Agent-Skills.md

Agent Skills: Evaluate SDLC Layers

Validate and iterate on the SDLC Layer Separation Architecture implementation across 6 check categories — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

UncategorizedID: Jamie-BitFlight/claude_skills/evaluate-sdlc-layers

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Jamie-BitFlight/claude_skills
Jamie-BitFlightLicense: MIT
6110

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pnpm dlx add-skill https://github.com/Jamie-BitFlight/claude_skills/tree/HEAD/.claude/skills/evaluate-sdlc-layers

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Skill Metadata

Name
evaluate-sdlc-layers
Description
Validate and iterate on the SDLC Layer Separation Architecture implementation across 6 check categories — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

Evaluate SDLC Layers

Systematically evaluate the SDLC Layer Separation Architecture implementation and support iterative improvement. Treats the implementation as first-pass until validated.

Arguments

  • --dry-run — Run all checks, produce report only. Do not apply fixes.
  • --fix — After evaluation, apply safe fixes for broken references, missing metadata, or obvious gaps. Report what was changed.
  • (no args) — Evaluate and produce report; offer to fix or delegate fixes.

Evaluation Checklist

Run each check and record PASS / FAIL / SKIP with evidence.

1. Cross-Reference Validation

For each linked path in plugins/development-harness/docs/sdlc-layers/ and related docs:

  • [ ] sam-definition.md — exists at plugins/development-harness/skills/work-backlog-item/references/sam-definition.md
  • [ ] plugins/development-harness/CLAUDE.md — exists
  • [ ] stateless-agent-methodology/research/arl/PROVENANCE.md — exists (sibling repo or configured path)
  • [ ] Layer 0 docs → TASK_FILE_FORMAT.md — exists at plugins/development-harness/docs/TASK_FILE_FORMAT.md
  • [ ] Layer 1 → language-manifest-schema.md, role-resolution-protocol.md — exist in development-harness
  • [ ] Layer 2 → plugins/development-harness/docs/sdlc-layers/layer-2/ — exists with README, schema, pilot profiles
  • [ ] Layer-0 redirect stubs — 4 files (artifact-conventions.md, task-file-format.md, sam-pipeline.md, arl-touchpoints.md) contain redirect pointers to canonical locations. Validate each redirect target exists.

Evidence: List each path checked and result (exists / 404 / wrong content).

2. Doc Completeness

  • [ ] Layer 0 content files (6): README, rt-ica-gate, verification-protocol, evidence-discipline, orchestrator-discipline, context-fit-complexity
  • [ ] Layer 0 redirect stubs (4): sam-pipeline, arl-touchpoints, artifact-conventions, task-file-format — each must contain a redirect pointing to its canonical skill reference location
  • [ ] Layer 1: All 6 docs present (README, layer-1-overview, language-manifest-template, linting-discovery-protocol, workflow-pattern-taxonomy, harness-role-mapping)
  • [ ] Layer 2: README, layer-2-overview, stack-profile-schema, stack-profile-template; pilot profiles python-fastapi, python-cli
  • [ ] ARL: arl-meta-layer.md, arl-human-probing-design.md

Evidence: Glob or Read results for each expected file.

3. Knowledge-Explorer Layer Filter

  • [ ] uv run research/knowledge-explorer.py list --layer 0 — returns entries with layer: "0"
  • [ ] uv run research/knowledge-explorer.py list --layer 1 — returns entries with layer: "1"
  • [ ] uv run research/knowledge-explorer.py list --layer 2 — returns entries with layer: "2"
  • [ ] Entries without layer metadata are excluded when --layer is used (expected)

Evidence: Paste command output for each.

4. Research Entry Layer Metadata

  • [ ] evaluation-testing/harness-engineering-openai.md — has layer: "0"
  • [ ] api-frameworks/fastapi.md, api-frameworks/tornado.md — have layer: "2", language, stack
  • [ ] developer-tools/copier-astral.md — has layer: "1" (or 2 if stack-scaffold)
  • [ ] research/README.md — has "Layer Mapping" section

Evidence: Grep for layer: in frontmatter of each.

5. Integration Points

  • [ ] work-backlog-item SKILL — documents --language, --stack; references layer docs
  • [ ] groom-backlog-item SKILL — documents ARL human-probing integration; references arl-human-probing-design
  • [ ] language-manifest-schema.md — has "Inherits from Layer 0"; typecheck: (none); Conventions schema
  • [ ] role-resolution-protocol.md — has "Layer 0 gates apply before role resolution"
  • [ ] plugins/development-harness/CLAUDE.md — references layer model

Evidence: Grep or Read for key phrases.

6. Consistency with Plan

  • [ ] Plan deliverables (from attached plan) — compare File and Directory Changes table to actual files
  • [ ] Dependency order — Layer 0 → Layer 1 → Layer 2 → Research → SAM/ARL → ARL probing → work-backlog-item

Evidence: List any plan items not yet implemented or diverged.

Output Format

Produce a structured report:

## SDLC Layer Evaluation Report
Date: {YYYY-MM-DD}

### Summary
- Cross-Reference: {PASS|FAIL|PARTIAL} — {brief}
- Doc Completeness: {PASS|FAIL|PARTIAL}
- Knowledge-Explorer: {PASS|FAIL|PARTIAL}
- Research Metadata: {PASS|FAIL|PARTIAL}
- Integration Points: {PASS|FAIL|PARTIAL}
- Plan Consistency: {PASS|FAIL|PARTIAL}

### Findings
1. [Category] {finding} — {suggested fix}
2. ...

### Recommended Actions
- [ ] {action 1}
- [ ] {action 2}

Iteration

After evaluation:

  1. If --fix: Apply safe fixes (broken paths, missing frontmatter fields, obvious typos). Report each change.
  2. If no --fix: Present findings; offer to create backlog items or apply fixes.
  3. Re-run: After fixes, re-run evaluation to confirm improvements.

Experiments

Flow experiments and learnings live in sam-flow-experiments. Clone via SSH: git clone git@github.com:Jamie-BitFlight/sam-flow-experiments.git. When iterating, consider running experiments against concept fixtures to validate changes.

References