> Night Market Skill — ported from claude-night-market/abstract. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

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.

1. 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.

Related Skills

• Skill(abstract:skills-eval) -- per-skill quality scoring
• Skill(abstract:plugin-review) -- plugin manifest + 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