away.
2. Simplify: Explain the decision in plain language. No function names or internal jargon. Say what it DOES, not what it's called.
3. Recommend: Lead with your recommendation and a one-line reason why. If options involve effort, show both scales: (human: ~X min / Claude: ~Y min).
4. Options: Provide 2-4 concrete, lettered choices. Each option should be a clear action, not an abstract concept.

Rules:

• One decision per question — never batch unrelated choices
• Provide an escape hatch ("Other" is always implicit in AskUserQuestion)
• Accept the user's choice — nudge on tradeoffs but never refuse to proceed
• Skip the question if there's an obvious answer with no tradeoffs (just state what you'll do)

Creating a skill

Capture Intent

Start by understanding the user's intent. The current conversation might already contain a workflow the user wants to capture (e.g., they say "turn this into a skill"). If so, extract answers from the conversation history first — the tools used, the sequence of steps, corrections the user made, input/output formats observed. The user may need to fill the gaps, and should confirm before proceeding to the next step.

Source inventory — always before drafting, with consent boundaries. Inventory the live conversation and existing docs/skills that overlap (see Prior Art Research below). Earlier local session JSONL files are a separate private source: do not open or parse them unless the user explicitly asks to mine history or affirmatively approves that source after you explain what will be read. If approved, fold only relevant prior sessions in through the conversation-mining workflow's redacted extraction; never load raw transcripts into your own context. If not approved, continue from the live conversation and existing project sources without treating the missing history as a blocker.

When mining a conversation (or session transcripts), inventory two kinds of assets — they land in different places. Knowledge — endpoints, parameters, pitfalls, decision rules — becomes SKILL.md guidance or references/. Code the session had to write — helper scripts, injected snippets, renderers, one-off templates — is a scripts/ candidate: if this session wrote it, the next invocation will have to rewrite it, so parameterize it, sanitize it, and bundle it. A prior distillation captured polished prose but omitted the reusable helpers; the general lesson is to keep both knowledge→references and code→scripts channels in frame.

When the source material is past session transcripts (the JSONL files under the Claude Code projects directory) rather than the live conversation, do not load them into your own context — a large transcript can exhaust the window and lose the session. Delegate extraction to subagents instead, with explicit instructions to parse line-by-line with a script, truncate every extracted field, and return only a distilled lessons list — the raw transcript never enters the main context.

First, resolve which DIRECTION this is — before the four questions below. The request may be one of several opposite things: build a NEW skill / edit an EXISTING skill / optimize skill-creator itself / or it's not-a-skill-at-all (a one-off task). Guessing wrong wastes the whole session — the research you'd do for "new skill" is the wrong research for "optimize the meta-tool." When the phrasing is ambiguous (e.g. "make me a skill" while pointing at skill-creator's own path), one AskUserQuestion here costs 30 seconds. The wrapper-skill fork below is one special case of this; the direction check is general.

1. What should this skill enable Claude to do?
2. When should this skill trigger? (what user phrases/contexts)
3. What's the expected output format?
4. Should we set up test cases to verify the skill works? Skills with objectively verifiable outputs (file transforms, data extraction, code generation, fixed workflow steps) benefit from test cases. Skills with subjective outputs (writing style, art, taste-calibrated reports) often can't use assertions — but "no assertions" is not "no verification". Their verification paths, in order of cost:
   • Historical-task replay: re-run one real prompt the skill has served before, old vs new skill, and compare outputs against the specific rules that changed ("does the new output actually follow the tokens / title grammar this update introduced?"). Cheap, catches "the rule was written but nothing reads it".
   • Production-as-eval: acknowledge that the real test is the user's next actual use — then make the loop explicit: every user correction afterward is an incident to fold back (the skill's own "迭代/活文档" section), every approval is corpus material. A taste skill that ships without this write-back habit doesn't improve; one that has it converges without ever running a formal eval.
   • Render + human review for visual outputs (the skill's own visual-QA gates), never a grep assertion pretending to measure aesthetics. And the renderer you verify with must be the same engine the deliverable will be consumed in — whatever previewer is conveniently installed is not a substitute. A thumbnailer whose layout engine differs from the target application will silently hide the exact defects you are looking for, and a green verification on the wrong engine is worse than no verification, because it buys false confidence. Real case (2026-07): a .docx was "visually verified" through macOS Quick Look thumbnails, which do not reproduce justified-text stretching; Word showed the document's info blocks blown apart the moment the user opened it. The fix was to install the Word-compatible engine (LibreOffice), convert to PDF, rasterize per page, and read every page. Match the engine, or the verification is theater. This generalizes past renderers to every verification tool — parser, linter, validator: it must share an implementation with production, or its green is meaningless. Second case, same shape: an author tried to catch a markup pattern that corrupts the final document by checking at the source stage with a different markdown implementation than the production toolchain used — it parsed all three known-bad inputs as perfectly fine, so any pre-check built on it would have silently passed everything. The honest conclusion was that this particular defect is only detectable after the production tool has run, and the check belongs there. When no available tool shares the production implementation, say the check cannot be done at that stage — do not build the one that can only produce false green. Suggest the appropriate default based on the skill type, but let the user decide.

After extracting answers from conversation history (or asking questions 1-3), use AskUserQuestion to confirm the skill type and testing strategy:

Creating skill "[name]" — here's what I understand so far:
- Purpose: [1-sentence summary]
- Triggers on: [key phrases]
- Output: [format]

RECOMMENDATION: [Objective/Subjective/Hybrid] skill → [suggested testing approach]

Options:
A) Objective output (files, code, data) — set up automated test cases (Recommended if output is verifiable)
B) Subjective output (writing, design) — qualitative human review only
C) Hybrid — automated checks for structure, human review for quality
D) Skip testing for now — just build the skill and iterate by feel

This upfront classification drives the entire evaluation strategy downstream. Get it right here to avoid wasted effort later.

The extend-vs-create check — runs BEFORE any specialized branch

Each of the three specialized workflows below ends with "do not continue reading the sections below", and Prior Art Research happens to sit after them. That ordering is layout, not execution order. The extend-vs-create judgment applies to every branch, and skipping it is exactly how a session ships a skill that duplicates one already installed.

So before routing into wrapper-skill / conversation-mining / artifact-corpus, answer one question: does a skill already exist that this capability belongs to?

Discover the roots, don't recall them. A hand-maintained list of install locations is exactly the artifact that goes stale, and the root you forget is the one that bites.

Search for the file, not for a directory named skills. Skill directories are named after the skill (skill-creator/, <suite>/<skill>/), so a source repo, a marketplace clone and a plugin cache contain no directory called skills at all — searching for that name silently skips them while appearing to work. Every skill has a SKILL.md; that is the layout-agnostic handle.

# 1) discover
find ~ -type f -name SKILL.md -not -path '*/node_modules/*' -not -path '*/.git/*' > /tmp/all-skills.txt

# 2) VERIFY COVERAGE BEFORE TRUSTING IT — `2>/dev/null` and permission denials hide gaps
#    silently, which is exactly how a sweep reports "nothing found" from a root it never
#    entered. A 0 on any line you expect means the search did not go there:
for r in '/.claude/skills/' '/plugins/marketplaces/' '/plugins/cache/' '/.claude-profiles/'; do
  printf '%6s  %s\n' "$(grep -c "$r" /tmp/all-skills.txt)" "$r"
done
# ...and grep for your own skill source repos by path; they must appear too.

# 3) filter by capability VOCABULARY, not by skill name — in every language the target
#    skill might be written in (a skill whose body is Chinese will not match English terms):
xargs grep -li -e '<domain-term>' -e '<域内术语>' < /tmp/all-skills.txt

Expect step 3 to take a few seconds and to still return more than you want; narrow with terms specific to the capability rather than generic ones (chart matches everything, stacked bar does not).

The roots this reaches — and that a from-memory list usually misses: the skill source repos (a claude-code-skills checkout and any -pro sibling), ~/.claude/plugins/marketplaces/ and ~/.claude/plugins/cache/ (marketplace-installed suites — nothing in the source repos hints they are there), ~/.claude/skills/, ~/.codex/skills, ~/.agents/skills, per-profile config homes (~/.claude-profiles/<name>/), and — the one with no signposts at all — every project's own .claude/skills/. Step 2 is what makes that a claim you verified rather than one you inherited.

Per-project skills are structurally invisible. They live inside an unrelated project's working tree, so they appear in no marketplace, no global skill list, and no source-repo listing; nothing you would normally open while planning a new skill mentions them. Real case (2026-07): a session built a global skill for a domain, swept the source repos, the global dirs and the other-agent dirs, found nothing, and shipped. A later conversation-history search turned up a mature project-level skill for that exact domain, a month old, sitting in one project's .claude/skills/ — carrying eight rules the new skill lacked, including one the user had personally dictated. Every root had been checked except the per-project one, and the sweep reported "no prior art" with complete confidence.

What to do when the overlap is a project-level skill in an unrelated project — the case that war story lands you in, and the one the three bullets below do not cover: you cannot add a sibling to a suite it has none of, and "extend it" would mean editing an unrelated project's working tree. The move that worked: harvest its rules into the skill you are building, then retire the project-local one with the owner's consent — it was written against real work, so treat it as the more mature source and reconcile toward it. Retiring someone's working skill is the owner's decision, not a side effect of your build.

Four things that sentence leaves out, each of which will stop you:

• "Reconcile toward it" is a rebuttable presumption, not a rule. Two standard exits: the project skill may be stale (rules written months ago against a system that moved), and it may be project-specific (rules that only hold under that project's constraints — importing them wholesale makes your skill narrow, which this file elsewhere tells you not to do). Harvested rules are another author's memory, so re-verify each one the way discipline #1 requires of anything you write into a skill.

• "Retire" needs a mechanism, and its first step is not the one you reach for. In order:

  1. find the skill's bodies, before grepping for its references. A skill routinely has more than one copy in the same repo — .claude/skills/<name>/ and .agents/skills/<name>/ are both loaded, by different tools, from the same working tree. Grep answers "who mentions it"; only find answers "how many of it are there".

     find <project> -type d -name '<skill-name>' -not -path '*/.git/*'
     

     Do not put the skill's own name in --exclude-dir (it matches by basename, so it hides every same-named directory including the copy you have not found — see the instrument rule in discipline #6). Real case (2026-07): a retirement did exactly that, fixed all five references it found, and left a second full copy under .agents/skills/ — git-tracked, no retirement marker, a stale snapshot missing the newest rule — which the other tool would still load as live.
  2. Verify the new home is actually reachable from where the old one was, before deleting anything — a marketplace skill you just pushed is not installed until the marketplace is updated and the plugin installed, and retiring first leaves a window with neither:

     claude plugin marketplace update <marketplace>   # your push is not their cache
     claude plugin install <skill>@<marketplace>
     find -L ~/.claude/plugins/cache -path '*<skill>*' -name '*.md'   # -L: installs are often symlinks
     

     The -L is not optional — plugin caches frequently symlink into a source repo, and a bare find reports the files missing (see the instrument rule in discipline #6).
  3. Then grep for references and repoint the live ones. Distinguish live instructions (a skill list, a cross-reference, a handoff doc telling the next agent what to use) from historical records (a decision log entry saying "on date X we shipped this") — rewriting the second destroys an audit trail to fix a problem it does not have.
  4. Then replace each body with a superseded by <skill> stub rather than a bare deletion, and make every copy's stub byte-identical. Keep the YAML frontmatter — a SKILL.md without it may fail to load rather than fail informatively — but rewrite the description so the stub announces its own retirement instead of advertising the old triggers; otherwise it keeps winning the routing it no longer serves. The body needs only: where the capability went, and one line on why it moved.

• Identify the owner with the ownership test below (it applies here too: a project-level skill has no marketplace.json, but the project's git remote still tells you whose it is). When the owner is the person you are talking to, "consent" is one AskUserQuestion. When the owner is unreachable, harvest only — do not retire (which leaves both skills live and competing for the trigger, same end state as a declined retirement — see Coexistence & Precedence below).

