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Skill Graph Audit Skill for Claude

Audit Skill() refs; detect hubs, isolates, and dangling targets. Use when auditing skills.

2k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
324
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/athola/claude-night-market --skill skill-graph-audit

The instruction itself

11 sections, as written by the author

Skill Graph Audit

Overview

Build a directed graph of Skill(plugin:name) invocations across the

marketplace and surface composition patterns: which skills are heavily

referenced (hubs), which orchestrate many others (orchestrators), which

have no incoming or outgoing references (isolates), and which point at

non-existent skills (dangling references).

The federation graph is now derivable from source rather than

hand-curated.

When To Use

  • Before a documentation pass on skill composition
  • After a renaming or retirement to catch broken Skill() references
  • During quarterly audits to spot orphaned skills
  • When evaluating consolidation candidates (hubs are higher-risk to merge)
  • When a new skill's outbound references should be sanity-checked

When NOT To Use

  • For per-skill quality scoring, use Skill(abstract:skills-eval) instead
  • For frontmatter/structure validation, use Skill(abstract:plugin-review)
  • For hook-specific audits, use Skill(abstract:hooks-eval)

Quick Start

python3 plugins/abstract/scripts/skill_graph.py \
  --plugins-root plugins --top-n 10

For machine-readable output:

python3 plugins/abstract/scripts/skill_graph.py \
  --plugins-root plugins --format json --output reports/skill-graph.json

See modules/usage.md for full CLI reference and example workflows.

Core Outputs

| Output | Meaning | Action when high |

|--------|---------|------------------|

| Hubs | Most-referenced skills | Treat as core API; retire with extreme care |

| Orchestrators | Skills that call many others | Verify each ref still resolves |

| Isolates | Zero in / zero out | Check role: library? entrypoint? typo? |

| Dangling: bugs | Missing internal target | Fix immediately (typo or retired skill) |

| Dangling: external | Reference to external plugin | Document plugin dependency |

| Dangling: placeholders | Template text like -NAME | Verify intentional |

See modules/interpretation.md for false-positive guidance and

isolation taxonomy.

Dogfood Evidence

This skill itself was scaffolded TDD-first; on first run against

plugins/, it caught two genuine dangling refs that the manual

audit (2026-04-25) had missed:

  • attune:makefile-generation -> abstract:makefile-dogfooder

(script name confused with skill name)

  • imbue:karpathy-principles -> spec-kit:speckit-clarify

(command referenced as skill)

Both were converted to correct command-style references in the

same session.

Verification

Two ways to validate the audit output is trustworthy:

  • Test-suite correctness check: Run `pytest -o addopts=

plugins/abstract/tests/scripts/test_skill_graph.py` to confirm

extraction, graph construction, ranking, isolate detection, and

dangling-ref classification all pass on the current code. The

-o addopts= flag bypasses the package-wide coverage gate, which

would otherwise fail on a single-file run.

  • Round-trip smoke check: Note the dangling-ref count from a

baseline run, fix one or more flagged references, then rerun and

verify the count drops by at least the number fixed. If the count

does not move, the report is stale or the regex missed a syntax

variant.

Exit Criteria

  • [ ] The graph builds: skill_graph.py runs against plugins/

without error and emits a node/edge count.

  • [ ] Dangling references are classified into bugs, external, and

placeholders (the three Core Outputs rows resolve).

  • [ ] Every Dangling: bugs entry is either fixed in the same

session or filed as a tracked issue.

  • [ ] pytest -o addopts= plugins/abstract/tests/scripts/test_skill_graph.py

passes.

  • [ ] The round-trip smoke check shows the dangling-ref count drops

by at least the number of references fixed.

  • Skill(abstract:skills-eval): per-skill quality scoring
  • Skill(abstract:plugin-review): plugin manifest and structure
  • Skill(abstract:hooks-eval): hook-specific validation
  • Skill(abstract:rules-eval): rules directory validation

References

  • Implementation: plugins/abstract/scripts/skill_graph.py
  • Tests: plugins/abstract/tests/scripts/test_skill_graph.py
  • Composition documentation:

docs/quality-gates.md#skill-level-quality-gate-composition

  • Skill role taxonomy: docs/skill-integration-guide.md#skill-role-taxonomy

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How to use it

Copy the folder

Take athola/skill-graph-audit from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.