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Get Started Free →This skill should be used when the user asks to "generate repository metadata", "create catalog-info.yaml", "add repo metadata", "document repository structure", or mentions generating structured metadata for service catalog or architecture documentation.
.claude/skills/aiskillstore-repo-metadata/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 158% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 75% | 0% |
Generate structured catalog-info.yaml metadata for repositories using industry-standard conventions (based on Backstage catalog format). This metadata enables cross-repository architecture analysis and service catalog functionality.
Create and maintain catalog-info.yaml files that describe a repository's role in the broader architecture. This metadata feeds into architectural views, dependency graphs, and service groupings across the entire organization.
Trigger this skill when:
The catalog-info.yaml file follows Backstage conventions with Astrabit-specific extensions:
yamlapiVersion: astrabit.io/v1 kind: Component metadata: name: service-name # Required: Unique identifier description: Brief description tags: - backend - user-management spec: # Service Classification type: service # Required: service, gateway, worker, library, frontend, database category: backend # Broader category domain: trading # Business domain owner: platform-team # Team responsible # Dependencies (Upstream) dependsOn: - component: auth-service type: service - component: user-db type: database # APIs Provided providesApis: - name: User API type: REST definition: ./openapi.yaml # APIs Consumed consumesApis: - name: Auth API providedBy: auth-service # Events Produced eventProducers: - name: user-events type: kafka topic: user.created schema: avro # Events Consumed eventConsumers: - name: order-events type: kafka topic: order.placed group: user-service-group # HTTP Routes (for gateways/services) routes: - path: /api/users/* methods: [GET, POST, PUT, DELETE] handler: this - path: /api/auth/* methods: [POST] forwardsTo: auth-service # Infrastructure runtime: nodejs # nodejs, python, go, java, etc. framework: nestjs # nestjs, fastapi, spring, etc.
Gather information about the repository:
bash python skills/repo-docs/scripts/analyze-repo-structure.py /path/to/repo python skills/repo-docs/scripts/find-integration-points.py /path/to/repo
INTEGRATIONS.md - contains upstream/downstream relationshipsARCHITECTURE.md - contains service role and dependenciesREADME.md - contains basic description and tech stackBased on analysis, generate catalog-info.yaml with detected values:
| Field | Detection Method | |-------|------------------| | name | Repo name or package.json name field | | description | README title/description or generated from code | | type | Inferred from code patterns (gateway has routes, worker has consumers only) | | runtime | From package files (package.json, pyproject.toml, go.mod) | | framework | From dependencies (nestjs, fastapi, spring-boot, etc.) | | dependsOn | From integration point scanning | | eventProducers | From kafka.producer or similar patterns | | eventConsumers | From @KafkaListener, @EventListener, or similar patterns | | routes | From @Controller, @GetMapping, router definitions |
Present the generated metadata to the user in a table format:
markdownGenerated catalog-info.yaml: | Field | Value | Source | |-------|-------|--------| | name | user-service | repo name | | type | service | detected: has routes and consumers | | runtime | nodejs | package.json | | framework | nestjs | dependencies | | domain | unknown | ❌ needs input | | owner | unknown | ❌ needs input | | dependsOn | auth-service, user-db | integration scan |
Prompt user to review and fill in missing fields (marked with ❌).
Write catalog-info.yaml to the repository root.
