Skill Graph Audit

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

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:

1. 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.
2. 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.

Related Skills

• 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

Source: athola/claude-night-market → plugins/abstract/skills/skill-graph-audit/SKILL.md