Borrowing it
Nothing to install: this file belongs to ProjectDXAI/labrat. 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/ProjectDXAI/labrat/main/AGENTS.mdgit clone --depth 1 https://github.com/ProjectDXAI/labratWrote 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/projectdxai/labrat/agents-md)<a href="https://agentmods.dev/instructions/projectdxai/labrat/agents-md"><img src="https://agentmods.dev/badge/instructions/projectdxai/labrat/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/projectdxai/labrat/agents-md"><img src="https://agentmods.dev/badge/instructions/projectdxai/labrat/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 | $0.00661 | $0.00661 |
| Opus 5.5 | $0.00264 | $0.00264 |
| Sonnet 5.5 | $0.00132 | $0.00132 |
| Haiku 4.5 | $0.00066 | $0.00066 |
Grade A, and why
labrat 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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- labrat CLAUDE.md — 92% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
labrat repo
This is the repository root, not a runnable lab directory.
Use this file when working on labrat itself from Codex.
If Codex is opened inside a nested lab that has its own AGENTS.md, follow the lab-local file for runtime operation. This root file governs repo maintenance, scaffold consistency, releases, and docs.
Source Of Truth
- Product/setup docs:
README.md,program.md,docs/getting-started.md,docs/runners.md,docs/MODEL_GUIDANCE.md - Scaffolded lab UX:
templates/AGENTS.md,templates/CLAUDE.md,templates/.agents/skills/,templates/.claude/commands/,templates/agent_prompts/ - Canonical example lab:
examples/nlp-sentiment/research_lab/ - Packaging/versioning:
pyproject.toml,labrat/__init__.py,CHANGELOG.md
Interface Rules
- Keep Codex and Claude Code first-class. If a lab gets one operator surface, add the matching one for the other interface.
- Generated labs should ship
AGENTS.md,.agents/skills/,CLAUDE.md,.claude/commands/, andagent_prompts/. - Keep
AGENTS.mdshort enough to stay durable. Put longer repeatable Codex workflows in.agents/skills/. - Do not require a hidden local setup for basic operation. The runnable contract should live in the repo.
- When operator guidance changes, keep templates, the canonical example lab, and the docs in sync.
- Prefer
labrat ...examples in docs when working from the repo root. Keep the copiedscripts/*.pyflow valid inside labs.
Frontier Model Rules
- Use
docs/MODEL_GUIDANCE.mdfor model and prompt-process updates. - GPT-5.5 is a Codex host selection, not a lab runtime constant. Do not hardcode API model IDs into
labratfor Codex usage. - Prefer explicit completion and verification contracts over broad encouragement.
- Reserve high reasoning effort for design, audit, frame break, profile authoring, and release work.
- Keep root
AGENTS.mdfocused on repo maintenance; lab runtime details belong in lab-localAGENTS.mdand.agents/skills/labrat-operator/SKILL.md.
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 · 45 lines · 661 tokens per session scan A e8b73b5c7480
labrat AGENTS.md is an instructions file published in the GitHub repository ProjectDXAI/labrat (236 stars, last pushed 2mo ago), licensed MIT. It adds 661 tokens to every session, about $0.0026 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.
Other instructions, from other repositories
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).
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.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
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.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
vscode model-prompts.instructions.md
Model-specific prompt authoring and registry guidelines.