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/hatch3r/hatch3rWrote 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/rules/hatch3r/hatch3r/hatch3r-ai-evals)<a href="https://agentmods.dev/rules/hatch3r/hatch3r/hatch3r-ai-evals"><img src="https://agentmods.dev/badge/rules/hatch3r/hatch3r/hatch3r-ai-evals/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/rules/hatch3r/hatch3r/hatch3r-ai-evals"><img src="https://agentmods.dev/badge/rules/hatch3r/hatch3r/hatch3r-ai-evals.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 | $0.00000 | $0.03680 |
| Opus 5.5 | $0.00000 | $0.01472 |
| Sonnet 5.5 | $0.00000 | $0.00736 |
| Haiku 4.5 | $0.00000 | $0.00368 |
Grade A, and why
hatch3r-ai-evals 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 1mo 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Feature Evaluation and Cost Governance (2026)
Scope
This rule governs the BACKEND half of an LLM-driven feature: eval harness, prompt versioning, cost telemetry, prompt caching, model fallback, hallucination-as-SLI, tool-use evals, safety/red-team, and audit logging. The FRONTEND half (streaming UI, tool-call cards, human-approval gates, cancel/abort/undo, citations) is the paired companion rule rules/hatch3r-ai-ux-patterns.md. Apply both rules for any LLM-driven feature — UI-only or backend-only coverage is a regression.
Detection mirrors the companion rule: this rule activates when the project imports an LLM SDK (openai, @anthropic-ai/sdk, @google/generative-ai, cohere-ai, ai, @ai-sdk/*, langchain, llama-index) or contains files under ai/, llm/, chat/, assistant/, agents/, copilot/, evals/, prompts/, rag/.
Eval Harness Mandate
Every AI feature has an automated eval harness committed to the repo before the feature ships. Hand-rolled "ask the model and eyeball the answer" is a regression in 2026.
Pick one tool by task class:
- promptfoo — broad coverage, declarative YAML, model-comparison defaults
- DeepEval — pytest-style assertions for CI gate integration
- RAGAS — retrieval-augmented generation metrics (context_precision, context_recall, faithfulness, answer_relevance)
- Inspect — UK AISI framework for safety and agentic evals. At
scaleup/enterprisematurity (CONSTITUTION §6 Decision 16), use its external-agent runner (one harness drives Claude Code / Codex CLI / Gemini CLI) with bootstrap statistical scoring for multi-agent statistical-significance gating — point-estimate eval scores carry a confidence interval rather than a single number. - braintrust — SaaS + OSS hybrid, run history retained per prompt version
- TruLens — observability-coupled, runs evals against live traces
- Arize Phoenix — open-source observability with eval modules
Grader-owner neutrality. When the eval tool is owned by a model vendor — promptfoo has been OpenAI-owned since 2026-03-09 — prefer an independently-owned grader or a second-tool cross-check for cross-vendor model comparison; promptfoo remains OSS MIT, so this is a grading-independence input, not a reason to drop the tool.
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.
- 1mo ago First seen · 169 lines · 0 tokens per session scan A b0e6b3406eaa
hatch3r-ai-evals is a cursor rule published in the GitHub repository hatch3r/hatch3r (26 stars, last pushed 21d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,680 tokens. 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 2026-09-03.
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