Borrowing it
Nothing to install: this file belongs to NiuTrans/ToFu. 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/NiuTrans/ToFu/main/AGENTS.mdgit clone --depth 1 https://github.com/NiuTrans/ToFuWrote 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/niutrans/tofu/agents-md)<a href="https://agentmods.dev/instructions/niutrans/tofu/agents-md"><img src="https://agentmods.dev/badge/instructions/niutrans/tofu/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/niutrans/tofu/agents-md"><img src="https://agentmods.dev/badge/instructions/niutrans/tofu/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 | $0.01604 | $0.01604 |
| Opus 5 | $0.00802 | $0.00802 |
| Sonnet 5 | $0.00321 | $0.00321 |
| Haiku 4.5 | $0.00160 | $0.00160 |
Grade A, and why
ToFu 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 yesterday.
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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex repository guidance
Keep task quality as the primary objective, then minimize model turns and raw context growth without weakening verification.
- Start unfamiliar work with one bounded discovery batch: use
rg/rg --filesand targeted ranges instead of repeatedly reading one file at a time. Do not dump the whole dirty worktree, full diffs, generated bundles, or large logs. - Use
docs/README.mdas the first-hop map. Edit retained browser code infrontend/src/runtime/sections/, never the generatedapp-runtime.js. Edit styles infrontend/src/styles/{application,settings}/, never generated files understatic/. - In code mode, put independent read-only inspections in one
functions.execprogram and run safe independent commands concurrently. Reduce intermediate output inside that program; return only evidence needed for the next judgment. - For commands expected to take 10–60 seconds, start the
functions.execsource with// @exec: {"yield_time_ms": 30000}and give the nested command a similar yield. If it still returns a session/cell, poll at 30–60 second intervals; never busy-poll. Keep user progress updates within 60 seconds. - Use a test ladder: smallest relevant tests first, then neighboring contracts, then broad gates once the worktree is stable. Do not rerun an unchanged suite. With concurrent writers, report that a full-suite result is a moving target.
- Batch mechanical edits, but inspect and test semantic boundaries separately. Preserve fault injection, rollback, authority, and user-visible error behavior.
- Before finishing a tool-heavy task, run
python3 audit_codex_session.py <rollout.jsonl>when a session path is available. Treat fewer calls as a win only when the same correctness checks and required evidence still pass.
Debugging with a conversation ID
When the user pastes a conversation ID (e.g. mt18xr3wfs0rbq, copied via the
sidebar copy-ID button / any data-conv-id attribute), the FIRST step is:
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.
- yesterday First seen · 125 lines · 1,604 tokens per session scan A bf8eb26216c8
ToFu AGENTS.md is an instructions file published in the GitHub repository NiuTrans/ToFu (144 stars, last pushed 2d ago), licensed MIT. It adds 1,604 tokens to every session, about $0.0080 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 2026-09-07.
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.