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.
npx agentmods add instructions/amenti-labs/hermes-lab/agents-mdgit clone --depth 1 https://github.com/amenti-labs/hermes-labWrote 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/amenti-labs/hermes-lab/agents-md)<a href="https://agentmods.dev/instructions/amenti-labs/hermes-lab/agents-md"><img src="https://agentmods.dev/badge/instructions/amenti-labs/hermes-lab/agents-md.svg" alt="Measured on agentmods" 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 | $0.00510 | $0.00510 |
| Opus 5 | $0.00255 | $0.00255 |
| Sonnet 5 | $0.00102 | $0.00102 |
| Haiku 4.5 | $0.00051 | $0.00051 |
Grade A, and why
hermes-lab 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 5d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hermes Lab agent instructions
Root-folder harness contract for AI coding and research agents
What this repo is
A generic autoresearch lab scaffold. The repo holds code and templates. The data root holds canonical experiment state.
python3 scripts/labctl.pyis the control surface.LAB_MANIFEST.jsonis the machine-readable command and contract reference.README.mdhas the full user-facing docs (templates, search strategies, examples).
Data root vs workspace
- Data root (
$HERMES_LAB_DATA_ROOT, default./lab-data): Lab state -- experiments, runs, metrics, dispatch queue. - Workspace (
workspace_rootin SPEC): Your experiment code. Can be any path on disk.
Repo ingress
Read these in order when pointed at the repo root:
README.mdLAB_MANIFEST.jsondocs/AUTORESEARCH-COMPATIBILITY.mddocs/REFERENCE-EXECUTOR.mddocs/MULTI-FIDELITY.mddocs/LOCAL-AGENT-MUTATION.mddocs/DISPATCH.md
If a lab data root already exists, then read:
README-FIRST.mdLAB-STATUS.mdLAB-INDEX.jsonPROGRAM.mdexperiments/<id>/RUNBOOK.mdexperiments/<id>/SUMMARY.mdexperiments/<id>/NEXT.md
Write rules
- Treat the data root as canonical state.
- Only write inside a claimed run bundle when executing an experiment.
- If working from
dispatch/running/<dispatch-id>/, only write insiderun/. - Do not hand-edit derived files.
RESULT.mdis the write-ahead artifact.- Use the runbook and spec to determine mutable vs read-only surfaces.
Dispatch protocol
For agent-managed execution, use the dispatch protocol instead of writing directly into experiments/<id>/runs/. The queue package includes dispatch.json, an input/ snapshot, and a writable run/ folder that is ingested into canonical history only after completion.
If mutation_command is omitted and agent_provider is set, the lab synthesizes a local mutation worker through scripts/local_agent_mutation.py.
See docs/DISPATCH.md for the full protocol.
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.
- 5d ago First seen · 58 lines · 510 tokens per session scan A dc92d29fca6c
hermes-lab AGENTS.md is an instructions file published in the GitHub repository amenti-labs/hermes-lab (5 stars, last pushed 5mo ago), licensed MIT. It adds 510 tokens to every session, about $0.0026 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
bio-gene-to-reference-tree copilot-instructions.md
Copilot instructions for Hongda-Zhao/bio-gene-to-reference-tree, a project described as: Auditable agent skill for resolving protein queries, selecting references, and planning reproducible phylogenetic trees.
braina GEMINI.md
Instructions for brainets/braina, covering project: braina (brain interaction analysis), 1. project context & purpose, 2. commands, verify environment (all core dependencies) and run the verification test suite for frites + hoi.
research-automation CLAUDE.md
Instructions for lucafusarbassini/research-automation, covering ricet - research automation framework, project overview, claude-flow mcp, workflow habits and file organization.
shannon-prover CLAUDE.md
Claude Code instructions for SkyShannonProver/shannon-prover, covering shannon prover: claude entry point, current boundary, easycrypt environment, eval safety and current documentation.
SciCrucible CLAUDE.md
Claude Code instructions for Xinyang-Li666/SciCrucible, covering 科学知识库, 知识库结构, 可用命令, 项目目录 and 工作原则.
torchcell CLAUDE.md
Claude Code instructions for Mjvolk3/torchcell, covering vision -- the virtual cell (north star), for local configs, git worktrees, programming guide and provenance & reproducibility.