• If the owner declines to retire it, you now have two skills competing for the same trigger; that is the Coexistence & Precedence problem below, not a failure.

Search by capability vocabulary, not by skill name. That project skill would not have matched a name search for the new skill's title; it matched on the domain terms inside its body. Grep the candidate roots for the concepts the new skill will handle.

If something overlaps:

Deciding which bullet applies — whose skill is it? A filesystem hit does not carry ownership. Read the marketplace's .claude-plugin/marketplace.json owner field, or git remote -v in the containing repo; a hit under ~/.claude/plugins/marketplaces/ can just as easily be your own marketplace installed back onto your machine. A project-level skill has no marketplace.json, but its project's git remote answers the same question.

Two cases the probes get wrong or cannot answer, so check for them before trusting the result: a fork shows your own remote while the content is someone else's — treat it as third-party, because their upstream improvements still stop reaching you. And when there is no marketplace.json and no remote (a local-only project, a skill hand-copied into a global skills dir), the probes are silent rather than negative: ask the owner instead of guessing.

• The overlap is a third party's skill (a marketplace suite, an official plugin): do not re-implement its capability. Write a thin increment that drives it correctly — the pitfalls you hit, the correct invocation, the verified helper script — and reference it by namespaced name. Cloning someone else's engine into your bundle is the expensive mistake: their upgrades stop reaching you, and the two copies drift apart silently.
• The overlap is your own skill: extend it, or add a sibling inside its existing suite. A standalone that competes for the same triggers helps nobody. Exception: if it lives inside an unrelated project's working tree, neither move applies — see the project-level case above.
• Some related skill already points at the gap you're filling (e.g. its description says "for X, use Y"): after you build, close the loop — update that pointer, or you have left a dangling reference behind.

Only when nothing overlaps do you build standalone.

Why this check earns its place at the top: a real 2026-07 session spent a day getting a third-party docx engine to produce correct Chinese business documents, then reached for the wrapper-skill branch — which skips straight past Prior Art Research. The shape it was about to ship was a fresh skill re-carrying that engine's capability. The correct shape was a three-layer reference chain: third-party engine untouched → a thin increment skill holding the correct usage plus the verified generator script → the domain-workflow skill calling that increment. The user had to catch it twice before it landed, with the second correction being the sharper one: "don't copy an extra one — write the correct usage on top of theirs, and reference their skill; that's what skill-as-code means."

Specialized Workflow: Wrapper Skills for Third-Party CLI Tools

Before committing to the generic skill-creation flow, check whether the session that led up to this point actually calls for the wrapper skill workflow instead. A wrapper skill is a companion that installs, configures, diagnoses, and repairs a pre-existing third-party CLI tool or skill package — code that someone else wrote and that the user has just spent a session getting to work on their machine.

Signals this applies (any two together are enough):

• The user has been installing a tool in the current conversation — downloading a .zip, running npx / pip install / brew install, dealing with an official installer.
• The session has produced real, concrete error messages and the user and Claude have worked out concrete fixes for them (edited files, added flags, bypassed aliases).
• The user says something like "wrap this up as a skill", "save this as a wrapper skill", "so other people don't have to go through this again", "把这次 session 做成一个 skill".
• The user explicitly mentions a third-party tool by name and wants other agents or other people to be able to use it without the learning curve they just paid.

Signals it does not apply (use the generic workflow above instead):

• The user wants a skill for something they're going to write from scratch.
• The session was smooth — no real friction to capture.
• The skill would wrap a service the user owns or controls (it's their code; edit the source instead of wrapping it).
• The "tool" is actually a methodology or workflow that doesn't involve installing any binary or package.

When the wrapper skill workflow applies, do not continue reading the sections below. Jump to workflows/wrapper-skill/workflow.md and follow that workflow end-to-end. It is a retrospective distillation workflow — its job is to mine the current conversation for the install flow, the bugs that were fixed, and the design decisions that were made, and to turn that mining output into a complete, self-contained wrapper skill that another user can install and benefit from without reliving the debugging session.

The wrapper skill workflow has its own architecture contract, code templates, and verification protocol — it does not share test-case infrastructure with the generic workflow, because its output is a user's install state rather than a file that can be easily asserted on. The canonical reference implementation is the ima-copilot skill (at the root of the daymade/claude-code-skills repository — a bare relative link here already broke once when this skill moved into a suite, exactly as the cross-skill-reference rule below warns), a wrapper around the Tencent IMA skill distilled from a real session using this exact workflow.

Specialized Workflow: Enrich a Skill from Conversation History

Before committing to the generic skill-creation flow, check whether the session is actually asking to distill past conversations into a skill. This is useful when the user has been debugging, designing, or exploring a topic over multiple Claude Code / Codex sessions and wants to turn the accumulated know-how into reusable references/.

Signals this applies (any one is enough):

• The user says something like "mine my chat history for patterns", "turn this conversation into a skill reference", "distill what we learned into the skill", "enrich this skill from my conversations", or "把这次对话沉淀到 skill 里".
• The session is explicitly about extracting lessons from a recent multi-turn debugging or design session.
• The user wants to add a references/ file to an existing skill based on real conversations they have already had.
• The target skill already exists, and the goal is to enrich it with conversation-mined knowledge rather than build it from scratch.

Signals it does not apply (use the generic workflow above instead):

• The user is creating a brand-new skill from a single prompt or idea.
• The user wants a wrapper around a third-party CLI tool they just installed (use the wrapper-skill workflow above).
• There is no local conversation history to mine and no transcript exports to process.
• The mined content is one-time personal notes that should live in memory/ rather than a reusable reference file.
• The source material is a batch of finished artifacts the user has endorsed, rather than dialogue — use the artifact-corpus-distillation workflow below.

When the conversation-mining workflow applies, do not continue reading the generic sections below. Jump to workflows/conversation-mining/workflow.md and follow that workflow end-to-end. It is a retrospective distillation workflow: it discovers local Claude Code project sessions, Codex transcripts, and command histories, redacts them, partitions them into agent-sized chunks, runs mining agents, and promotes the resulting candidate references into the target skill's references/ after validation.

The conversation-mining workflow has its own architecture contract, agent prompts, templates, and verification protocol. It is the canonical way to turn real conversation history into a skill's reusable knowledge base.

Specialized Workflow: Distill User Preferences from an Approved-Artifact Corpus

Before committing to the generic flow, check whether the session is asking to extract the user's real preferences from a batch of finished artifacts they have endorsed — approved HTML report pages, generated documents, designs. This is the third distillation source, distinct from the two above: the material is products, not conversations, and the output is taste made executable (explicit principles, quantified parameters, vocabulary), not knowledge or install fixes.

Signals this applies (any one is enough):

• The user lists finished artifacts and says "这些都是我认可的样例" / "你来学到底什么是我想要的" / "extract my preferences from these approved examples".
• A taste-calibration skill (report generator, doc styler, deck builder) has an approved-sample corpus that keeps growing, and the user asks to make the skill learn from it rather than just index it.
• The user complains that a previous update "只加了示例" — only cataloged samples without changing skill behavior.

Signals it does not apply: the source material is dialogue/corrections rather than endorsed products (use conversation-mining); the samples are not personally approved by the user (approval is the admission gate — ask first).

When it applies, jump to workflows/artifact-corpus-distillation/workflow.md. Its core discipline, which also applies any time you add material to an existing skill: cataloging ≠ distillation — registering a sample in a corpus table changes nothing about the skill's next run; ask of every addition "does this change a decision rule?", and do not declare a distillation session done while the answer is no for everything written (methodology Case 15). The workflow's spine: script-extracted quantitative comparison across ALL artifacts (≥3-artifact threshold per pattern, checked exception lists per claimed constant) → layered induction with evidence anchors → write to the decision-rule layer (separating invariants from register-dependent variables) → independent completeness audit (standing discipline #5) → regression audit.

Prior Art Research (Do Not Skip)

The user's private methodology — their domain rules, workflow decisions, competitive edge — is what makes a skill valuable. No public repo can provide that. But the user shouldn't waste time reinventing infrastructure (API clients, auth flows, rate limiting) when mature tools exist. Prior art research finds building blocks for the infrastructure layer so the skill can focus on encoding the user's unique methodology.

Two axes, don't conflate them. This section sources the infrastructure layer (tools / MCPs / libraries / existing skills to reuse). The methodology layer has two inputs of its own: the user's private edge (theirs alone, un-retrievable) and the domain's established best-practices / science, which you retrieve into context by default per standing discipline #3. Finding the right tool does not discharge the second — a viz skill that adopts a charting library but never absorbs Cleveland/Bertin is still capped at your pretraining. Do both.

Search these channels in order (use subagents for 4-8 in parallel):

Channels 1-3 surface the user's own proven patterns and existing integrations. Channels 4-8 find public infrastructure. The user's private SOP always takes precedence — public tools are building blocks, not replacements. In competitive domains (finance, trading, proprietary operations), the valuable methodology will never be public.

Bias toward merge/extend over create-new, and sweep EVERY skill root — not just ~/.claude. When channels 1-3 turn up an existing skill that overlaps the requested domain, the usual right move is to extend or merge into it — except when it lives in an unrelated project's working tree, where the direction reverses: harvest from it into the skill you are building rather than merging into it (see the project-level case in the extend-vs-create check above) (one real "new skill" task became "make the existing extractor the extract-phase of the new archiver"), not to ship a parallel skill that competes for the same triggers — two overlapping skills fight over triggering and confuse users. When searching, discover the install roots rather than recalling a list — use the SKILL.md sweep and its coverage self-check from the extend-vs-create section above (searching for a directory named skills misses source repos, marketplace clones and plugin caches entirely, because their skill directories are named after the skill). Run the coverage check rather than trusting this sentence: every project's own .claude/skills/ is the root a from-memory list reliably drops, because nothing outside that project references it. A skill the user already installed anywhere is the strongest prior art there is, and a project-local one is often the most mature: it was written against real work.

If a public MCP server or skill is found, clone it and verify — don't trust the README:

1. Read the actual source code — many projects have polished READMEs on hollow codebases
2. Verify auth method — does it match how the API actually authenticates? (X-Api-Key headers vs Bearer vs OAuth — many get this wrong)
3. Check test coverage — zero tests = prototype, not production-grade
4. Check maintenance — last commit date, open issue count, response to bug reports
5. Check environment compatibility — proxy/network assumptions, hardcoded DNS/IPs, region locks
6. Check license — MIT/Apache is fine; GPL/SSPL may conflict with proprietary use
7. Check dependency weight — huge dependency trees create conflict and security surface

Decision matrix:

Coexistence & Precedence (deliberate overlap)

Merging into the existing skill is the default fix for overlap (above). But when the user deliberately ships a skill that overlaps an installed one — a fork of an official plugin, a hardened in-house edition — the two entries will sit in the skill list with similar descriptions and Claude will route between them at random. Resolve it, in escalating order: rename if the overlap is accidental; add a description tiebreaker ("supersedes X — when both appear, always use this one"); and for distributed forks, stamp a conditional supersede kit into the skill with scripts/generate_supersede_kit.py — a consent-based SessionStart routing hook that only ever installs on machines where the competitor is actually present, refuses to install elsewhere, and self-disables if either side disappears. Mechanics, decision table, SKILL.md sample wording, and sandbox verification: references/skill-precedence-and-coexistence.md. This skill dogfoods the same kit against the official skill-creator plugin (see "First: coexistence check" at the top).

