Evaluate Sdlc Layers

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.

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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).

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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.

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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.

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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.

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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.

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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.

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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}

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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.

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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.

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References

• [SDLC Layers](../../../plugins/development-harness/docs/sdlc-layers/)
• [verify-done](../../../plugins/development-harness/skills/verify-done/SKILL.md) — evidence discipline
• [groom-backlog-item](../../../plugins/development-harness/skills/groom-backlog-item/SKILL.md) — orchestration pattern

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Jamie-BitFlight
• Source: Jamie-BitFlight/claude_skills
• License: MIT

Install and usage instructions live in the source repository linked above.