graph-audit
Graph Audit
Coverage
- Schema conformance: checking that every
skills/<name>/SKILL.mdvalidates againstschemas/skill.schema.jsonwithout errors - Manifest sync: verifying that
examples/skills.manifest.sample.jsonmatches the output ofscripts/generate-manifest.jsrun against the current skills - Relation integrity: confirming that every target named under
relations.*corresponds to a real sibling skill directory and uses the right predicate semantics (related/broader/boundary/disjoint_with/verify_with/depends_on) - Eval artifact coherence: ensuring that
eval_artifacts: presentis backed by a real eval artifact underexamples/evals/that names the skill in itsskill_namefield - Grounding presence: confirming that every
scope: codebaseskill has a fully populatedgroundingblock withdomain_object,grounding_mode,truth_sources,failure_modes, andevidence_priority - Name-directory parity: checking that a skill's
namefield matches the name of the parent directory (required for SKILL.md compatibility)
Philosophy
Skill graphs fail silently. A broken relation or a drifted enum value does not crash the agent — it just makes retrieval subtly wrong, and subtly-wrong retrieval is worse than a crash because nothing tells you to look. The audit's job is to turn every silent bug into a loud one before the graph accumulates enough drift that agents can no longer trust its edges.
Key Files
More from jacob-balslev/skill-graph
a11y
Use when building or reviewing interactive UI, forms, navigation, or dynamic content. Covers semantic HTML, keyboard access, focus management, labeling, state-change announcement, and reduced-motion / high-contrast preferences. Do NOT use for color-palette creation, visual branding, feedback-state staging, or prose reading-level accessibility - those belong to `visual-design-foundations`, `interaction-feedback`, and documentation respectively.
7intent-recognition
Use BEFORE any tool call that could modify state, touch sensitive targets, rewrite history, install dependencies, publish packages, or expose credentials/environment data. Classifies intent into Passive/Read, Reconnaissance, Modification, or Destructive/Irreversible using operation type plus target sensitivity, then runs Identify / Confirm / Verify before action. Do NOT use for deciding what code to write, executing already-classified work, reactive post-execution guardrails, or defining upstream governance policy.
6dependency-architecture
Use when designing or auditing dependency structure: package boundaries, runtime vs build dependencies, adapter layers, duplicate-purpose libraries, supply-chain risk, upgrade policy, lock-in, and dependency graph health. Do NOT use for choosing a major framework (use `framework-fit-analysis`), vulnerability-only review (use `owasp-security`), or routine refactoring without dependency boundary changes (use `refactor`).
6information-architecture
Use when structuring information for findability: navigation, page hierarchy, docs architecture, sitemap shape, labeling systems, wayfinding, and content grouping. Do NOT use for formal category-governance work (use `taxonomy-design`), responsive page composition (use `layout-composition`), component/token architecture (use `design-system-architecture`), or sentence-level UI text (use `microcopy`).
6design-thinking
Use when orchestrating a full human-centered design process across discovery, definition, ideation, prototyping, and testing — when uncertain which stage of the arc a team is in, when deciding whether to loop back, or when routing to the right stage-specific sibling skill. Do NOT use for single-stage execution (go directly to problem-framing, user-research, research-synthesis, journey-mapping, ideation, prototyping, or usability-testing) or for engineering domain discovery (use event-storming).
6knowledge-modeling
Use when deciding *which representation paradigm* fits a piece of domain knowledge — knowledge graph vs frames vs production rules vs semantic network vs concept map vs procedural ontology vs hybrid — when designing AI-agent context systems, building a knowledge base, structuring a skill or reference library, or planning a GraphRAG retrieval pipeline. Covers the seven paradigms with structure / best-for / weakness tables, the tacit-to-explicit knowledge acquisition pipeline (elicitation → articulation → formalization → validation → encoding), knowledge graph design principles (reify when needed, separate schema from instance, label precisely, bidirectional naming, minimal redundancy), the four knowledge-validation types (completeness / consistency / relevance / currency) plus expert walkthrough, the seven-phase knowledge lifecycle (Create / Validate / Publish / Use / Monitor / Update / Retire), the application to AI-agent systems (skills as frames, routing as rules, memory as graph), and a full GraphRAG section covering the five patterns (entity-anchored retrieval, relationship-aware context, path-based reasoning, subgraph summarization, hybrid vector+graph) with rules for when graph-grounded retrieval beats plain RAG. Do NOT use for the *human-readable* domain analysis layer (use `conceptual-modeling`), for the database / ER design layer (a logical-modeling skill), for pure classification hierarchies (a taxonomy skill), for formal ontology axioms (an ontology skill), or for the live skill-library tooling that consumes modeled knowledge (use `skill-infrastructure`).
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