Borrowing it
Nothing to install: this file belongs to hermes-labs-ai/hermeneutic. 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/hermes-labs-ai/hermeneutic/main/AGENTS.mdgit clone --depth 1 https://github.com/hermes-labs-ai/hermeneuticWrote 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/instructions/hermes-labs-ai/hermeneutic/agents-md)<a href="https://agentmods.dev/instructions/hermes-labs-ai/hermeneutic/agents-md"><img src="https://agentmods.dev/badge/instructions/hermes-labs-ai/hermeneutic/agents-md/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/instructions/hermes-labs-ai/hermeneutic/agents-md"><img src="https://agentmods.dev/badge/instructions/hermes-labs-ai/hermeneutic/agents-md.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 | $0.00740 | $0.00740 |
| Opus 5 | $0.00370 | $0.00370 |
| Sonnet 5 | $0.00148 | $0.00148 |
| Haiku 4.5 | $0.00074 | $0.00074 |
Grade A, and why
hermeneutic AGENTS.md 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 9d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — using hermeneutic from a coding agent
This file is for AI coding agents and host applications that want to call the
standalone hermeneutic CLI or Python library.
What this tool does
A fixed deterministic English drift check for assistant-generated drafts. It flags eight surface shapes including completion overclaiming, relayed authority, unhedged certainty, scope expansion, and unsupported quality adjectives. It does not read the personal correction corpus or run the optional Router.
When to invoke it
Run hermeneutic gate on any assistant draft before you send it to the user
when:
- The draft contains numeric claims (file counts, test counts, percentages, ms).
- The draft summarizes work completed by a subagent.
- The draft uses completion language ("done", "shipped", "all green").
- The draft contains universal quantifiers ("every", "all", "always").
- You want a cheap second-opinion gate that doesn't require an LLM call.
Inspect the printed verdict as well as the exit code: low-severity RISK is
advisory and exits 0; medium/high RISK exits 1. A caller that chooses to hold a
draft should either:
- Add the missing evidence (run the verification commands, paste the output).
- Hedge the claim ("appears to" / "based on N samples").
- Cut the unverifiable text.
Programmatic use
from hermeneutic import Router, PressureProbe
probe = PressureProbe(judge=your_llm_call) # callable: prompt -> str
def repair(request, draft, reason):
return your_llm_call(f"Revise: {reason}\n\n{draft}")
router = Router(probe=probe, repairer=repair)
result = router.gate(request=user_request, draft=your_draft)
if result.repaired:
log(f"hermeneutic caught drift: {result.summary()}")
final = result.final_output
Calibration
PressureProbe ships with a generic "rigorous-skeptic" calibration. To make it
match your team's standards, pass your own calibration text:
my_calibration = """
You are reviewing drafts for a security-critical product.
Bias toward HOLD when:
- Any security claim lacks a CVE or vendor-confirmed citation.
- Code changes touch authentication or crypto without a test diff.
"""
probe = PressureProbe(judge=your_llm_call, calibration=my_calibration)
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.
- 9d ago First seen · 85 lines · 740 tokens per session scan A 3eaccda84f2c
hermeneutic AGENTS.md is an instructions file published in the GitHub repository hermes-labs-ai/hermeneutic (6 stars, last pushed 6d ago), licensed Apache-2.0. It adds 740 tokens to every session, about $0.0037 per session on Opus 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 2026-08-31.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.