The more common case: your new skill silently loses the trigger to the installed population, without any deliberate fork. A skill's domain (image generation, PDF handling, dashboards) is often already crowded with several installed skills, and a fresh skill can lose auto-routing to all of them. So verify triggering early — the build isn't done when the content is good. After a draft exists, fire a few realistic queries through claude -p and check the new skill actually WINS; if it doesn't, name the specific competitor it lost to (different queries often lose to different skills). Then know two things: (1) prose can't always win a crowded slot — the resolution ladder is rename → description tiebreaker/SUPERSEDES → manual invocation → SessionStart routing hook (structural; modifies global config, so requires the user's explicit consent, same discipline as --no-verify); and (2) the fix depends on who authored the competitor — competitors that are third-party → accept manual invocation or a routing hook; competitors that are your own → merge/consolidate them into one, don't keep two of your skills fighting for the same trigger. (The full resolution ladder lives in references/skill-precedence-and-coexistence.md — that file is the SSOT; the summaries here and above are pointers, don't extend them independently.) Documenting the chosen path (e.g. an "Activation" note saying "invoke manually, competitors are third-party") stops the next session from re-litigating it. (methodology Case 13)

Complementary Skills (bundle, don't depend)

When building a skill that touches a domain with an existing complementary skill, you have two choices:

• Depend on it being installed: fragile — the user may not have it, or may have a different version. Every missing-dependency failure traces back to this choice.
• Bundle it: copy the complementary skill's self-contained assets (scripts, templates, reference docs) into your own bundle, and wire them up so your skill works standalone.

Rule: if a sub-capability your skill needs is provided by another installable skill, bundle it. This is especially important for:

• Statusline / UI rendering scripts (e.g., statusline-generator's generate_statusline.sh)
• Shared validation / sanitization scripts
• Common data transformation utilities

Example: claude-switch-models-setup manages multiple Claude Code profiles. Each profile needs a statusline. The statusline-generator skill provides generate_statusline.sh. Rather than depending on the user running statusline-generator first, the profile setup skill bundles statusline.sh and wires it into each new profile during claude-profiles-init. The two skills remain independently useful, but the wrapper skill works standalone.

Anti-pattern: writing "run other-skill's installer first" in your SKILL.md. That pushes the dependency to the user and creates a fragile install order. Bundle instead.

After research completes, present findings via AskUserQuestion:

Research complete for "[skill-name]". Here's what I found:

[1-2 sentence summary of what exists publicly]

RECOMMENDATION: [ADOPT / EXTEND / BUILD] because [one-line reason]

Options:
A) Adopt [tool/MCP X] for infrastructure, build methodology layer on top (Recommended)
B) Extend [partial tool Y] — use what works, fill gaps in the skill
C) Build from scratch — nothing found matches well enough
D) Show me the detailed findings before I decide

When in doubt, bias toward adopting mature infrastructure for the plumbing layer and building custom logic for the methodology layer — that's where the value lives.

Interview and Research

Proactively ask questions about edge cases, input/output formats, example files, success criteria, and dependencies. Wait to write test prompts until you've got this part ironed out.

Check available MCPs - if useful for research (searching docs, finding similar skills, looking up best practices), research in parallel via subagents if available, otherwise inline. Come prepared with context to reduce burden on the user.

Write the SKILL.md

Based on the user interview, fill in these components:

• name: Skill identifier
• description: When to trigger, what it does. This is the primary triggering mechanism - include both what the skill does AND specific contexts for when to use it. All "when to use" info goes here, not in the body. Note: currently Claude has a tendency to "undertrigger" skills -- to not use them when they'd be useful. To combat this, please make the skill descriptions a little bit "pushy". So for instance, instead of "How to build a simple fast dashboard to display internal Anthropic data.", you might write "How to build a simple fast dashboard to display internal Anthropic data. Make sure to use this skill whenever the user mentions dashboards, data visualization, internal metrics, or wants to display any kind of company data, even if they don't explicitly ask for a 'dashboard.'"
• compatibility: Required tools, dependencies (optional, rarely needed)
• the rest of the skill :)

Skill Writing Guide

Anatomy of a Skill

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter (name, description required)
│   └── Markdown instructions
└── Bundled Resources (optional)
    ├── scripts/    - Executable code for deterministic/repetitive tasks
    ├── references/ - Docs loaded into context as needed
    └── assets/     - Files used in output (templates, icons, fonts)

YAML Frontmatter Reference

All frontmatter fields except description are optional. Configure skill behavior using these fields between --- markers:

---
name: my-skill
description: What this skill does and when to use it. Use when...
context: fork
agent: general-purpose
argument-hint: "[topic]"
---

Special placeholder: $ARGUMENTS in skill content is replaced with text the user provides after the skill name. For example, /deep-research quantum computing replaces $ARGUMENTS with quantum computing.

Inline vs Fork: Critical Decision

This is the most important architectural decision when designing a skill. Choosing wrong will silently break your skill's core capabilities.

CRITICAL CONSTRAINT: Subagents cannot spawn other subagents. A skill running with context: fork (as a subagent) CANNOT:

• Use the Task tool to spawn parallel exploration agents
• Use the Skill tool to invoke other skills
• Orchestrate any multi-agent workflow

Decision guide:

Example: Orchestrator skill (MUST be inline):

---
name: product-analysis
description: Multi-path parallel product analysis with cross-model synthesis
---

# Orchestrates parallel agents — inline is REQUIRED
1. Auto-detect available tools (which codex, etc.)
2. Launch 3-5 Task agents in parallel (Explore subagents)
3. Optionally invoke /competitors-analysis via Skill tool
4. Synthesize all results

Example: Specialist skill (fork is correct):

---
name: deep-research
description: Research a topic thoroughly using multiple sources
context: fork
agent: Explore
---

Research $ARGUMENTS thoroughly:
1. Find relevant files using Glob and Grep
2. Read and analyze the code
3. Summarize findings with specific file references

Example: Reference skill (inline, no task):

---
name: api-conventions
description: API design patterns for this codebase
---

When writing API endpoints:
- Use RESTful naming conventions
- Return consistent error formats

Composable Skill Design (Orthogonality)

Skills should be orthogonal: each skill handles one concern, and they combine through composition.

Pattern: Orchestrator (inline) calls Specialist (fork)

product-analysis (inline, orchestrator)
  ├─ Task agents for parallel exploration
  ├─ Skill('competitors-analysis', 'X') → fork subagent
  └─ Synthesizes all results

competitors-analysis (fork, specialist)
  └─ Single focused task: analyze one competitor codebase

Rules for composability:

1. The caller must be inline (no context: fork) to use Task/Skill tools
2. The callee should use context: fork to run in isolated subagent context
3. Each skill has a single responsibility — don't mix orchestration with execution
4. Share methodology via references (e.g., checklists, templates), not by duplicating code

Pipeline Handoff (Sequential Skill Chaining)

Beyond orchestrator/specialist composition, skills often form sequential pipelines where one skill's output is the next skill's input. Each skill should proactively suggest the logical next step after completing its work.

Pattern: "Next Step" section at the end of SKILL.md

## Next Step: [Action Description]

After [this skill completes], suggest the natural next skill:

\```
[Summary of what was just accomplished].

Options:
A) [Next skill] — [one-line reason] (Recommended)
B) [Alternative skill] — [when this is better]
C) No thanks — [the current output is sufficient]
\```

Real-world pipeline examples:

youtube-downloader → asr-transcribe-to-text → transcript-fixer → meeting-minutes-taker → pdf-creator
deep-research → fact-checker → ppt-creator
doc-to-markdown → docs-cleaner
claude-code-history-files-finder → continue-claude-work

Rules for pipeline handoff:

1. Every handoff is opt-in via AskUserQuestion — never auto-invoke the next skill without asking
2. Suggest only when the output naturally feeds into another skill — don't force connections
3. Include a "No thanks" option — the user may not need the full pipeline
4. The suggestion should explain why the next step helps (e.g., "ASR output typically contains recognition errors")
5. Keep it to 1-2 recommendations max — too many choices cause decision fatigue

When to add a handoff: Ask "does this skill's output commonly become another skill's input?" If yes, add a "Next Step" section. If the connection is rare or forced, don't add one.

Anti-pattern: Chaining skills that don't share a natural data flow. pdf-creator → youtube-downloader makes no sense. The pipeline must follow the user's actual workflow.

Auto-Detection Over Manual Flags

Never add manual flags for capabilities that can be auto-detected. Instead of requiring users to pass --with-codex or --verbose, detect capabilities at runtime:

# Good: Auto-detect and inform
Step 0: Check available tools
  - `which codex` → If found, inform user and enable cross-model analysis
  - `ls package.json` → If found, tailor prompts for Node.js project
  - `which docker` → If found, enable container-based execution

# Bad: Manual flags
argument-hint: [scope] [--with-codex] [--docker] [--verbose]

Principle: Capabilities auto-detect, user decides scope. A skill should discover what it CAN do and act accordingly, not require users to remember what tools are installed.

Invocation Control

Progressive Disclosure

Skills use a three-level loading system:

1. Metadata (name + description) - Always in context (~100 words)
2. SKILL.md body - In context whenever skill triggers
3. Bundled resources - As needed (unlimited, scripts can execute without loading)

Key patterns:

• SKILL.md length should be driven by information density, not a line count target. A 600-line skill with no filler is better than a 200-line skill that omits critical knowledge and forces the model to guess. If the skill is getting long, ask: "Is every section earning its keep?" If yes, keep it. If sections are padded or explain things Claude already knows, trim those — not the useful content. When a skill genuinely covers many domains, split into references by domain rather than artificially cramming everything into a short main file.
• For an existing skill, progressive disclosure is a relocation strategy, not a deletion heuristic. The old runtime contract must remain reachable from SKILL.md in the packaged bundle; a trigger query, test assertion, changelog entry, or reviewer memory is not a runtime replacement.
• Reference files clearly from SKILL.md with guidance on when to read them
• For large reference files (>300 lines), include a table of contents

Domain organization: When a skill supports multiple domains/frameworks, organize by variant:

cloud-deploy/
├── SKILL.md (workflow + selection)
└── references/
    ├── aws.md
    ├── gcp.md
    └── azure.md

Claude reads only the relevant reference file.

Principle of Lack of Surprise

This goes without saying, but skills must not contain malware, exploit code, or any content that could compromise system security. A skill's contents should not surprise the user in their intent if described. Don't go along with requests to create misleading skills or skills designed to facilitate unauthorized access, data exfiltration, or other malicious activities. Things like a "roleplay as an XYZ" are OK though.

Writing Patterns

Prefer using the imperative form in instructions.

Defining output formats - You can do it like this:

## Report structure
ALWAYS use this exact template:
# [Title]
## Executive summary
## Key findings
## Recommendations

Examples pattern - It's useful to include examples. You can format them like this (but if "Input" and "Output" are in the examples you might want to deviate a little):

## Commit message format
**Example 1:**
Input: Added user authentication with JWT tokens
Output: feat(auth): implement JWT-based authentication

Usability patterns worth building into a skill - These four structural elements repeatedly turned a "works but confusing" skill into one users could actually drive — they were the concrete enhancements that made a heavily-used skill usable, so reach for them when a skill has modes, runs commands, or can be re-run:

• Entry decision-tree: if the skill has multiple modes, open with a tiny "user said X → use mode Y" map so the model routes correctly instead of guessing.
• Expected-output block after each command: show "what you should see" right after a command, so the model (and the user) can tell real success from silent failure.
• Troubleshooting section: enumerate the known failure modes and their fixes — the single most valuable section for a skill others will run on machines you can't see.
• Step-0 idempotency guard: if re-running could redo finished work, open with a cheap "is this already done?" check before doing anything expensive.

Writing Style

Try to explain to the model why things are important in lieu of heavy-handed musty MUSTs. Use theory of mind and try to make the skill general and not super-narrow to specific examples. Start by writing a draft and then look at it with fresh eyes and improve it.

Dates and Version References

Keep factual dates — they tell readers when information was verified. A skill about Suno v5.5 should say "Suno v5.5 (March 2026)" because without the date, future readers can't judge if the information is still current. Removing dates makes things worse, not better.

What to avoid is conditional logic based on dates ("if before August 2025, use the old API") — that becomes wrong the moment the date passes and nobody updates it.