Offer to update related docs to reference the new metadata file:
catalog-info.yaml in README.mdINTEGRATIONS.md to be consistent with metadata| Type | Indicators | |------|------------| | gateway | Has routes with forwardsTo, handles external requests, minimal business logic | | service | Has both providesApis and consumesApis, business logic | | worker | Only eventConsumers, no HTTP routes, background processing | | library | No APIs consumed, only provides, shared utilities | | frontend | type: frontend in package.json, has build artifacts | | database | Contains migrations, schemas, no application code |
Use scripts/generate-metadata.py to automate metadata generation:
bash# Generate from current directory python skills/repo-metadata/scripts/generate-metadata.py # Generate from specific repo python skills/repo-metadata/scripts/generate-metadata.py /path/to/repo # Output as JSON for inspection python skills/repo-metadata/scripts/generate-metadata.py --format json
The script:
catalog-info.yaml contentreferences/schema.md - Complete catalog-info.yaml schema referencereferences/detection-patterns.md - Patterns for detecting service characteristicsexamples/catalog-info-template.yaml - Full template with all fieldsexamples/catalog-info-gateway.yaml - Example gateway serviceexamples/catalog-info-worker.yaml - Example worker serviceexamples/catalog-info-library.yaml - Example shared libraryBefore finalizing metadata, verify:
name is unique across the organizationtype correctly classifies the servicedomain and owner are filled (not auto-detected)dependsOn lists all upstream dependencieseventProducers and eventConsumers are completecatalog-info.yaml)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 58,869 | 338,047 | +474% | 1 | 1 | 0% | 3,116 | 2,123 | -32% | 0 | 0 | — |
case-02 | fail→fail | 21,416 | 35,541 | +66% | 1 | 1 | 0% | 2,888 | 3,955 | +37% | 0 | 0 | — |
case-03 | fail→fail | 46,637 | 35,370 | -24% | 1 | 1 | 0% | 229 | 1,950 | +752% | 0 | 0 | — |
case-04 | pass→pass | 43,748 | 35,457 | -19% | 1 | 1 | 0% | 6,143 | 6,113 | -0% | 0 | 0 | — |
case-05 | pass→pass | 18,538 | 42,690 | +130% | 1 | 1 | 0% | 2,425 | 3,705 | +53% | 0 | 0 | — |
case-06 | pass→pass | 22,150 | 17,571 | -21% | 1 | 1 | 0% | 2,777 | 4,399 | +58% | 0 | 0 | — |
case-07 | fail→fail | 10,781 | 6,717 | -38% | 1 | 1 | 0% | 1,803 | 2,999 | +66% | 0 | 0 | — |
case-08 | pass→pass | 14,229 | 27,153 | +91% | 1 | 1 | 0% | 1,546 | 3,085 | +100% | 0 | 0 | — |
case-09 | fail→pass | 19,917 | 7,752 | -61% | 1 | 1 | 0% | 2,519 | 3,032 | +20% | 0 | 0 | — |
case-10 | fail→pass | 9,475 | 19,201 | +103% | 1 | 1 | 0% | 1,691 | 4,357 | +158% | 0 | 0 | — |
case-11 | fail→pass | 17,106 | 8,659 | -49% | 1 | 1 | 0% | 2,329 | 3,242 | +39% | 0 | 0 | — |
case-12 | fail→pass | 18,564 | 9,846 | -47% | 1 | 1 | 0% | 2,079 | 3,192 | +54% | 0 | 0 | — |
case-13 | pass→pass | 25,066 | 10,327 | -59% | 1 | 1 | 0% | 1,756 | 2,839 | +62% | 0 | 0 | — |
case-14 | fail→pass | 14,243 | 13,941 | -2% | 1 | 1 | 0% | 1,808 | 3,160 | +75% | 0 | 0 | — |
case-15 | fail→pass | 18,695 | 14,231 | -24% | 1 | 1 | 0% | 1,373 | 3,029 | +121% | 0 | 0 | — |
case-16 | fail→pass | 20,784 | 7,687 | -63% | 1 | 1 | 0% | 3,258 | 2,214 | -32% | 0 | 0 | — |
case-17 | fail→pass | 9,756 | 12,181 | +25% | 1 | 1 | 0% | 1,476 | 2,209 | +50% | 0 | 0 | — |
case-18 | fail→pass | 12,215 | 9,686 | -21% | 1 | 1 | 0% | 2,076 | 3,084 | +49% | 0 | 0 | — |
case-19 | fail→pass | 13,243 | 14,502 | +10% | 1 | 1 | 0% | 2,249 | 3,829 | +70% | 0 | 0 | — |
case-20 | fail→pass | 7,739 | 23,467 | +203% | 1 | 1 | 0% | 1,479 | 3,560 | +141% | 0 | 0 | — |
case-21 | pass→pass | 7,777 | 3,880 | -50% | 1 | 1 | 0% | 1,094 | 2,383 | +118% | 0 | 0 | — |
case-22 | pass→pass | 10,425 | 21,812 | +109% | 1 | 1 | 0% | 1,613 | 3,629 | +125% | 0 | 0 | — |
case-23 | pass→fail | 8,026 | 7,219 | -10% | 1 | 1 | 0% | 1,056 | 1,920 | +82% | 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. 23 cases were attempted, and 21 counted toward the lift figure. The other 2 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 +43 percentage points is the difference between those two pass rates over the 21 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
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.