open-knowledge-write-skill

Writing an OpenKnowledge skill

You are helping the user author an Agent Skill — a SKILL.md file (plus optional references/ and scripts/) that teaches an AI agent how to do a recurring task. In OpenKnowledge a skill is a first-class, versioned, installable artifact: you author it with the write / edit skill verbs, then install it into the user's editors.

Skills earn their keep by being recognized at the right moment and followed faithfully. Most of the craft is in two places: a description that triggers reliably, and a body short and concrete enough that the agent actually does what it says. Work the stages below in order, but jump to where the user already is.

Stage 1 — Capture intent and classify the skill

Gate — does this already exist? Check BEFORE you build. First list managed skills with skills({}), then read any likely match with skills({ name }); these are the Project/Global skills OpenKnowledge already manages. Also search the public marketplace with skills({ query: "<2-4 trigger words>" }) before drafting when the task sounds reusable beyond this project. Each marketplace row returns name, source, and description; inspect the strongest descriptions, then import a chosen candidate with import({ source, skill: name, add: [...] }) and adapt it only if reuse is the right call. Use the Vercel find-skills skill, npx skills find <query>, or manual skills.sh search only when the OK MCP skills({ query }) path is unavailable. If the user already has a skills.sh page open, pass the full skill-page URL as source, e.g. import({ source: "https://www.skills.sh/<owner>/<repo>/<skill>", add: [...] }) — the middle segment is the REPO, not a literal skills. Do not run npx skills add as the default install path in this flow: import through OpenKnowledge (import({ source, skill, add })) so the skill lands as a real folder with provenance, versioning, and managed fan-out; add says where it goes, and install afterwards changes where it lives. Use 2-4 concrete trigger phrases from the user's request plus the domain or tool name, then open/read the strongest candidates' descriptions before judging. If an existing or public skill covers most of it, STOP and recommend reuse — a near-duplicate with overlapping triggers mis-fires and dilutes both. If a public skill is close but not exact, decide WITH the user whether to import/adapt it into OpenKnowledge, install it outside OK with the Skills CLI, or write a narrower companion whose description explicitly hands off to it. Build a new skill only when it is genuinely distinct. Surface the overlap and the installed-skill plus marketplace search outcome before drafting or writing anything. This is a disclosure gate: tell the user what you checked, what matched, and why reuse/import/adapt/new-skill is the right next step. Never discover overlap after the skill is written. If skills({ query }) / skills.sh is unreachable, say so plainly and continue with the installed-skill check.

Ask only what you can't infer:

• What recurring task should this skill handle? Get one concrete example.
• Skill type, because it sets how much rigor to apply:
  • Reference / technique (most skills) — "how to do X." Prose body, examples.
  • Discipline — enforces a behavior the agent tends to skip under pressure (e.g. "always write a failing test first"). These need the RED baseline + pressure-testing in Stage 4–6; reference skills don't.
• Degrees of freedom (calibrate body precision to task fragility): high (free prose — judgment tasks), medium (parameterized steps), low (a fixed scripts/ command — when any deviation breaks the result). Don't over-specify a judgment task or under-specify a fragile one.

Stage 2 — Resolve scope FIRST (never infer silently)

Scope determines where the skill lives and where install fans it. This is the user's decision and has different blast radius — make it explicit.

Default heuristic: inside an OK project and the task is specific to it → project; "for all my work / globally" → global; otherwise ask one question. State the choice and its consequence before writing.

Stage 3 — Plan the contents

• Body = the durable, reusable instructions — under ~500 lines. If it's growing past that, move depth into references/<topic>.md (loaded only when needed) and point at it from the body. For a project skill the reference auto-connects in the graph either way, so a backticked `references/<topic>.md` path is fine; use a [[references/<topic>]] wiki-link only when you want the mention to be a clickable inline link. For a global skill use a plain backtick path — global references aren't graph docs, so a wiki-link there dangles. Keep references one level deep.
• Do NOT include: a README/CHANGELOG/QUICK_REFERENCE, install instructions for the skill itself, version histories, or anything host-specific. The skill is the instructions, not documentation about the instructions.
• For a discipline skill, plan the failure mode you're correcting and how you'll prove the skill fixes it (Stage 4).

Stage 4 — RED baseline (discipline skills only)

Before writing the skill, run the scenario WITHOUT it and capture what the agent does wrong — verbatim, including its rationalizations ("the test is trivial so I skipped it"). Those rationalizations are the exact loopholes the skill body must close. Run the scenario in a fresh agent session with the skill absent — a new chat, a sub-agent, or a second terminal — and keep the transcript; if you have no way to run it clean, reason through the baseline with the user instead. Skip this stage for plain reference skills.

Stage 5 — Draft the skill

Author the source with the skill verb (fs-direct; a live preview updates if a server is running):

write({ skill: { name: "<lowercase-hyphen-name>", description: "<triggers>", body: "<markdown>", scope: "project" } })

Frontmatter contract (validated on write — get it right):

• name — lowercase letters, digits, hyphens; ≤64; equals the directory.
• description — ≤1024 chars, no XML tags, no version field. See Stage 7.
• Nothing else. OK never injects its own frontmatter; bookkeeping lives in .ok/.