Rules:

• Release dates, "last verified" dates: Keep them. They're reference points, not expiration dates
• Pricing, rankings, legal status: Include but mark as volatile ("~$0.035/gen as of last check") so readers know to re-verify
• "Before X date do Y, after X date do Z": Don't write this. Pick the current method and optionally document the old one in a collapsed/deprecated section

Bundled Resources

Scripts (scripts/)

Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.

• When to include: When the same code is being rewritten repeatedly or deterministic reliability is needed
• Example: scripts/rotate_pdf.py for PDF rotation tasks
• Benefits: Token efficient, deterministic, may be executed without loading into context
• Note: Scripts may still need to be read by Claude for patching or environment-specific adjustments
• User-mutable data lives outside the bundle: if a script accumulates user data (correction dictionaries, learned preferences, caches), store it under a stable home-relative directory (e.g. ~/.<skill-name>/) with its own backup — never inside the skill directory. Skill installs are wiped and re-created on every update and suite migration; a home-relative store survives them untouched. This is how a dictionary-accumulating skill survived a full suite migration with zero user data loss

References (references/)

Documentation and reference material intended to be loaded as needed into context to inform Claude's process and thinking.

• When to include: For documentation that Claude should reference while working
• Examples: references/finance.md for financial schemas, references/mnda.md for company NDA template
• Use cases: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
• Benefits: Keeps SKILL.md lean, loaded only when Claude determines it's needed
• Best practice: If files are large (>10k words), include grep search patterns in SKILL.md
• Avoid duplication: Information should live in either SKILL.md or references files, not both

Assets (assets/)

Files not intended to be loaded into context, but rather used within the output Claude produces.

• When to include: When the skill needs files that will be used in the final output
• Examples: assets/logo.png for brand assets, assets/slides.pptx for PowerPoint templates
• Use cases: Templates, images, icons, boilerplate code, fonts, sample documents
• Component shelf: for artifact-generating skills, a components subfolder under assets/ holds user-approved verbatim-embed fragments with frozen behavior contracts and a registry reference — see the Component-shelf check in Step 4

Privacy and Path References

Decide the destination before applying any of this. Everything below describes what breaks when a skill ships to strangers. For a skill that lives in the author's own private repo, the same "violations" are usually why it works — a real absolute path is what makes the script runnable, a real account in a template is what saves the next run from re-filling it. quick_validate auto-detects this (it asks gh whether the containing repo is private) and downgrades portability/identifier findings to notes there; --audience=public forces the strict pass when a private skill is being prepared for release.

In a private skill, these findings are the owner's call, not yours. Do not placeholder a real path, rewrite a hardcoded credential, or "sanitize" an example without asking — you will break a working tool to satisfy a rule written for a destination it was never going to. If you think something should change, say what you found and let the owner decide. (The design precedent is npm's "private": true: a declared destination that changes what the tooling enforces, added because people kept publishing things by accident.)

CRITICAL for skills intended for public distribution — these must not contain user-specific or company-specific information:

• Forbidden: Absolute paths to user directories (for example, user home directories)
• Forbidden: Personal usernames, company names, product names
• Forbidden: Hardcoded skill installation paths like ~/.claude/skills/
• Allowed: Relative paths within the skill bundle (scripts/example.py, references/guide.md)
• Allowed: Standard placeholders (<workspace>/project, <user>, <organization>)
• Carve-outs (a validator implementing the Forbidden list literally would flag this very skill — misfiring on healthy input is worse than missing): the publisher's own name/brand when a supersede tiebreaker or attribution requires naming it; install paths like ~/.claude/skills/ when the passage is about those paths (teaching material, not a hardcoded dependency)

Cross-skill references: a bare relative path always means "inside this skill's own bundle" — validators and readers both treat it that way, so a bare path pointing at another skill's file fails validation and misleads readers. When pointing at another skill, name the owner in prose ("marketplace-dev's cache-and-source-patterns reference") and invoke skills by their namespaced name (/suite-name:skill-name, not a bare /skill-name). Bare cross-references break silently when skills move between suites — one suite migration left 21 broken cross-references across two cleanup passes because of this.

Versioning

CRITICAL: Skills should NOT contain version history or version numbers in SKILL.md:

• Forbidden: Version sections (## Version, ## Changelog) in SKILL.md
• Correct location: Skill versions are tracked in marketplace.json under plugins[].version
• Rationale: Marketplace infrastructure manages versioning; SKILL.md should be timeless content

Reference File Naming

Filenames must be self-explanatory without reading contents.

Pattern: <content-type>_<specificity>.md

Examples:

• Bad: commands.md, cli_usage.md, reference.md
• Good: script_parameters.md, api_endpoints.md, database_schema.md

Test: Can someone understand the file's contents from the name alone?

Two carve-outs: hyphenated names are as good as underscored ones (the separator was never the point — self-explanation is); and files inside a named workflow directory (workflows/<name>/workflow.md, patterns.md) are directory-qualified — the directory supplies the specificity, and renaming them would break the parallel structure across workflows.

Skill Creation Best Practice

Anthropic has written skill authoring best practices — retrieve it before you create or update any skills: https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices.md

Development Methodology Reference

Also read references/skill-development-methodology.md before starting — it covers the full 8-phase development process with prior art research, counter review, and real failure case studies. The two references are complementary: the Anthropic doc covers principles, the methodology covers process.

Test Cases

After writing the skill draft, come up with 2-3 realistic test prompts — the kind of thing a real user would actually say. Present them via AskUserQuestion:

Skill draft is ready. Here are [N] test cases I'd like to run:

1. "[test prompt 1]" — tests [what aspect]
2. "[test prompt 2]" — tests [what aspect]
3. "[test prompt 3]" — tests [what aspect]

Each test runs the skill + a baseline (no skill) for comparison.
Estimated time: ~[X] minutes total.

RECOMMENDATION: Run all [N] test cases now.

Options:
A) Run all test cases (Recommended)
B) Run test cases, but let me modify them first
C) Add more test cases before running
D) Skip testing — the skill looks good enough to ship

Save test cases to evals/evals.json. Don't write assertions yet — just the prompts. You'll draft assertions in the next step while the runs are in progress.

{
  "skill_name": "example-skill",
  "evals": [
    {
      "id": 1,
      "prompt": "User's task prompt",
      "expected_output": "Description of expected result",
      "files": []
    }
  ]
}

See references/eval_pipeline_schemas.md for the full schema (including the assertions field, which you'll add later).

Running and evaluating test cases

This section is one continuous sequence — don't stop partway through. Do NOT use /skill-test or any other testing skill.

Put results in <skill-name>-workspace/ as a sibling to the skill directory. Within the workspace, organize results by iteration (iteration-1/, iteration-2/, etc.) and within that, each test case gets a directory (eval-0/, eval-1/, etc.). Don't create all of this upfront — just create directories as you go.

Step 1: Spawn all runs (with-skill AND baseline) in the same turn

For each test case, spawn two subagents in the same turn — one with the skill, one without. This is important: don't spawn the with-skill runs first and then come back for baselines later. Launch everything at once so it all finishes around the same time.

With-skill run:

Execute this task:
- Skill path: <path-to-skill>
- Task: <eval prompt>
- Input files: <eval files if any, or "none">
- Save outputs to: <workspace>/iteration-<N>/eval-<ID>/with_skill/outputs/
- Outputs to save: <what the user cares about — e.g., "the .docx file", "the final CSV">

Baseline run (same prompt, but the baseline depends on context):

• Creating a new skill: no skill at all. Same prompt, no skill path, save to without_skill/outputs/.
• Improving an existing skill: the old version captured by the mandatory existing-skill regression gate before the first edit. Point the baseline subagent at that immutable snapshot and save to old_skill/outputs/. If no pre-edit snapshot exists, stop and reconstruct an authoritative baseline from Git before continuing; never use the already-edited tree as the old baseline.

Write an eval_metadata.json for each test case (assertions can be empty for now). Give each eval a descriptive name based on what it's testing — not just "eval-0". Use this name for the directory too. If this iteration uses new or modified eval prompts, create these files for each new eval directory — don't assume they carry over from previous iterations.

{
  "eval_id": 0,
  "eval_name": "descriptive-name-here",
  "prompt": "The user's task prompt",
  "assertions": []
}

Step 2: While runs are in progress, draft assertions

Don't just wait for the runs to finish — you can use this time productively. Draft quantitative assertions for each test case and explain them to the user. If assertions already exist in evals/evals.json, review them and explain what they check.

Good assertions are objectively verifiable and have descriptive names — they should read clearly in the benchmark viewer so someone glancing at the results immediately understands what each one checks. Subjective skills (writing style, design quality) are better evaluated qualitatively — don't force assertions onto things that need human judgment; their real verification paths (historical-task replay, production-as-eval with a write-back habit, render + human review) are listed under question 4 of "Capture Intent".

Update the eval_metadata.json files and evals/evals.json with the assertions once drafted. Also explain to the user what they'll see in the viewer — both the qualitative outputs and the quantitative benchmark.

Step 3: As runs complete, capture timing data

When each subagent task completes, you receive a notification containing total_tokens and duration_ms. Save this data immediately to timing.json in the run directory:

{
  "total_tokens": 84852,
  "duration_ms": 23332,
  "total_duration_seconds": 23.3
}

This is the only opportunity to capture this data — it comes through the task notification and isn't persisted elsewhere. Process each notification as it arrives rather than trying to batch them.

Step 4: Grade, aggregate, and launch the viewer

Once all runs are done:

1. Grade each run — spawn a grader subagent (or grade inline) that reads agents/grader.md and evaluates each assertion against the outputs. Save results to grading.json in each run directory. The grading.json expectations array must use the fields text, passed, and evidence (not name/met/details or other variants) — the viewer depends on these exact field names. For assertions that can be checked programmatically, write and run a script rather than eyeballing it — scripts are faster, more reliable, and can be reused across iterations. But objective grep/script assertions cut both ways (same-word-different-meaning false hits, wording-difference misses), so benchmark pass-rate is a signal, not a verdict: spot-check what each assertion actually matched, and watch for a baseline run that reveals a factual error in the skill itself (the "wait, the data IS in the API" moment). See methodology §5.3–5.6 + §6.4.

2. Aggregate into benchmark — run the aggregation script from the skill-creator directory:

   python -m scripts.aggregate_benchmark <workspace>/iteration-N --skill-name <name>
   

   This produces benchmark.json and benchmark.md with pass_rate, time, and tokens for each configuration, with mean +/- stddev and the delta. If generating benchmark.json manually, see references/eval_pipeline_schemas.md for the exact schema the viewer expects. Put each with_skill version before its baseline counterpart.

3. Do an analyst pass — read the benchmark data and surface patterns the aggregate stats might hide. See agents/analyzer.md (the "Analyzing Benchmark Results" section) for what to look for — things like assertions that always pass regardless of skill (non-discriminating), high-variance evals (possibly flaky), and time/token tradeoffs.

4. Launch the viewer with both qualitative outputs and quantitative data:

   nohup python <skill-creator-path>/eval-viewer/generate_review.py \
     <workspace>/iteration-N \
     --skill-name "my-skill" \
     --benchmark <workspace>/iteration-N/benchmark.json \
     > /dev/null 2>&1 &
   VIEWER_PID=$!
   

   For iteration 2+, also pass --previous-workspace <workspace>/iteration-<N-1>.

   Cowork / headless environments: If webbrowser.open() is not available or the environment has no display, use --static <output_path> to write a standalone HTML file instead of starting a server. Feedback will be downloaded as a feedback.json file when the user clicks "Submit All Reviews". After download, copy feedback.json into the workspace directory for the next iteration to pick up.

Note: please use generate_review.py to create the viewer; there's no need to write custom HTML.

5. Tell the user via AskUserQuestion:

Results are ready! I've opened the eval viewer in your browser.

- "Outputs" tab: click through each test case, leave feedback in the textbox
- "Benchmark" tab: quantitative comparison (pass rates, timing, tokens)

Take your time reviewing. When you're done, come back here.

