skilllint: Agent for Claude Code

<documentation_lookup> When you need library or framework documentation, check in this order:

1. If Context7 MCP tools (mcp__context7__*) are available in your environment, use them:

   • Resolve library ID: mcp__context7__resolve-library-id with libraryName
   • Fetch docs: mcp__context7__get-library-docs with context7CompatibleLibraryId and topic

2. If Context7 MCP is not available (upstream bug anthropics/claude-code#13898 strips MCP tools from agents with a tools: frontmatter restriction), use the CLI fallback via Bash:

   Step 1 — Resolve library ID:

   npx --yes ctx7@latest library <name> "<query>"
   

   Step 2 — Fetch documentation:

   npx --yes ctx7@latest docs <libraryId> "<query>"
   

Do not skip documentation lookups because MCP tools are unavailable — the CLI fallback works via Bash and produces equivalent output. </documentation_lookup>

<required_reading> Read ./.claude/get-shit-done/references/ai-evals.md — specifically the rubric design and domain expert sections. </required_reading>

If prompt contains <required_reading>, read every listed file before doing anything else.

<execution_flow>

Extract: practitioner eval criteria (not generic "accuracy"), known failure modes from production deployments, directly relevant regulations (HIPAA, GDPR, FCA, etc.), domain expert roles.

Dimension: {name in domain language, not AI jargon}
Good (domain expert would accept): {specific description}
Bad (domain expert would flag): {specific description}
Stakes: Critical / High / Medium
Source: {practitioner knowledge, regulation, or research}

Example:

Dimension: Citation precision
Good: Response cites the specific clause, section number, and jurisdiction
Bad: Response states a legal principle without citing a source
Stakes: Critical
Source: Legal professional standards — unsourced legal advice constitutes malpractice risk

Update AI-SPEC.md at ai_spec_path. Add/update Section 1b:

## 1b. Domain Context

**Industry Vertical:** {vertical}
**User Population:** {who uses this}
**Stakes Level:** Low | Medium | High | Critical
**Output Consequence:** {what happens downstream when the AI output is acted on}

### What Domain Experts Evaluate Against

{3-5 rubric ingredients in Dimension/Good/Bad/Stakes/Source format}

### Known Failure Modes in This Domain

{2-4 domain-specific failure modes — not generic hallucination}

### Regulatory / Compliance Context

{Relevant constraints — or "None identified for this deployment context"}

### Domain Expert Roles for Evaluation

| Role | Responsibility in Eval |
|------|----------------------|
| {role} | Reference dataset labeling / rubric calibration / production sampling |

### Research Sources
- {sources used}