Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Manage persistent Knowledge Graph for specifications. Provides read, query, update, and validation capabilities for codebase analysis caching. Use when: spec-to-tasks needs to cache/reuse codebase analysis, task-implementation needs to validate task dependencies or contracts, spec-quality needs to synchronize provides, or any command needs to query existing patterns/components/APIs. Reduces redundant codebase exploration by caching agent discoveries.
.claude/skills/giuseppe-trisciuoglio-knowledge-graph/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 820% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 2095% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 427% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 1260% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 47% | 0% |
The Knowledge Graph (KG) is a persistent JSON file that stores discoveries from codebase analysis, eliminating redundant exploration and enabling task validation.
Location: docs/specs/[ID-feature]/knowledge-graph.json
Key Benefits:
Use this skill when:
Trigger phrases:
1. read-knowledge-graph - Load and parse KG for a specification
docs/specs/001-feature/)2. query-knowledge-graph - Query specific sections (components, patterns, APIs)
3. update-knowledge-graph - Update KG with new discoveries
4. validate-against-knowledge-graph - Validate task dependencies against KG
5. validate-contract - Validate provides/expects between tasks
6. extract-provides - Extract symbols from implemented files
7. aggregate-knowledge-graphs - Merge patterns from all specs
See references/query-examples.md for detailed usage examples.
Read Knowledge Graph:
Input: /knowledge-graph read docs/specs/001-hotel-search/
Output: {
metadata: { spec_id: "001-hotel-search", version: "1.0" },
patterns: { architectural: [...], conventions: [...] },
components: { controllers: [...], services: [...]}
}Query Components:
Input: /knowledge-graph query docs/specs/001-hotel-search/ components {"category": "services"}
Output: [{ id: "comp-svc-001", name: "HotelSearchService", type: "service"}]Update Knowledge Graph:
Input: /knowledge-graph update docs/specs/001-hotel-search/ {
patterns: { architectural: [{ name: "Repository Pattern"}] }
}
Output: "Added 1 pattern to knowledge graph"Validate Dependencies:
Input: /knowledge-graph validate docs/specs/001-hotel-search/ {
components: ["comp-repo-001"]
}
Output: { valid: true, errors: [], warnings: [] }See references/examples.md for comprehensive workflow examples.
See references/schema.md for complete JSON schema with examples.
See references/integration-patterns.md for detailed integration with Developer Kit commands.
See references/error-handling.md for comprehensive error handling strategies and recovery procedures.
See references/performance.md for optimization strategies and performance characteristics.
See references/security.md for security considerations, threat mitigation, and best practices.
When to Query KG: Before codebase analysis, task generation, dependency validation
When to Update KG: After agent discoveries, component implementation, pattern discovery
KG Freshness:
See references/performance.md and references/security.md for detailed best practices.
knowledge-graph.json files.docs/specs/[ID]/ paths.See references/error-handling.md and references/security.md for complete constraints and warnings.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 3,809 | 9,176 | +141% | 1 | 1 | 0% | 358 | 2,744 | +666% | 0 | 0 | — |
case-01 | fail→fail | 3,517 | 4,892 | +39% | 1 | 1 | 0% | 320 | 2,384 | +645% | 0 | 0 | — |
case-03 | fail→fail | 3,024 | 5,533 | +83% | 1 | 1 | 0% | 313 | 2,537 | +711% | 0 | 0 | — |
case-04 | fail→pass | 5,849 | 7,625 | +30% | 1 | 1 | 0% | 337 | 3,101 | +820% | 0 | 0 | — |
case-05 | fail→pass | 3,297 | 8,814 | +167% | 1 | 1 | 0% | 136 | 2,985 | +2095% | 0 | 0 | — |
case-06 | fail→pass | 3,434 | 7,235 | +111% | 1 | 1 | 0% | 522 | 2,749 | +427% | 0 | 0 | — |
case-11 | fail→fail | 6,318 | 3,775 | -40% | 1 | 1 | 0% | 303 | 1,791 | +491% | 0 | 0 | — |
case-07 | fail→pass | 2,705 | 7,054 | +161% | 1 | 1 | 0% | 216 | 2,938 | +1260% | 0 | 0 | — |
case-08 | pass→pass | 10,543 | 4,423 | -58% | 1 | 1 | 0% | 1,660 | 2,149 | +29% | 0 | 0 | — |
case-09 | pass→pass | 6,395 | 4,037 | -37% | 1 | 1 | 0% | 905 | 1,994 | +120% | 0 | 0 | — |
case-10 | fail→pass | 8,129 | 5,787 | -29% | 1 | 1 | 0% | 1,660 | 2,437 | +47% | 0 | 0 | — |
case-12 | fail→fail | 3,186 | 3,956 | +24% | 1 | 1 | 0% | 265 | 2,201 | +731% | 0 | 0 | — |
case-13 | fail→pass | 5,183 | 10,048 | +94% | 1 | 1 | 0% | 259 | 2,441 | +842% | 0 | 0 | — |
case-14 | fail→fail | 6,280 | 6,593 | +5% | 1 | 1 | 0% | 588 | 2,536 | +331% | 0 | 0 | — |
case-15 | fail→pass | 7,594 | 2,839 | -63% | 1 | 1 | 0% | 1,165 | 1,922 | +65% | 0 | 0 | — |
case-16 | pass→pass | 13,657 | 7,915 | -42% | 1 | 1 | 0% | 2,184 | 2,736 | +25% | 0 | 0 | — |
case-17 | fail→pass | 8,383 | 4,841 | -42% | 1 | 1 | 0% | 1,474 | 2,281 | +55% | 0 | 0 | — |
case-18 | fail→pass | 4,674 | 11,455 | +145% | 1 | 1 | 0% | 715 | 3,640 | +409% | 0 | 0 | — |
case-19 | fail→pass | 5,570 | 4,243 | -24% | 1 | 1 | 0% | 348 | 2,192 | +530% | 0 | 0 | — |
case-20 | fail→pass | 11,344 | 3,613 | -68% | 1 | 1 | 0% | 1,717 | 2,074 | +21% | 0 | 0 | — |
case-21 | fail→pass | 3,881 | 4,269 | +10% | 1 | 1 | 0% | 591 | 2,319 | +292% | 0 | 0 | — |
case-22 | fail→pass | 5,546 | 5,143 | -7% | 1 | 1 | 0% | 222 | 2,324 | +947% | 0 | 0 | — |
case-23 | fail→pass | 12,648 | 9,428 | -25% | 1 | 1 | 0% | 2,861 | 3,158 | +10% | 0 | 0 | — |
case-24 | fail→pass | 9,566 | 11,359 | +19% | 1 | 1 | 0% | 1,645 | 3,241 | +97% | 0 | 0 | — |
case-25 | fail→fail | 10,600 | 19,005 | +79% | 1 | 1 | 0% | 2,330 | 5,090 | +118% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted, and 19 counted toward the lift figure. The other 6 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +60 percentage points is the difference between those two pass rates over the 19 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.