Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/megamen32/LastHumanCommitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/megamen32/lasthumancommit/gsd-domain-researcher)<a href="https://agentmods.dev/agents/megamen32/lasthumancommit/gsd-domain-researcher"><img src="https://agentmods.dev/badge/agents/megamen32/lasthumancommit/gsd-domain-researcher/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/megamen32/lasthumancommit/gsd-domain-researcher"><img src="https://agentmods.dev/badge/agents/megamen32/lasthumancommit/gsd-domain-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.01454 |
| Opus 5 | $0.00039 | $0.00727 |
| Sonnet 5 | $0.00015 | $0.00291 |
| Haiku 4.5 | $0.00008 | $0.00145 |
Grade A, and why
gsd-domain-researcher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to gsd-domain-researcher — 21 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<codex_agent_role> role: gsd-domain-researcher tools: Read, Write, Bash, Grep, Glob, WebSearch, WebFetch, mcp__context7__* purpose: Researches the business domain and real-world application context of the AI system being built. Surfaces domain expert evaluation criteria, industry-specific failure modes, regulatory context, and what "good" looks like for practitioners in this field — before the eval-planner turns it into measurable rubrics. Spawned by $gsd-ai-integration-phase orchestrator. </codex_agent_role>
<documentation_lookup> When you need library or framework documentation, check in this order:
-
If Context7 MCP tools (
mcp__context7__*) are available in your environment, use them:- Resolve library ID:
mcp__context7__resolve-library-idwithlibraryName - Fetch docs:
mcp__context7__get-library-docswithcontext7CompatibleLibraryIdandtopic
- Resolve library ID:
-
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 /tmp/gsd-npm-codex-stage/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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 153 lines · 77 tokens per session scan A ea504bc03e7a
gsd-domain-researcher is an agent published in the GitHub repository megamen32/LastHumanCommit (2 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 1,454 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to gsd-domain-researcher, differing in 21 lines, and is treated as a copy.
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