RECOMMENDATION: Review the Outputs tab first — your qualitative feedback drives the next iteration more than the numbers do.

Options:
A) I've finished reviewing — read my feedback and improve the skill
B) I have questions about the results before giving feedback
C) Results look good enough — skip iteration, let's package the skill
D) Results need major rework — let's discuss before iterating

What the user sees in the viewer

The "Outputs" tab shows one test case at a time:

• Prompt: the task that was given
• Output: the files the skill produced, rendered inline where possible
• Previous Output (iteration 2+): collapsed section showing last iteration's output
• Formal Grades (if grading was run): collapsed section showing assertion pass/fail
• Feedback: a textbox that auto-saves as they type
• Previous Feedback (iteration 2+): their comments from last time, shown below the textbox

The "Benchmark" tab shows the stats summary: pass rates, timing, and token usage for each configuration, with per-eval breakdowns and analyst observations.

Navigation is via prev/next buttons or arrow keys. When done, they click "Submit All Reviews" which saves all feedback to feedback.json.

Step 5: Read the feedback

When the user tells you they're done, read feedback.json:

{
  "reviews": [
    {"run_id": "eval-0-with_skill", "feedback": "the chart is missing axis labels", "timestamp": "..."},
    {"run_id": "eval-1-with_skill", "feedback": "", "timestamp": "..."},
    {"run_id": "eval-2-with_skill", "feedback": "perfect, love this", "timestamp": "..."}
  ],
  "status": "complete"
}

Empty feedback means the user thought it was fine. Focus your improvements on the test cases where the user had specific complaints.

Kill the viewer server when you're done with it:

kill $VIEWER_PID 2>/dev/null

Improving the skill

This is the heart of the loop. You've run the test cases, the user has reviewed the results, and now you need to make the skill better based on their feedback.

How to think about improvements

1. Generalize from the feedback. The big picture thing that's happening here is that we're trying to create skills that can be used a million times (maybe literally, maybe even more who knows) across many different prompts. Here you and the user are iterating on only a few examples over and over again because it helps move faster. The user knows these examples in and out and it's quick for them to assess new outputs. But if the skill you and the user are codeveloping works only for those examples, it's useless. Rather than put in fiddly overfitty changes, or oppressively constrictive MUSTs, if there's some stubborn issue, you might try branching out and using different metaphors, or recommending different patterns of working. It's relatively cheap to try and maybe you'll land on something great.

2. Keep the prompt lean without deleting the contract. Remove things that aren't pulling their weight, but treat every deletion from an existing skill as a regression candidate until the old-vs-new audit classifies it. Move detailed but reusable behavior into a directly linked reference; do not leave it only in evals, a changelog, or your memory. Read the transcripts, not just final outputs—if the skill causes unproductive work, simplify the instruction and rerun the old capability cases rather than assuming fewer words means better behavior.

3. Explain the why. Try hard to explain the why behind everything you're asking the model to do. Today's LLMs are smart. They have good theory of mind and when given a good harness can go beyond rote instructions and really make things happen. Even if the feedback from the user is terse or frustrated, try to actually understand the task and why the user is writing what they wrote, and what they actually wrote, and then transmit this understanding into the instructions. If you find yourself writing ALWAYS or NEVER in all caps, or using super rigid structures, that's a yellow flag — if possible, reframe and explain the reasoning so that the model understands why the thing you're asking for is important. That's a more humane, powerful, and effective approach.

4. Look for repeated work — in the eval transcripts AND in whatever conversation the skill was distilled from. Read the transcripts from the test runs and notice if the subagents all independently wrote similar helper scripts or took the same multi-step approach to something. If all 3 test cases resulted in the subagent writing a create_docx.py or a build_chart.py, that's a strong signal the skill should bundle that script. Write it once, put it in scripts/, and tell the skill to use it. This saves every future invocation from reinventing the wheel. The same signal hides in a source conversation you distilled a skill from — code that session wrote even once is code every future run must rewrite; don't wait for eval runs to prove the repetition (skills whose eval loop is skipped never get that proof — the Scripts check in Step 4 of the creation process is the catch-point for those).

5. Fold corrections back verbatim — same session, both levels. For taste-calibrated skills the user's corrections ARE the eval set, but only if they land where a future run will read them. Two properties make the write-back converge: (a) record the correction verbatim, not paraphrased — exact words carry calibration signal a summary flattens ("绝对禁止这种东西" teaches a hard boundary; "user prefers fewer jumps" does not). Quote the words inside the rule they correct, with a date. (b) Write back in the same session the correction happens, at both levels — fix the artifact AND the skill's rule/reference/component; a correction that only fixes the artifact is invisible to every future run, and one deferred to "later" usually never lands. While writing it back, check whether the skill's own text taught the anti-pattern just banned — a correction often falsifies an existing instruction, and leaving the old advice standing guarantees recurrence (real case: a decision-page contract advised jump-style "goto anchors to the referenced figure"; the user banned exactly that, so the write-back had to amend the old sentence, not just add a new rule beside it). When the corrected thing is an interaction fragment, the write-back's final step is shelf promotion — see the Component-shelf check in Step 4.

This task is pretty important (we are trying to create billions a year in economic value here!) and your thinking time is not the blocker; take your time and really mull things over. I'd suggest writing a draft revision and then looking at it anew and making improvements. Really do your best to get into the head of the user and understand what they want and need.

After analyzing feedback, present your improvement plan via AskUserQuestion:

I've read the feedback from [N] test cases. [X] had specific complaints, [Y] looked good.

Key issues:
- [Issue 1]: [plain-language summary]
- [Issue 2]: [plain-language summary]

RECOMMENDATION: [strategy] because [reason]

Options:
A) Iterative refinement — targeted fixes for the specific issues above (Recommended)
B) Structural redesign — the core approach needs rethinking
C) Bundle a script — I noticed all test runs independently wrote similar code for [X]
D) Expand test set first — add [N] more test cases to avoid overfitting to these examples

The iteration loop

After improving the skill:

1. Apply your improvements to the skill
2. Rerun all test cases into a new iteration-<N+1>/ directory, including baseline runs. If you're creating a new skill, the baseline is always without_skill (no skill). If you're improving an existing skill, the immutable original pre-edit version remains the preservation baseline for every iteration. You may additionally compare against the immediately previous iteration to measure incremental quality, but never replace the original baseline with it.
3. Launch the reviewer with --previous-workspace pointing at the previous iteration
4. Wait for the user to review and tell you they're done
5. Read the new feedback, improve again, repeat

At the end of each iteration, use AskUserQuestion as a checkpoint:

Iteration [N] complete. Results: [pass_rate]% assertions passing, [delta vs previous].

RECOMMENDATION: [Continue / Accept / Revert] because [one-line reason from the delta and remaining feedback].

Options:
A) Continue iterating — I see more room for improvement
B) Accept this version — it's good enough, let's move to packaging
C) Revert to previous iteration — this round made things worse
D) Run blind comparison — rigorously compare this version vs the previous one

Keep going until:

• The user says they're happy
• The feedback is all empty (everything looks good)
• You're not making meaningful progress

Advanced: Blind comparison

For situations where you want a more rigorous comparison between two versions of a skill (e.g., the user asks "is the new version actually better?"), there's a blind comparison system. Read agents/comparator.md and agents/analyzer.md for the details. The basic idea is: give two outputs to an independent agent without telling it which is which, and let it judge quality. Then analyze why the winner won.

This is optional, requires subagents, and most users won't need it. The human review loop is usually sufficient.

Description Optimization

The description field in SKILL.md frontmatter is the primary mechanism that determines whether Claude invokes a skill. After creating or improving a skill, offer to optimize the description for better triggering accuracy.

Step 1: Generate trigger eval queries

Create 20 eval queries — a mix of should-trigger and should-not-trigger. Save as JSON:

[
  {"query": "the user prompt", "should_trigger": true},
  {"query": "another prompt", "should_trigger": false}
]

The queries must be realistic and something a Claude Code or Claude.ai user would actually type. Not abstract requests, but requests that are concrete and specific and have a good amount of detail. For instance, file paths, personal context about the user's job or situation, column names and values, company names, URLs. A little bit of backstory. Some might be in lowercase or contain abbreviations or typos or casual speech. Use a mix of different lengths, and focus on edge cases rather than making them clear-cut (the user will get a chance to sign off on them).

Bad: "Format this data", "Extract text from PDF", "Create a chart"

Good: "ok so my boss just sent me this xlsx file (its in my downloads, called something like 'Q4 sales final FINAL v2.xlsx') and she wants me to add a column that shows the profit margin as a percentage. The revenue is in column C and costs are in column D i think"

For the should-trigger queries (8-10), think about coverage. You want different phrasings of the same intent — some formal, some casual. Include cases where the user doesn't explicitly name the skill or file type but clearly needs it. Throw in some uncommon use cases and cases where this skill competes with another but should win.

For the should-not-trigger queries (8-10), the most valuable ones are the near-misses — queries that share keywords or concepts with the skill but actually need something different. Think adjacent domains, ambiguous phrasing where a naive keyword match would trigger but shouldn't, and cases where the query touches on something the skill does but in a context where another tool is more appropriate.

The key thing to avoid: don't make should-not-trigger queries obviously irrelevant. "Write a fibonacci function" as a negative test for a PDF skill is too easy — it doesn't test anything. The negative cases should be genuinely tricky.

Step 2: Review with user

Present the eval set to the user for review using the HTML template:

1. Read the template from assets/eval_review.html
2. Replace the placeholders:
   • __EVAL_DATA_PLACEHOLDER__ → the JSON array of eval items (no quotes around it — it's a JS variable assignment)
   • __SKILL_NAME_PLACEHOLDER__ → the skill's name
   • __SKILL_DESCRIPTION_PLACEHOLDER__ → the skill's current description
3. Write to a temp file (e.g., /tmp/eval_review_<skill-name>.html) and open it: open /tmp/eval_review_<skill-name>.html
4. The user can edit queries, toggle should-trigger, add/remove entries, then click "Export Eval Set"
5. The file downloads to ~/Downloads/eval_set.json — check the Downloads folder for the most recent version in case there are multiple (e.g., eval_set (1).json)

This step matters — bad eval queries lead to bad descriptions.

Step 3: Run the optimization loop

Tell the user: "This will take some time — I'll run the optimization loop in the background and check on it periodically."

Save the eval set to the workspace, then run in the background:

python -m scripts.run_loop \
  --eval-set <path-to-trigger-eval.json> \
  --skill-path <path-to-skill> \
  --model <model-id-powering-this-session> \
  --max-iterations 5 \
  --verbose

Use the model ID from your system prompt (the one powering the current session) so the triggering test matches what the user actually experiences.

While it runs, periodically tail the output to give the user updates on which iteration it's on and what the scores look like.

This handles the full optimization loop automatically. It splits the eval set into 60% train and 40% held-out test, evaluates the current description (running each query 3 times to get a reliable trigger rate), then calls Claude to propose improvements based on what failed. It re-evaluates each new description on both train and test, iterating up to 5 times. When it's done, it opens an HTML report in the browser showing the results per iteration and returns JSON with best_description — selected by test score rather than train score to avoid overfitting.

The loop self-aborts after iteration 1 if it detects total silence — you don't have to catch this by hand. This is a code-level guard in run_loop.py, not just advice, and it distinguishes two causes of "iteration 1 came back with zero triggers on every should-trigger query":

• exit_reason: "infra_error: ..." — some query executions (should-trigger or not-should-trigger) raised exceptions (claude CLI not on PATH, timeout too short, network down) rather than cleanly returning "no trigger." Checked first, and deliberately fires on any nonzero error count, even a single flaky run out of dozens — a crash, however rare, is a more specific and actionable lead than "nothing fired," and the fix is environmental, not a description rewrite. The ratio in the message (e.g. 3/15) is the read on severity: a large fraction means "stop, fix your environment"; a small fraction alongside a lot of clean 0-trigger runs means the environment probably has a minor flake worth checking and the description may genuinely be bad — check both. Check stderr for Warning: query failed lines either way.
• exit_reason: "degenerate_harness: ..." — checked only once the branch above rules out any execution errors, so this means every should-trigger and not-should-trigger query actually ran and still fired zero times (this is what "precision=100%" via a zero-denominator default actually means: not "perfect," but "nothing to divide"). The probe genuinely measures nothing — go diagnose competitor collision or a low-threshold domain (below) before touching the description at all.

