Borrowing it
Nothing to install: this file belongs to timothywarner-org/claude-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/timothywarner-org/claude-code/main/.claude/skills/genai-prompt-eval/SKILL.mdgit clone --depth 1 https://github.com/timothywarner-org/claude-codeWrote 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/skills/timothywarner-org/claude-code/genai-prompt-eval)<a href="https://agentmods.dev/skills/timothywarner-org/claude-code/genai-prompt-eval"><img src="https://agentmods.dev/badge/skills/timothywarner-org/claude-code/genai-prompt-eval/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/skills/timothywarner-org/claude-code/genai-prompt-eval"><img src="https://agentmods.dev/badge/skills/timothywarner-org/claude-code/genai-prompt-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.00631 |
| Opus 5.5 | $0.00041 | $0.00252 |
| Sonnet 5.5 | $0.00021 | $0.00126 |
| Haiku 4.5 | $0.00010 | $0.00063 |
Grade A, and why
genai-prompt-eval 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 2mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Score GenAI outputs before shipping
This skill measures whether a generative-AI feature produces answers that are grounded, relevant, coherent, and safe. It runs a set of eval cases through the model, scores each output on those four dimensions, and reports pass or fail against thresholds. Pair it with the azure-ai-deploy skill: evals are Gate 1 of that deploy checklist.
When to reach for this
- A GenAI feature is changing and you need a regression signal on answer quality.
- A deploy gate requires proof that outputs meet a quality bar.
- You want a repeatable eval corpus that reflects real enterprise questions, not toy prompts.
Workflow
1. Read the dimensions
Read resources/references/EVAL-DIMENSIONS.md. It defines groundedness, relevance, coherence, and safety, states what each one measures, and gives a pass signal for each.
2. Build the eval corpus
Start from resources/templates/eval_cases.jsonl. Each line is one case: an input prompt, optional context the answer must stay grounded to, and expected_criteria describing a passing answer. Add cases that mirror the questions real users send.
3. Run the harness
uv run python ${CLAUDE_SKILL_DIR}/resources/scripts/run_eval.py \
--cases ${CLAUDE_SKILL_DIR}/resources/templates/eval_cases.jsonl \
--threshold 0.8
The script loads the cases, calls the model for each, scores the output on the four dimensions, prints a per-case and aggregate report, and exits non-zero when the aggregate score falls below the threshold. That non-zero exit fails a CI or deploy step.
4. Read the report and act
- Cases below threshold name the failing dimension. Fix the prompt, the retrieval context, or the guardrail, then re-run.
- Record the aggregate score as the new baseline so the next run detects regressions.
Conventions
- uv manages Python, not pip. Run scripts with
uv run. - No hardcoded secrets. The scoring model client reads its endpoint and deployment from env vars.
- Realistic cases only. Eval inputs are enterprise scenarios, never placeholder prompts.
- Deterministic scoring where possible. Prefer a low temperature on any model-graded dimension so scores are stable across runs.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 48 lines · 103 tokens per session scan A d12cf865df8a
genai-prompt-eval is a skill published in the GitHub repository timothywarner-org/claude-code (226 stars, last pushed 3mo ago), licensed MIT. It adds 103 tokens to every session and 631 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
genai-prompt-eval compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| genai-prompt-evaltimothywarner-org/claude-code | Skill | 226 | 3mo ago | MIT | A | 103 | Claude Code |
| cli-evaldiegosouzapw/OmniRoute | Skill | 83k | 5d ago | MIT | A | 34 | Claude Code, Codex |
| emerging-techniques-model-mergingdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 77 | Claude Code, Codex |
| darwinian-evolverNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | B | 22 | Claude Code, Codex |
| dummy-datasetphuryn/pm-skills | Skill | 28k | 8d ago | MIT | A | 48 | Claude Code |
| validatesemantica-agi/semantica | Skill | 14k | yesterday | MIT | A | 0 | Claude Code |
| agent-evalssickn33/agentic-awesome-skills | Skill | 49k | yesterday | MIT | B | 38 | Claude Code |