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/huggingface/tau/agents-mdgit clone --depth 1 https://github.com/huggingface/tauWhat 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 | $0.00678 | $0.00678 |
| Opus 5 | $0.00339 | $0.00339 |
| Sonnet 5 | $0.00136 | $0.00136 |
| Haiku 4.5 | $0.00068 | $0.00068 |
Grade A, and why
tau 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 2d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tau Agent Instructions
Tau is a Python implementation of Pi's minimalist coding-agent harness architecture. The goal is to develop it incrementally, with each phase clearly documented and tested.
Project Roadmap
The implementation roadmap is tracked in GitHub issue #1:
Use that issue as the primary reference for phase ordering and architectural intent.
Architecture Principles
Preserve Pi's core separation of concerns:
AgentHarness = reusable agent brain
AgentSession = coding-agent environment
TUI = one possible frontend
Tau should be organized around these layers:
tau_ai provider/model streaming layer
tau_agent portable agent harness, loop, tools, events, sessions
tau_coding CLI app, resources, skills, extensions, commands, TUI integration
Keep the core agent package independent of CLI, Textual, Rich rendering, session file locations, and application-specific resource loading.
TUI Direction
Use Textual for the full interactive TUI, but only behind an adapter boundary. The agent harness should emit events; UI layers should consume those events.
Early phases should prioritize:
- print-mode CLI
- Rich renderers
- Textual interactive app
Do not let Textual become a dependency of the reusable agent harness.
Development Workflow
- Work in small, documented phases.
- Keep changes aligned with the roadmap issue.
- Add or update docs when introducing architectural concepts.
- Add tests for behavior before expanding features.
- Run tests and Python commands through
uv(for example,uv run pytestoruv run python ...) so they use the project environment. - Prefer simple, explicit abstractions over framework-heavy designs.
- Keep commits atomic: one coherent feature, fix, docs update, refactor, or cleanup per commit.
GitHub Issue and PR Formatting
- When creating or editing GitHub issues and pull requests from the CLI, write multiline Markdown bodies through a temporary file or heredoc and pass them with
--body-file. - Do not pass escaped newlines like
\ninside quoted--bodystrings; GitHub will render them literally instead of as line breaks. - Use Markdown headings, blank lines, bullets, and backticks for commands/paths so issue and PR descriptions are readable.
- After creating or editing a GitHub issue or PR body, verify the rendered source with
gh issue view ... --json bodyorgh pr view ... --json bodywhen practical.
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.
- 2d ago First seen · 82 lines · 678 tokens per session scan A 081b6d99eefa
tau AGENTS.md is an instructions file published in the GitHub repository huggingface/tau (2,579 stars, last pushed yesterday), licensed MIT. It adds 678 tokens to every session, about $0.0034 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 2026-08-30.
Other instructions, from other repositories
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
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codex AGENTS.md
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langchain AGENTS.md
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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).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.