llms.txt git:20260606.7277088A

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# Owner: Hermes Labs - https://hermes-labs.ai

# rule-audit

> Static analyzer for LLM system prompts. Parses a prompt into normative rules and reports logical contradictions, coverage gaps, priority ambiguities, meta-rule paradoxes, and absolute-rule edge cases. Pure Python, zero LLM dependency, deterministic. Part of the Hermes Labs reliability stack.

rule-audit is, roughly, a linter for AI system prompts. Given a raw prompt string, it extracts normative rules, classifies their modality (MUST / MUST_NOT / SHOULD / MAY), scores absoluteness, and finds pairs that conflict. For each finding it renders a concrete example scenario with a suggested attack vector and mitigation — illustrative starting points for testing, not verified exploits. No API keys, no network, no LLM calls.

Install: `pip install rule-audit`. Python 3.9+. MIT licensed.

## Docs

- [README](README.md): Install, quickstart, CLI and Python API usage, architecture overview.
- [SPEC](SPEC.md): Full technical spec — parsing algorithm, contradiction detection methodology, severity calibration, data model, performance characteristics, known limitations.
- [ROADMAP](ROADMAP.md): planned work — see the file for current status.
- [CLAUDE](CLAUDE.md): Project conventions for AI agents editing this repo.
- [AGENTS](AGENTS.md): Public API surface, extension points (`_KEYWORD_CLUSTERS`, detector functions), conventions, don'ts.
- [CHANGELOG](CHANGELOG.md): Release history.
- [CONTRIBUTING](CONTRIBUTING.md): How to add detectors, clusters, severity rules. Tests required.

## Examples

- [samples/basic_assistant.txt](samples/basic_assistant.txt): Minimal helpful-assistant prompt with built-in contradictions.
- [samples/code_assistant.txt](samples/code_assistant.txt): Code-assistant system prompt with permission/refusal conflicts.
- [samples/content_moderator.txt](samples/content_moderator.txt): Moderation prompt with absolute-vs-nuanced tensions.
- [samples/customer_support.txt](samples/customer_support.txt): Support prompt with policy-vs-empathy ambiguity.
- [samples/enterprise_rag.txt](samples/enterprise_rag.txt): RAG-based enterprise prompt with retrieval-scope conflicts.
- [benchmarks/README](benchmarks/README.md): Per-sample finding counts and how to reproduce them; the regression gate lives in `tests/test_benchmark.py`.

## Related

- [hermes-jailbench](https://github.com/hermes-labs-ai/hermes-jailbench): Dynamic jailbreak regression suite — complements rule-audit's static analysis with live-endpoint testing.
- [colony-probe](https://github.com/hermes-labs-ai/colony-probe): Prompt-confidentiality audit (system-prompt reconstruction via multi-turn probing).
- [lintlang](https://github.com/roli-lpci/lintlang): Static linter for agent configs and tool descriptions — complementary to rule-audit (structure vs logical content).
- [Hermes Labs](https://hermes-labs.ai): AI reliability tooling.

## Optional

- [LICENSE](LICENSE): MIT.
- [SECURITY](SECURITY.md): Responsible disclosure — security@hermes-labs.ai.
- [CITATION](CITATION.cff): How to cite.


## About Hermes Labs

Hermes Labs is an independent AI-reliability lab building open-source tools that catch silent failure modes in production AI. More at [hermes-labs.ai](https://hermes-labs.ai).

Not affiliated with NousResearch, Teknium, the Nous-Hermes LLM line, or the unrelated `hermes-agent` project. Different companies, different work.