/skill-graph-audit

name: skill-graph-audit
role: library
description: 'Audit Skill() refs; detect hubs, isolates, and dangling targets. Use when auditing skills.'
category: skill-management
alwaysApply: false
tools: []
modules:
- modules/usage.md
- modules/interpretation.md

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

# Positive control first. The fixture tree has a known answer (1
# bug-class dangling ref, 1 isolate, 2 edges); a regex that stopped
# matching would report plugins/ as clean, and this catches it.
uv run pytest -o addopts= -q plugins/abstract/tests/scripts/test_skill_graph.py -k Planted

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:

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 planted fixture test passed before the audit ran; a clean

report on `plugins/` is not reportable without it

• [ ] 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`