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dfrostar/neuralmind/skills/github-issue-audit/SKILL.md

github-issue-audit

Audit a GitHub repository's open issues for hygiene — stale issues, likely duplicates, missing/incorrect labels, and issues that are actually already fixed on main. Use when asked to triage the backlog, clean up issues, find duplicates, or report on issue health. Produces a ranked, actionable report; it never closes or edits issues on its own.

Source repository stars
22
Declared platforms
0
Static risk flags
0
Last source update
2026-07-28
Source checked
2026-07-28

Decision brief

What it does—and where it fits

Produce an honest, actionable audit of a repo's open issues. You read and recommend; you do not close, relabel, or comment on issues unless the user explicitly asks for that action after seeing the report.

Best for

  • Use when asked to triage the backlog, clean up issues, find duplicates, or report on issue health.

Not for

  • Don't close an issue on a hunch. "Already fixed" needs a commit ref;
  • Don't relabel to match a convention you assumed — check the repo's

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/dfrostar/neuralmind --skill "skills/github-issue-audit"
Safe inspection promptEditorial

Inspect the Agent Skill "github-issue-audit" from https://github.com/dfrostar/neuralmind/blob/943df635efb24992b4c53470e856a4438eb1c6cb/skills/github-issue-audit/SKILL.md at commit 943df635efb24992b4c53470e856a4438eb1c6cb. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Procedure

    Be frugal with API calls — batch, use minimaloutput when the full body isn't needed, and only deep-read issues that a cheap signal already flagged.

    Be frugal with API calls — batch, use minimaloutput when the full body isn't needed, and only deep-read issues that a cheap signal already flagged.
  2. 02

    What "audit" means here

    Four checks, run over every open issue, reported most-actionable first:

    Stale — no activity for a long window (default 90 days). DistinguishLikely duplicate — another open or recently-closed issue covers theLabel hygiene — missing a type label (bug/feat/docs), missing a
  3. 03

    Report shape

    A markdown table, then a short "recommended actions" list. Rank by actionability (fixed duplicate mislabeled stale), not by issue number.

    A markdown table, then a short "recommended actions" list. Rank by actionability (fixed duplicate mislabeled stale), not by issue number.Every "close" or "relabel" suggestion is a proposal. Confidence is honest: high only when you verified (a commit ref, an exact-duplicate body); medium/low otherwise, and say what would raise it.
  4. 04

    Acting on the audit (only when asked)

    If the user says "go ahead and apply these" (or similar), then and only then: mutate in small, reversible steps — relabel before closing, close with a comment that references the duplicate/fix, and never bulk-close on medium confidence without a second confirmation. Report what…

    If the user says "go ahead and apply these" (or similar), then and only then: mutate in small, reversible steps — relabel before closing, close with a comment that references the duplicate/fix, and never bulk-close on m…

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score75/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars22SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
dfrostar/neuralmind
Skill path
skills/github-issue-audit/SKILL.md
Commit
943df635efb24992b4c53470e856a4438eb1c6cb
License
MIT
Collected
2026-07-28
Default branch
main
View the original SKILL.md

GitHub Issue Audit

Produce an honest, actionable audit of a repo's open issues. You read and recommend; you do not close, relabel, or comment on issues unless the user explicitly asks for that action after seeing the report.

What "audit" means here

Four checks, run over every open issue, reported most-actionable first:

  1. Stale — no activity for a long window (default 90 days). Distinguish abandoned (needs a close/ping) from quietly-still-valid (needs a label, not a close).
  2. Likely duplicate — another open or recently-closed issue covers the same problem. Report the pair with a one-line reason; never assume — the maintainer confirms.
  3. Label hygiene — missing a type label (bug/feat/docs), missing a priority, or a label that contradicts the body. Propose the corrected set.
  4. Already fixed — the described bug looks resolved on main. Cross-check with list_commits / search_code before claiming this; a fixed-looking issue that's actually still open is the most embarrassing miss.

Procedure

1. get_me                         confirm permissions/context
2. list_issues (state=open)       page in batches of 5-10, minimal_output
3. For each issue:
     issue_read                   full body + current labels + timeline
     classify into the 4 buckets above
4. For "already fixed" candidates only:
     search_code / list_commits   verify the fix actually landed
5. Emit the report (below). Stop. Do not mutate anything.

Be frugal with API calls — batch, use minimal_output when the full body isn't needed, and only deep-read issues that a cheap signal already flagged.

Report shape

A markdown table, then a short "recommended actions" list. Rank by actionability (fixed > duplicate > mislabeled > stale), not by issue number.

| # | Title | Finding | Confidence | Suggested action |
|---|-------|---------|------------|------------------|
| 142 | "crash on empty repo" | already fixed in a1b2c3d | high | close w/ ref |
| 130 | "add --json flag" | dup of #98 | medium | link + close one |

Every "close" or "relabel" suggestion is a proposal. Confidence is honest: high only when you verified (a commit ref, an exact-duplicate body); medium/low otherwise, and say what would raise it.

Acting on the audit (only when asked)

If the user says "go ahead and apply these" (or similar), then and only then: mutate in small, reversible steps — relabel before closing, close with a comment that references the duplicate/fix, and never bulk-close on medium confidence without a second confirmation. Report what you changed.

Anti-patterns

  • Don't close an issue on a hunch. "Already fixed" needs a commit ref; "duplicate" needs the sibling issue number.
  • Don't relabel to match a convention you assumed — check the repo's actual labels with get_label / the labeler config first.
  • Don't dump all issues verbatim. The value is the classification, not a reprint of the backlog.

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