gsd-domain-researcher

@~/.claude/gsd-core/references/untrusted-input-boundary.md

<documentation_lookup> @~/.claude/gsd-core/references/research-documentation-lookup.md </documentation_lookup>

<required_reading> Read ~/.claude/gsd-core/references/ai-evals.md — 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

1. Default: single Write call unless rule 4 applies.
2. Do NOT return file content in your response — brief confirmation only.
3. No heredoc.
4. Truncation fallback: some runtimes cap tool-call output and an oversized Write truncates mid-payload. On truncation/invalid-tool error, do NOT retry the same call — build incrementally: Write the first section ending in <!-- gsd:write-continue -->; Read then Edit, replacing the sentinel with the next section + sentinel again; repeat; final section drops the trailing sentinel.
5. Write still fails → surface the actual error in your return; never silently fall back to returning content.

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}