Both checks run only against iteration 1, and only when zero should-trigger queries fired — a weak-but-nonzero iteration 1 (say 1/9 triggers) is a different, potentially-recoverable case and is deliberately left to iterate. degenerate_harness additionally requires not-should-trigger queries to have fired zero times too: if the description is merely polarity-inverted — missing every positive while accidentally matching several negatives — the probe is proven to work and improve_description has real gradient to act on, so the guard does not abort that case (only a genuinely dead probe does). And it requires zero execution errors on either side — a not-should-trigger query that raised an exception never actually ran, so its "0 triggers" can't be used as evidence the probe is dead; that's exactly what routes to infra_error instead. Either way it breaks immediately and skips improve_description entirely — the JSON and HTML still report best_description/best_score normally (nothing is suppressed), the exit reason is the signal that this particular run's numbers shouldn't be read as "this is a good description." (Real case, 2026-08: a full 5-iteration loop was launched with no pre-check; two complete iterations — each a full eval batch, minutes of wall time and dozens of claude -p subprocess calls — came back precision=100%/recall=0%/identical scores before anyone looked at the numbers, and every should-trigger and not-should-trigger query alike had fired zero times. The failure was diagnosable from iteration 1 alone; nothing in iterations 2+ added information.) The HTML report also renders a banner when either exit reason fires, so the browser view carries the same signal as the JSON/stderr, not just the two lower-visibility channels. (An earlier draft of this guard checked should-trigger triggers alone; two rounds of independent review — a fresh agent each round, the second checking the first round's fixes rather than re-covering old ground — constructed the polarity-inverted counter-example, the infra-vs-description ambiguity, the negative-side-errors gap, and a pre-existing --holdout 0 report crash it surfaced along the way, all fixed before shipping.)

One caveat this guard can't cover: with --runs-per-query 1 (the default is 3), a single should-trigger query with a genuinely-50%-ish trigger rate has a real chance of reading 0/1 by chance alone and tripping the guard on noise, not on a dead probe. The default runs_per_query=3 is what makes "zero across every repeat of every positive query" a strong signal — if you override it down, this guard's false-positive risk goes up with it.

Sanity-check the harness before you trust best_description — it can still return hollow output even when neither guard above fired. Both checks only catch a total failure at iteration 1; a harness can also limp along with a persistently weak-but-nonzero signal across all 5 iterations without ever tripping either one. If every iteration reports recall staying near 0% with identical or near-identical scores past iteration 1, that's the same underlying problem the guards couldn't rule out — the "winning" description is usually just noise around iteration 1 unchanged. Before applying any best_description, run one obviously-should-trigger query yourself and confirm recall > 0. A recall-flat result across iterations means the harness is a hidden variable (commonly: the skill is losing the trigger to installed competitors — see Coexistence below) — do NOT apply its "best"; hand-author the description and verify it with real probes instead. (methodology Cases 12, 14) There is a second recall-0 root cause, distinct from competitor loss: the skill's task domain is low-threshold — the model does the job itself and never consults any skill — spelled out at the end of How skill triggering works below. Distinguish the two before reacting, because the fixes are opposite: a competitor is fixed by precedence/supersede, a low-threshold domain by abandoning the trigger probe entirely for production-as-eval.

When you probe triggering yourself with claude -p, collect ALL skill calls in the run, not just the first — and remember tool-invocation is a proxy. A UserPromptSubmit/SessionStart hook can inject an unrelated skill before the model chooses, so an "exit on the first Skill call" probe will misreport a false "didn't trigger." And "did it call the Skill tool" is not the same as "did the skill's content shape the output" (the model may read the skill without a visible Skill call) nor "is the output correct." A buggy probe is itself a hidden variable that keeps you optimizing against a wrong conclusion.

How skill triggering works

Understanding the triggering mechanism helps design better eval queries. Skills appear in Claude's available_skills list with their name + description, and Claude decides whether to consult a skill based on that description. The important thing to know is that Claude only consults skills for tasks it can't easily handle on its own — simple, one-step queries like "read this PDF" may not trigger a skill even if the description matches perfectly, because Claude can handle them directly with basic tools. Complex, multi-step, or specialized queries reliably trigger skills when the description matches — with one systematic exception (a low-threshold task domain) spelled out at the end of this section.

This means your eval queries should be substantive enough that Claude would actually benefit from consulting a skill. Simple queries like "read file X" are poor test cases — they won't trigger skills regardless of description quality.

But some skills fail this test for a reason no query rewrite can fix — the skill's whole job is a low-threshold task. The paragraph above blames the query ("write a more substantial one"); this is the orthogonal case, where the skill's domain is what blocks triggering. If a skill exists to do something Claude will just do itself with a basic tool — convert audio to 16 kHz, downsample, extract one field, bulk-rename — then even a fully-specified query ("downsample this 48k stereo wav to 16k mono for whisper," path and all) won't trigger it: the model reaches straight for Bash/ffmpeg and never consults any skill. Query substance can't fix this, because the "I can just do this" judgment is about the task, not the wording.

Diagnose it before you burn an optimization loop: run one realistic query through claude -p and watch whether the model ever consults the skill across the whole run (per the ALL-skill-calls rule above), or goes straight to Bash and does the job itself. If it never consults, the trigger probe is structurally blind to this skill — recall reads ~0 no matter what you write, and that zero measures the model's do-it-myself reflex, not your description. Stop tuning against claude -p recall and verify the real way: production-as-eval (the user's next real, interactive use — headless -p is more action-biased than a real session and systematically under-reports triggering), plus output comparison where the skill produces a checkable artifact. (Observed on an audio-preprocessing skill: a headless run_eval sweep read recall 0 on every query — including an obvious "transcribe this recording" one — and a manual claude -p probe showed the model going straight to Bash to find and process the file, Skill=0 across the run. The zero measured headless action-bias, not the description; the real signal only shows up in interactive use.)

Step 4: Apply the result

Take best_description from the JSON output and update the skill's SKILL.md frontmatter. Show the user before/after and report the scores.

CRITICAL: Edit Skills at Source Location

NEVER edit installed skill copies first. Treat all of these as installed/runtime copies unless the user explicitly says they are the source:

• ~/.codex/skills/<skill-name>
• ~/.claude/skills/<skill-name>
• ~/.agents/skills/<skill-name>
• ~/.claude/plugins/cache/...
• ~/.codex/plugins/cache/...

Editing installed copies first causes changes to be:

• Lost when cache refreshes
• Not synced to source control
• Wasted effort requiring manual re-merge

ALWAYS verify you're editing the source repository:

# WRONG - cache location (read-only copy)
~/.claude/plugins/cache/daymade-skills/my-skill/1.0.0/my-skill/SKILL.md

# WRONG - personal installed copy unless explicitly used as source
~/.codex/skills/my-skill/SKILL.md

# RIGHT - source repository
<repo-root>/my-skill/SKILL.md

Before any edit, run a source-location check and say which path is source:

pwd
git rev-parse --show-toplevel
rg -n '"name": "<skill-or-suite-name>"' .claude-plugin/marketplace.json
find . -path '*/SKILL.md' -maxdepth 4 | rg '(^|/)<skill-name>/SKILL.md$'

If the available-skills list points at ~/.codex/skills, ~/.claude/skills, or a plugin cache, do not assume that path is source. Locate the repository-backed source first, edit it, validate it, and only then sync the installed copy when the user needs immediate local runtime use.

Concurrent sessions on the same skill repo

Power users run several Claude sessions at once, and skill repos are exactly where they collide: while you edit skill A, a sibling session may commit skill B (or even an earlier round of skill A) under you. One real session hit all three symptoms inside an hour — a Write rejected because the file changed after reading, and HEAD moving twice mid-task (methodology Case 16). The failure isn't the collision; it's a stale baseline or a clobbering write that silently mixes two sessions' work. Standing rules:

1. Baseline from a git ref, not from the working tree, whenever the repo is clean at task start: git archive <HEAD-sha> <skill-dir> | tar -x -C <workspace>/skill-before and pass --baseline-origin git-ref:<sha> to the audit. A tree snapshot taken minutes before someone else's commit is a baseline for a tree that no longer exists.
2. Re-read before write when a write is rejected or any time has passed: diff what changed (git log --oneline -3, git show <new-commit> --stat), fold the other session's intent into your version — their edit usually has a reason — and only then write.
3. Check HEAD before committing (git log --oneline -1): if it moved since your baseline, re-run the regression compare against the new ref before verify — the audit tool will reject a stale review anyway ("after skill changed"), so catching it yourself saves a round.
4. Stage only your own paths (git add <skill-dir> <registry-file>), never git add . — the sibling session's uncommitted work must not ride along. (Already the rule for packaging; doubly load-bearing under concurrency.)
5. One version bump per session outcome, not per editing round: consecutive same-session rounds on one skill collapse into a single bump — unless an intermediate state was already consumed (committed + pulled by the user or another session), which makes each consumed state its own version.

Skill Creation Process (Step-by-Step)

When creating or updating a skill, follow these steps in order. Skip steps only when clearly not applicable.

Step 0: Prerequisites Check

Before starting any skill work, auto-detect all dependencies and proactively install anything missing. Discovering a missing tool mid-workflow (e.g., gitleaks at packaging time, PyYAML at validation) wastes time and breaks flow.

Run the quick check from references/prerequisites.md, auto-install what you can, and present the user a summary checklist. Only proceed when all blocking dependencies are satisfied.

Key blockers: Python 3, uv, PyYAML (validation/packaging), gitleaks (security scan), claude CLI (evals). Run Python tools with explicit uv dependency declarations, for example uv run --with PyYAML python -m scripts.quick_validate <skill-path> from the skill-creator root directory. Bare python3 depends on ambient site packages and can miss PyYAML.

Step 1: Understanding the Skill with Concrete Examples

Skip this step only when the skill's usage patterns are already clearly understood.

To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.

For example, when building an image-editor skill, relevant questions include:

• "What functionality should the image-editor skill support? Editing, rotating, anything else?"
• "Can you give some examples of how this skill would be used?"
• "What would a user say that should trigger this skill?"

To avoid overwhelming users, avoid asking too many questions in a single message.

Step 2: Planning the Reusable Skill Contents

Analyze each example by:

1. Considering how to execute on the example from scratch
2. Determining the appropriate level of freedom for Claude
3. Identifying what scripts, references, and assets would be helpful when executing these workflows repeatedly

Match specificity to task risk:

• High freedom (text instructions): Multiple valid approaches exist
• Medium freedom (pseudocode with parameters): Preferred patterns exist with acceptable variation
• Low freedom (exact scripts): Operations are fragile, consistency critical

Step 3: Initializing the Skill

Skip this step if the skill already exists.

When creating a new skill from scratch, always run the init_skill.py script:

scripts/init_skill.py <skill-name> --path <output-directory>

The script creates a template skill directory with proper frontmatter, resource directories, and example files.

Step 4: Edit the Skill

Before writing, retrieve Anthropic's best-practices doc (linked in "Skill Creation Best Practice" above) and the methodology reference — do this even when you feel you already know them: the doc updates, training-data versions go stale, and "I basically know it" is exactly the state in which editors skip it and miss the newest guidance.

When editing, remember that the skill is being created for another instance of Claude to use. Focus on information that would be beneficial and non-obvious to Claude.

