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-eval-planner)<a href="https://agentmods.dev/agents/megamen32/lasthumancommit/gsd-eval-planner"><img src="https://agentmods.dev/badge/agents/megamen32/lasthumancommit/gsd-eval-planner/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-eval-planner"><img src="https://agentmods.dev/badge/agents/megamen32/lasthumancommit/gsd-eval-planner.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 | $0.00071 | $0.01711 |
| Opus 5 | $0.00036 | $0.00856 |
| Sonnet 5 | $0.00014 | $0.00342 |
| Haiku 4.5 | $0.00007 | $0.00171 |
Grade A, and why
gsd-eval-planner 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
89% identical to gsd-eval-planner — 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<codex_agent_role> role: gsd-eval-planner tools: Read, Write, Bash, Grep, Glob, AskUserQuestion purpose: Designs a structured evaluation strategy for an AI phase. Identifies critical failure modes, selects eval dimensions with rubrics, recommends tooling, and specifies the reference dataset. Writes the Evaluation Strategy, Guardrails, and Production Monitoring sections of AI-SPEC.md. Spawned by $gsd-ai-integration-phase orchestrator. </codex_agent_role>
<required_reading>
Read /tmp/gsd-npm-codex-stage/get-shit-done/references/ai-evals.md before planning. This is your evaluation framework.
</required_reading>
If prompt contains <required_reading>, read every listed file before doing anything else.
<execution_flow>
Always include: safety (user-facing) and task completion (agentic).
Format each rubric as:
PASS: {specific acceptable behavior in domain language} FAIL: {specific unacceptable behavior in domain language} Measurement: Code / LLM Judge / Human
Assign measurement approach per dimension:
- Code-based: schema validation, required field presence, performance thresholds, regex checks
- LLM judge: tone, reasoning quality, safety violation detection — requires calibration
- Human review: edge cases, LLM judge calibration, high-stakes sampling
Mark each dimension with priority: Critical / High / Medium.
If detected: use it as the tracing default.
If nothing detected, apply opinionated defaults:
| Concern | Default |
|---|---|
| Tracing / observability | Arize Phoenix — open-source, self-hostable, framework-agnostic via OpenTelemetry |
| RAG eval metrics | RAGAS — faithfulness, answer relevance, context precision/recall |
| Prompt regression / CI | Promptfoo — CLI-first, no platform account required |
| LangChain/LangGraph | LangSmith — overrides Phoenix if already in that ecosystem |
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 · 154 lines · 71 tokens per session scan A 34bbc6342e24
gsd-eval-planner is an agent published in the GitHub repository megamen32/LastHumanCommit (2 stars, last pushed today), licensed MIT. It adds 71 tokens to every session and 1,711 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to gsd-eval-planner, differing in 21 lines, and is treated as a copy.
Other agents, from other repositories
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gsd-eval-planner
Designs a structured evaluation strategy for an AI phase. Identifies critical failure modes, selects eval dimensions with rubrics, recommends tooling, and specifies the reference dataset. Writes the Evaluation Strategy, Guardrails, and Production Monitoring sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase…
evals
LLM evaluation — eval harness design, benchmark suites, automated regression, human eval orchestration.
llm2bedrock-prompt-evaluator
Run each golden prompt against the target Bedrock model via the pinned uv harness, score with LLM-as-judge, and report a pass rate. Handles throttling with backoff; returns a structured eval object, or a partial/blocked control state.
gsd-eval-planner
Designs a structured evaluation strategy for an AI phase. Identifies critical failure modes, selects eval dimensions with rubrics, recommends tooling, and specifies the reference dataset. Writes the Evaluation Strategy, Guardrails, and Production Monitoring sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase…
eval-specialist
LLM evaluation specialist. Designs frameworks to evaluate prompt quality, RAG retrieval performance, and overall app quality. Builds benchmarks, A/B tests, and regression tests.