Write the body as direct instructions to the agent (imperative, second person), concrete over abstract.

Add depth files — references/*.md (loaded on demand) and scripts/* (shown as text, never executed by OK) — through the skill verbs, never native Write/cat:

# write one or more bundle files (independent of body — no need to resend SKILL.md)
write({ skill: { name: "<name>", files: [{ path: "references/tiers.md", content: "..." }] } })

# surgical edit inside one bundle file (mirrors edit({ document }))
edit({ skill: { name: "<name>", file: "references/tiers.md", find: "...", replace: "..." } })

# list the bundle, then read one file (no native cat)
skills({ name: "<name>" })                         # → files: [{ path, kind }]
skills({ name: "<name>", file: "references/tiers.md" })   # → { path, kind, text }

# delete specific bundle files (omit `files` to delete the whole skill)
delete({ skill: { name: "<name>", files: ["references/tiers.md"] } })

Paths are skill-relative and must stay inside the skill dir (no ../, no absolute paths). references/ and scripts/ are the conventional homes and the ones to reach for; other roots (assets/, a per-harness dir) are accepted because published skills ship them and an import preserves them verbatim. Keep references one level deep. A project .md reference becomes a live content doc that auto-connects to its SKILL in the graph regardless of how the body mentions it — a backticked `references/<name>.md` path joins the graph just like a [[references/<name>]] wiki-link. Reach for a wiki-link (or [label](references/<name>.md)) only when you want a clickable inline link. Global skills are different: their references aren't graph docs, so use a plain backtick path there — a wiki-link would dangle.

Stage 6 — GREEN eval + refactor

Re-run the scenario WITH the skill. For a discipline skill, pressure-test: combine 2–3 pressures (time, authority, sunk cost) and confirm the agent still follows the rule — then patch any loophole and re-test (references/pressure-testing.md). For a reference skill, confirm the agent now does the task correctly and the body isn't longer than it needs to be. Cut anything the agent already knows.

Stage 7 — Optimize the description (this is what makes the skill fire)

The description is the only thing the agent sees when deciding whether to load the skill. Get it right (references/description-optimization.md):

• Triggers, not a summary. Say WHEN to use it, in the user's words and phrasings — NOT a recap of the body. Summarizing the workflow in the description makes the agent follow the description and skip the body.
• Concrete and a little pushy to fight under-triggering: name the situations, verbs, and phrasings that should activate it.
• Sanity-check against near-miss queries: phrasings that SHOULD trigger it and adjacent ones that should NOT.

Stage 8 — Install (choose where it's available)

The skill is already live for whichever agent reads the folder it was created in — its own folder IS the skill (there is no draft state). install manages WHERE ELSE it is available, additively:

install({ name: "<name>", add: ["codex", "opencode"] })   // add locations
install({ name: "<name>", remove: ["codex"] })            // remove locations
install({ name: "<name>", add: ["codex"], mode: "link" }) // ...as a symlink
install({ name: "<name>", convert: ["codex"], mode: "copy" })  // change one location's form
install({ name: "<name>", source: ".team/skills" })       // move the real folder (run alone)

Location ids are editor host ids (claude … pi), agents (the vendor-neutral hub), or a custom root path like .team/skills.

Which form a location takes. mode applies only to the locations the call names — those in add, or those in convert — and to nothing else. Omit it on add and the new location takes the form the skill already uses. There is no skill-wide mode: installing never rewrites a location you did not name. Changing an existing location is convert, which leaves membership alone; pass every location to make them uniform.

What each form means. A symlink points at the source, so it cannot drift. A copy is its own folder and refreshes automatically from the source — until someone hand-edits it, at which point it forks and is never overwritten again.

Lifecycle. Edit with edit({ skill }); it routes to the source and versions in place. There is no "uninstall everywhere" — removing every other location still leaves the source folder loading for its agent, and a skill stops existing only via delete({ skill }), which removes the source and every location. Roll a project skill back with history({ skill }) → restore_version({ skill, version }); global skills are unversioned in this build. For a project skill, commit the skill folders — they are ordinary files in git, so teammates get them on pull.

Reminders

• Prefer ONE good skill over many overlapping ones; split only when triggers diverge. (The Stage 1 gate is where you ENFORCE this — don't leave overlap to discover later.)
• Scope is the only placement decision — don't fold harness/format/toolchain assumptions into it, and don't bake one into the scope question's wording. You are authoring an OpenKnowledge skill: create it with write({ skill }) — it lands as a real folder at the project's default skill home with attribution and versioning — and fan it out with install; don't hand-scatter copies across editor dirs, because install owns fan-out and refreshes unedited copies from the source (a hand-edited copy forks and stops refreshing). If you load this flow, author through it.
• Ground claims about how skills behave (versioning, install targets, scope semantics) in this guide or the tool descriptions — don't assert system facts from assumption.
• Avoid blanket ALWAYS/NEVER rules without a stated reason — they read as noise and get ignored. Explain the why.
• A skill that ships executable scripts/ is projected verbatim into another agent's trust domain — only include scripts the user has reviewed.