Existing-skill migration gate — required before the first edit:

1. Capture the complete current bundle before editing. For a non-Git or dirty source, use the tool so the snapshot carries a verifiable provenance manifest:

   cd <skill-creator-path>
   uv run --with PyYAML python -m scripts.audit_skill_regression snapshot \
     --source <path/to/skill-folder> \
     --output <workspace>/skill-before
   

   For a clean Git-tracked source, materialize the directory from the chosen ref. Include SKILL.md, references, scripts, assets, workflows, and existing evals—not just the main prompt. Never copy the already-edited tree and label it "before".

2. Inventory the old skill's actor/jobs, trigger contexts, runtime contracts, commands/flags, failure and recovery cases, page/domain variants, bundled resources, and eval coverage. Add preservation cases for important old edge behavior before a structural rewrite.

3. After editing, generate an old-vs-new review:

   cd <skill-creator-path>
   uv run --with PyYAML python -m scripts.audit_skill_regression compare \
     --before <workspace>/skill-before \
     --after <path/to/skill-folder> \
     --output <workspace>/skill-regression-review.json \
     --baseline-origin pre-edit-snapshot
   

   If the old directory was reconstructed from Git, replace the final flag with --baseline-origin git-ref:<ref>. The tool resolves the ref to an immutable commit and verifies every included file and executable bit against that tree.

4. Review every candidate. Use exactly one disposition and record concrete evidence/reason: preserved_or_moved, intentional_sanitization, intentional_boundary, removed_by_explicit_user_request, not_reusable, or true_gap_fixed. Runtime capabilities cannot use not_reusable; moving a runtime capability outside this skill requires the owning destination, current boundary evidence, and traceable user approval. Explicit retirement must also quote/trace the user's approval. Preserved/sanitized/fixed claims must point to a real current file and line and include a short contains quote that the verifier can locate nearby. File-level candidates use the current file fingerprint plus a named semantic review explaining why behavior survived—the fingerprint alone proves file identity, not capability preservation.

   Don't hand-edit the review JSON or rewrite the same filler script each round — the classify subcommand does the mechanical part (locates the quote's line in the destination file, fills evidence/semantic_review, fail-fasts on a missing quote or a too-short reason). You still author every disposition and reason; it only types them in:

   uv run --with PyYAML python -m scripts.audit_skill_regression classify \
     --review <workspace>/skill-regression-review.json \
     --after <path/to/skill-folder> \
     --map <workspace>/dispositions.json \
     --reviewer "<who-reviewed>"
   

   where dispositions.json maps candidate index (or id) to {"destination": "<rel-file>", "needle": "<verbatim current quote>", "reason": "<why this counts as preserved/sanitized/…>", "disposition": "preserved_or_moved"} (disposition defaults to preserved_or_moved; file-level candidates need only destination + a 40+ char reason — the fingerprint is computed for you).

5. Verify the completed review. Hashes make the review stale after any further edit, so regenerate and reclassify when the candidate changes. A passing verification writes .skill-regression-reviewed, a content-bound local status receipt. It helps detect later edits, but it is deliberately not standalone packaging authority; packaging re-verifies the completed review itself:

   uv run --with PyYAML python -m scripts.audit_skill_regression verify \
     --before <workspace>/skill-before \
     --after <path/to/skill-folder> \
     --review <workspace>/skill-regression-review.json
   

What success looks like at each gate step (real output, so silent failure is recognizable):

$ … compare …
Regression audit: 8 candidate(s), 371 exact preservation(s)   # exit 1 = candidates to review
$ … classify …
Classified 8 candidate(s).                                    # exit 1 = some still unclassified
$ … verify …
Skill regression review passed.
Regression attestation created: .skill-regression-reviewed    # exit 0 = gate cleared

compare returning 1 means review candidates exist, not that the tool failed; 2 means invocation/runtime failure. The tool proves exact movement and interface preservation, but deliberately refuses to infer semantic equivalence from fuzzy word overlap. A generic phrase such as “check permission denied” cannot silently replace a precise signed-in-without-role contract. Also inspect candidates marked only_outside_runtime: runtime behavior present only in evals, tests, or an unreachable reference is absent from the normal invocation path.

Validate immediately after every SKILL.md edit — don't wait for packaging (Step 7). The failure this catches early is real: a frontmatter description written as an unquoted YAML scalar parses fine in Claude Code's lenient parser but breaks in strict parsers (codex reported invalid YAML: mapping values are not allowed on a skill that had been shipping for months), and a # inside an unquoted description doesn't even error — it silently truncates everything after it, so the trigger keywords vanish while every scan stays green.

cd <skill-creator-path>
uv run --with PyYAML python -m scripts.quick_validate <path/to/skill-folder>

Write the description as a YAML block scalar (description: >- followed by an indented paragraph) whenever it contains : or # or spans multiple sentences — block scalars tolerate both characters natively — the recommended convention for every new or edited description since the incident above.

When updating an existing skill: Scan every existing reference and bundled resource for corresponding updates, then pass the migration gate above. Moving a contract requires a direct runtime pointer from SKILL.md; an eval or changelog is not a replacement.

Scripts check: Before calling the edit done, ask: what code did the source conversation (or the eval transcripts) write — that every future invocation would otherwise rewrite? Bundle it into scripts/ (parameterized, sanitized) and change the docs to point at it. The division of labor: scripts carry the execution, docs carry the understanding — a skill whose method lives only in prose re-pays the full authoring cost on every run. This check exists here, in the edit step, precisely because paths that skip the eval loop (conversation distillation, direct edits) never reach the eval-transcript version of this check in "Improving the skill".

Component-shelf check (the same question, asked of artifact-EMBEDDED fragments): the Scripts check covers code the skill executes; artifact-generating skills (report pages, decks, documents) also accumulate fragments their outputs embed — an image-overlay widget, a sticky nav, a chart config, a CSS block. If successive outputs each hand-write a similar fragment, that is the same repetition signal pointing at a different shelf: a dedicated components subfolder under the skill's assets/ directory, plus a registry reference. What turns a snippet into a shelf component is the contract around it: (a) verbatim-embed block — BEGIN/END markers copied whole into the artifact, zero dependencies, so single-file artifacts stay self-contained; (b) registry entry stating the interaction contract (triggers, key bindings, close behavior, edge cases) and provenance — which delivered artifact it came from, when the user approved it; (c) admission gate — only fragments the user has actually used and approved enter (the approved-corpus discipline applied to interactions; unapproved-but-pretty stays out); (d) behavior frozen, skin adjustable — calibrated behavior and key bindings are the contract and must not drift between runs, while colors and sizing may follow the artifact's register; (e) write-back loop — a fragment invented for one artifact is promoted to the shelf in the same session its approval lands. The reason this matters beyond token savings: hand-rewritten fragments drift — each rewrite subtly changes key bindings, close behavior, counters — and for a user who reads these artifacts daily, interaction consistency IS the product experience. In the user's founding words for the first such shelf: 交互形式要沉淀下来、可复用、有复利，代表一致的交互喜好 (2026-07). Real instance (project fingerprints removed): a report-page skill's lightbox-gallery component (image overlay: ←/→ cycles the group, ESC closes, in-place zoom, n/N counter) with its interaction-components registry reference — extracted the same session the user corrected jump-style references, and verified with an automated click-through test before shipping. See methodology Case 17.

Pipeline check: Consider whether this skill's output naturally feeds into another skill. If so, add a "Next Step" handoff section (see "Pipeline Handoff" in the Skill Writing Guide). Also check if any existing skill should chain into this one.

Step 5: Sanitization Review (mandatory for any public skill)

Before this gate: discipline #5's independent pass must have run, and independent-review.md (in your private git-tracked knowledge repo — not the wiped workspace, not any repo that is or may become public or distributed) must exist. For a new skill this is the first step where anything leaves your hands, so it is where that discipline is actually enforced rather than merely stated — a rule that lives 1000 lines above the point of use, with nothing checking it, loses to completion-drive every time.

For an edit to an already-published skill, the moment it leaves your hands is the push, not this step. This gate can be answered "internal-only, skip" in one sentence, and packaging may never run at all for a docs-only change — so anchoring the review here means a skill edit can reach a public branch having passed no independent eye. Real case (2026-07): a change to this very file shipped a discovery command that could not reach three of the roots the prose beside it claimed it covered; it was merged, and only a review commissioned afterwards caught it. So for an existing skill: the independent pass and its recorded artifact are due before git push / opening the PR — the gates that run at push (regression audit, validation, scans) all check structure, and none of them can tell you the instructions are wrong. This inherits discipline #5's threshold unchanged: it is due when a rule, contract, or number changed, not for a typo or a pure reformat. Making a spelling fix wait on a review round would teach exactly the reflex #6 warns about — "this one's small" becomes the universal exemption, and then it covers the changes that mattered too.

Not optional for a skill going to a public repo. Private content leaks into public skills all the time, and the leaks a scanner misses are the dangerous ones — a real name in a non-English language, a verbatim line from a real transcript, a real example dropped into an illustration. Skip only if the skill is genuinely internal-only.

Scope the pass by destination, not by topic. Only the artifact that ships publicly — the skill bundle itself — gets sanitized. Companion documents that stay in a private repo (the incident report the skill was distilled from, internal runbooks, the project's CLAUDE.md) keep their real hostnames, paths, and timestamps: redacting those destroys their audit value, and you will end up reverting it. One distillation session went through three rounds of rework precisely because the redaction pass was applied to everything the source material touched instead of just the public skill.

Check the destination first, and let it pre-fill the recommendation. Run gh repo view --json isPrivate on the repo the skill will live in (or read the note quick_validate already printed). A private destination makes option C the default recommendation rather than an afterthought — "assume public unless told otherwise" is what turns a private skill's working paths into placeholders nobody asked for.

The trigger for sanitizing is the destination's isPrivate, not how much the task feels like publishing. These come apart, and when they do the feeling wins unless you name it. Moving content into a marketplace skill feels like publishing — marketplaces are where things get distributed — so the sanitizing reflex fires even when both the source and the destination are private repos and nothing is going anywhere. Real case (2026-07): a migration from a project-level skill into a private marketplace skill generalized a real config path, a real hex value and a real named example into placeholders. quick_validate had printed 🔒 audience: private on every run; the author read it every time and sanitized anyway, because the activity read as publication. The cost is not cosmetic — a real path is what lets the next reader go check the value; "the project's own token file" cannot be opened.

So make it a lookup, not a judgment: read isPrivate for the destination, and if it is true, sanitizing requires the owner to ask for it. A private→private move is a move, not a release. When neither probe answers (no remote yet, quick_validate not run), you have no lookup — fall back to treating it as public and say so, because the asymmetry runs that way: an unnecessary question costs a sentence, an unnecessary leak does not come back. And isPrivate reports the destination's state today — a skill sitting in a private repo on its way to release gets its sanitization pass on the push that publishes it, not on this one. If you find yourself reasoning about whether something "should" be generalized in a private destination, that reasoning is the tell.

Use AskUserQuestion to confirm the depth (for a public destination, confirm the depth, not whether to do it; for a private one, confirm whether it is wanted at all):

This skill will be public. I'll do a sanitization pass — the core of it is
me reading the whole skill and judging each name/example/snippet, because
scanners miss real content that has no keyword to match.

Options:
A) Full — I replace everything that looks lifted from a real project/person
B) Selective — I show you each finding and you decide (Recommended)
C) This skill is genuinely internal-only — skip

Sanitization process — the read-through is the method, the scan is a helper:

1. Read the entire skill yourself and judge semantically (this is the real check): SKILL.md + every reference + every example. For each concrete noun / example / snippet ask "generic-placeholder-or-public-entity, or lifted-from-a-real-project/person/transcript?" Replace the latter — even if no scanner flagged it. This is the only thing that catches no-keyword leaks. Full guidance + the semantic question in references/sanitization_checklist.md.
2. Run scanners as a cheap first pass: the checklist's grep patterns + security_scan.py (Step 6). They catch obvious secrets / paths / known names fast — but "no matches" is not a pass.
3. Replace each finding with a generic equivalent that keeps the teaching point (real name → public figure or <placeholder>, real snippet → <placeholder>). Two rules learned the hard way: the placeholder itself must not encode the real value — <acme-corp-domain> leaks exactly the name it was supposed to hide; name the role instead (<api-domain>, <upstream-provider>). And when you script a bulk replace, give it an explicit file whitelist scoped to the skill directory — an unscoped find-and-replace will happily rewrite the project's own CLAUDE.md and force a git restore.
4. Verify by re-reading, not by re-grepping: re-read the changed sections and confirm no broken references.

