Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — 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.
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Systematically evaluate the SDLC Layer Separation Architecture implementation and support iterative improvement. Treats the implementation as first-pass until validated.
--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.Run each check and record PASS / FAIL / SKIP with evidence.
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.mdplugins/development-harness/CLAUDE.md — existsstateless-agent-methodology/research/arl/PROVENANCE.md — exists (sibling repo or configured path)TASK_FILE_FORMAT.md — exists at plugins/development-harness/docs/TASK_FILE_FORMAT.mdrole-resolution-protocol.md — exists in development-harnessartifact-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).
Evidence: Glob or Read results for each expected file.
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"--layer is used (expected)Evidence: Paste command output for each.
evaluation-testing/harness-engineering-openai.md — has layer: "0"developer-tools/copier-astral.md — has layer: "1"research/README.md — has "Layer Mapping" sectionEvidence: Grep for layer: in frontmatter of each.
work-backlog-item SKILL, groom subcommand (references/workflows/groom/start.md) — documents ARL human-probing integration; references arl-human-probing-designrole-resolution-protocol.md — has "Layer 0 gates apply before role resolution"plugins/development-harness/CLAUDE.md — references layer modelEvidence: Grep or Read for key phrases.
Evidence: List any plan items not yet implemented or diverged.
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}
After evaluation:
--fix: Apply safe fixes (broken paths, missing frontmatter fields, obvious typos). Report each change.--fix: Present findings; offer to create backlog items or apply fixes.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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Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.
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SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into an executable SAM plan.
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Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
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Browser automation for AI agents using the agent-browser CLI and Playwright. Use when navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, logging into sites, or automating any browser task. Triggers on "open a website", "fill out a form", "click a button", "scrape data", "test this web app", "automate browser actions", or any programmatic web interaction request.
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Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.
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Create or adapt Claude Code agent definitions. Use when creating an agent, changing subagent configuration, selecting agent scope, or designing a specialized delegation role.
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