Step 6: Security Review

Before packaging or distributing a skill, run the security scanner to detect hardcoded secrets and personal information:

# Required before packaging
uv run --with PyYAML python -m scripts.security_scan <path/to/skill-folder>

# Verbose mode includes additional checks for paths, emails, and code patterns
uv run --with PyYAML python -m scripts.security_scan <path/to/skill-folder> --verbose

Detection coverage:

• Hardcoded secrets (API keys, passwords, tokens) via gitleaks
• Personal information (usernames, emails, company names) in verbose mode
• Unsafe code patterns (command injection risks) in verbose mode

What it does NOT cover — why Step 5's read-through is still required: gitleaks and the regex rules only match known secret formats and patterns you listed. They are structurally blind to private content with no keyword — a real person/project name in a non-English language, a verbatim line from a real transcript, a real example lifted from your own work. A green security_scan means "no known-format secret was found", not "the skill is sanitized". Never treat it as the latter.

First-time setup: Install gitleaks if not present:

# macOS
brew install gitleaks

# Linux/Windows - see script output for installation instructions

Exit codes:

• 0 - Clean (safe to package)
• 1 - High severity issues
• 2 - Critical issues (MUST fix before distribution)
• 3 - gitleaks not installed
• 4 - Scan error

In a private skill, a finding is information, not a work order. The scanner cannot tell a leaked credential from a credential that is supposed to be there — a template whose whole value is that it comes pre-filled, a script pointing at the one machine it runs on. Never auto-fix in a private repo: report what was found and let the owner choose. Option A below ("fix automatically") is for skills headed somewhere public.

If issues are found, present them via AskUserQuestion:

Security scan found [N] issues in "[skill-name]":
- [SEVERITY] [file]: [description]
- ...

RECOMMENDATION: Fix automatically — these look like [accidental leaks / false positives].

Options:
A) Fix all issues automatically (Recommended)
B) Review each finding — let me decide per-item (some may be intentional)
C) Override and proceed — I accept the risk for internal distribution

Step 7: Packaging a Skill

Once the skill is ready, package it into a distributable file:

cd <skill-creator-path>
uv run --with PyYAML python -m scripts.package_skill <path/to/skill-folder>

For every existing Git-tracked skill, packaging is blocked until the completed review is supplied and re-verified. A current marker is only a local status receipt, so committing first or hand-writing a marker cannot bypass the review. The review becomes stale on the next edit:

uv run --with PyYAML python -m scripts.package_skill \
  <path/to/skill-folder> \
  --regression-review <workspace>/skill-regression-review.json

Optional output directory, and --include-evals to ship the root evals/ directory (excluded by default as a development asset):

cd <skill-creator-path>
uv run --with PyYAML python -m scripts.package_skill <path/to/skill-folder> ./dist --include-evals

The packaging script will:

1. Validate the skill automatically (YAML frontmatter, naming conventions, path reference integrity)
2. Re-verify the completed existing-skill regression review whenever Git HEAD already contains the skill; a marker alone never authorizes packaging
3. Verify security scan (content hash must match last scan)
4. Package the skill into a distributable archive

If validation fails, the script reports errors and exits without creating a package.

Step 8: Update Marketplace

After packaging, update the marketplace registry to include the new or updated skill.

For new skills, add an entry to .claude-plugin/marketplace.json:

{
  "name": "skill-name",
  "description": "Copy from SKILL.md frontmatter description",
  "source": "./skill-name",
  "strict": false,
  "version": "1.0.0",
  "category": "developer-tools",
  "keywords": ["relevant", "keywords"]
}

For updated skills, bump the version in plugins[].version following semver. Any change to a skill's files — even a one-line typo fix — needs a bump: without it, marketplace update sees no new version, so already-installed copies never refresh and users keep running the old skill while your fix sits unshipped.

Plugin boundaries are not this skill's domain. Whether to split skills into separate plugins, how to lay out source/skills, and whether users can toggle skills individually all belong to the packaging/distribution domain — the SSOT is the marketplace-dev skill, not here. When a task actually needs those decisions: ensure marketplace-dev is available (auto-install it if missing — the same way skill-reviewer pulls in skill-creator when it needs its scripts), then read marketplace-dev's cache-and-source-patterns reference and follow it. Don't restate its rules here; a copy would drift.

Renaming, relocating, or removing a marketplace entry is a breaking change for every user who already installed it — Claude Code does not clean up installed copies when an entry disappears, leaving dangling installs that error on every marketplace update. Treat such changes like an API deprecation: ship a migration note in the changelog, and follow marketplace-dev's guidance for the mechanics.

If you commit/push the skill repo yourself — for an existing skill, confirm independent-review.md exists before you push (Step 5's "before this gate" note explains why the push, not Step 5, is the real moment for an edit to something already published; a docs-only change may never reach Step 5 or packaging at all). Then: stage only the skill's explicit paths (git add <skill-dir> .claude-plugin/marketplace.json) — never git add .; the working tree is usually full of unrelated churn that will otherwise ride into the commit (one commit swept in a pile of unrelated transcript files and had to be git reset and re-staged). Before pushing, confirm the repo's real visibility with gh repo view --json visibility,isPrivate instead of assuming from the path — a public skill repo deserves a PR + review, not a direct push to main.

Step 9: Ship or Iterate

After completing the skill, use AskUserQuestion to determine next steps:

Skill "[name]" is complete. Security scan passed, marketplace updated.

RECOMMENDATION: [pick based on state — e.g. "B) optimize the description" if triggering was never verified, else "D) done for now"] because [one-line reason].

Options:
A) Package and export as .skill file for distribution
B) Run description optimization — improve auto-triggering accuracy (~5 min)
C) Expand test set and iterate more — add edge cases before shipping
D) Done for now — I'll test it manually and come back if needed

After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.

Refinement filter: Only add what solves observed problems. If best practices already cover it, don't duplicate.

Package and Present (only if present_files tool is available)

Check whether you have access to the present_files tool. If you don't, skip this step. If you do, package the skill and present the .skill file to the user:

uv run --with PyYAML python -m scripts.package_skill <path/to/skill-folder>

After packaging, direct the user to the resulting .skill file path so they can install it.

Claude.ai-specific instructions

In Claude.ai, the core workflow is the same (draft -> test -> review -> improve -> repeat), but because Claude.ai doesn't have subagents, some mechanics change. Here's what to adapt:

Running test cases: No subagents means no parallel execution. For each test case, read the skill's SKILL.md, then follow its instructions to accomplish the test prompt yourself. Do them one at a time. This is less rigorous than independent subagents (you wrote the skill and you're also running it, so you have full context), but it's a useful sanity check — and the human review step compensates. Skip the baseline runs — just use the skill to complete the task as requested.

Reviewing results: If you can't open a browser (e.g., Claude.ai's VM has no display, or you're on a remote server), skip the browser reviewer entirely. Instead, present results directly in the conversation. For each test case, show the prompt and the output. If the output is a file the user needs to see (like a .docx or .xlsx), save it to the filesystem and tell them where it is so they can download and inspect it. Ask for feedback inline: "How does this look? Anything you'd change?"

Benchmarking: Skip the quantitative benchmarking — it relies on baseline comparisons which aren't meaningful without subagents. Focus on qualitative feedback from the user.

The iteration loop: Same as before — improve the skill, rerun the test cases, ask for feedback — just without the browser reviewer in the middle. You can still organize results into iteration directories on the filesystem if you have one.

Description optimization: This section requires the claude CLI tool (specifically claude -p) which is only available in Claude Code. Skip it if you're on Claude.ai.

Blind comparison: Requires subagents. Skip it.

Packaging: The package_skill.py script works anywhere with Python and a filesystem. On Claude.ai, you can run it and the user can download the resulting .skill file.

• Updating an existing skill: The user might be asking you to update an existing skill, not create a new one. In this case:
  • Preserve the original name. Note the skill's directory name and name frontmatter field — use them unchanged. E.g., if the installed skill is research-helper, output research-helper.skill (not research-helper-v2).
  • Copy to a writeable location before editing. The installed skill path may be read-only. Copy it to /tmp/skill-name/, immediately create the audit tool's pre-edit snapshot from that writeable copy, then edit and package from the copy with --regression-review. Do not pass --new-skill; a non-Git copy of an existing skill is still an existing skill.
  • If packaging manually, stage in /tmp/ first, then copy to the output directory — direct writes may fail due to permissions.

Cowork-Specific Instructions

If you're in Cowork, the main things to know are:

• You have subagents, so the main workflow (spawn test cases in parallel, run baselines, grade, etc.) all works. (However, if you run into severe problems with timeouts, it's OK to run the test prompts in series rather than parallel.)
• You don't have a browser or display, so when generating the eval viewer, use --static <output_path> to write a standalone HTML file instead of starting a server. Then proffer a link that the user can click to open the HTML in their browser.
• For whatever reason, the Cowork setup seems to disincline Claude from generating the eval viewer after running the tests, so just to reiterate: whether you're in Cowork or in Claude Code, after running tests, you should always generate the eval viewer for the human to look at examples before revising the skill yourself and trying to make corrections, using generate_review.py (not writing your own boutique html code). Sorry in advance but I'm gonna go all caps here: GENERATE THE EVAL VIEWER BEFORE evaluating inputs yourself. You want to get them in front of the human ASAP!
• Feedback works differently: since there's no running server, the viewer's "Submit All Reviews" button will download feedback.json as a file. You can then read it from there (you may have to request access first).
• Packaging works — package_skill.py just needs Python and a filesystem.
• Description optimization (run_loop.py / run_eval.py) should work in Cowork just fine since it uses claude -p via subprocess, not a browser, but please save it until you've fully finished making the skill and the user agrees it's in good shape.
• Updating an existing skill: The user might be asking you to update an existing skill, not create a new one. Follow the update guidance in the claude.ai section above.

Reference files

The agents/ directory contains instructions for specialized subagents. Read them when you need to spawn the relevant subagent.

• agents/grader.md — How to evaluate assertions against outputs
• agents/comparator.md — How to do blind A/B comparison between two outputs
• agents/analyzer.md — How to analyze why one version beat another

The references/ directory has additional documentation:

• references/eval_pipeline_schemas.md — JSON structures for evals.json, grading.json, benchmark.json, etc.
• references/sanitization_checklist.md — Checklist for sanitizing business-specific content before public distribution

The scripts/ directory includes deterministic gates used by this workflow:

• scripts/audit_skill_regression.py — compares an immutable old skill bundle with the edited bundle, creates an explicit disposition review, and verifies its hashes/evidence before packaging an existing skill.

Repeating one more time the core loop here for emphasis:

• Figure out what the skill is about
• For an existing skill, snapshot and inventory the old bundle before editing
• Draft or edit the skill
• Run claude-with-access-to-the-skill on test prompts
• With the user, evaluate the outputs:
  • Create benchmark.json and run eval-viewer/generate_review.py to help the user review them
  • Run quantitative evals
• Repeat until you and the user are satisfied
• Run and clear the existing-skill regression review; eval-only survival does not count
• Package the final skill and return it to the user.

Please add steps to your TodoList, if you have such a thing, to make sure you don't forget. If you're in Cowork, please specifically put "Create evals JSON and run eval-viewer/generate_review.py so human can review test cases" in your TodoList to make sure it happens.

Good luck!