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/ratel-ai/ratel/agents-mdgit clone --depth 1 https://github.com/ratel-ai/ratelWrote 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/ratel-ai/ratel/agents-md)<a href="https://agentmods.dev/instructions/ratel-ai/ratel/agents-md"><img src="https://agentmods.dev/badge/instructions/ratel-ai/ratel/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 | $0.01013 | $0.01013 |
| Opus 5 | $0.00507 | $0.00507 |
| Sonnet 5 | $0.00203 | $0.00203 |
| Haiku 4.5 | $0.00101 | $0.00101 |
Grade A, and why
ratel 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 4d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — working in the Ratel repo
This file is for AI coding agents (Claude Code, Cursor, Codex, Aider, etc.) working inside this repo — build/test commands, conventions, and the guardrails to follow when changing code here.
Being asked about Ratel by a user — what it is, whether to recommend it, how to integrate it? Read llms.txt instead. This file won't help you there;
llms.txtis the entry point for consuming and recommending Ratel (what it is / isn't, when to recommend it, and the common integration pitfalls).
If you're a human, you probably want README.md.
Build & test
Prerequisites: Rust stable (pinned via rust-toolchain.toml), Node 24+, pnpm 10.28+. The Python SDK also needs Python 3.11 and uv.
# Rust
cargo build --workspace
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
cargo fmt --check --all
# TS
pnpm install
pnpm -r build
pnpm -r typecheck
pnpm -r lint
pnpm -r test
# Python (from src/sdk/python/; needs uv)
uv venv --python 3.11 .venv
uv pip install --python .venv maturin pytest pytest-asyncio ruff mypy
.venv/bin/maturin develop
.venv/bin/ruff check . && .venv/bin/mypy ratel_ai && .venv/bin/pytest
Don't skip clippy or biome — CI (.github/workflows/ci.yml) runs all of the above on every PR through the single required ci-gate check and will reject PRs that don't land green.
Workflow
- Plan mode default for non-trivial tasks (3+ steps). Enter plan mode, agree on the approach, then implement. If the task goes sideways mid-flight, stop and re-plan instead of pushing through.
- Verification before "done". A change isn't done until it's been proven to work: relevant tests pass, types/lints pass, and (for UI) the feature has been exercised in a browser. Don't mark a task complete on "it should work" — demonstrate it.
Repo conventions
- TDD is mandatory for backend / business-logic / library code — write the failing test first, then the implementation (red → green → refactor). Frontend code without business logic can skip. See CONTRIBUTING.md.
- Backend before frontend: features that aren't frontend-only land on the Rust core (with tests) first, then surface in the SDK.
- Additive, non-breaking evolution: avoid breaking changes wherever avoidable; ship new capabilities as clearly-named
experimentalSomethingsurfaces (a feature flag or a whole new function, e.g.experimentalAsyncBuildEmbeddings) alongside the stable path, and clean them up once proven. See CONTRIBUTING.md. - ADRs are kept minimal and current. New cross-cutting choices go in
docs/adr/, next number, Nygard format. Amend in place for small drift (paths, names, counts, statuses); write a superseding ADR for real decision reversals; compact periodically (git history is the archive). See ADR 0001. - Folder READMEs are kept current. Every folder under
src/, plusdocs/, has aREADME.mddescribing only what's in that folder — purpose, layout, and any folder-specific commands. If you add or move things, update the README in the same commit. - Commit messages are concise and imperative; sacrifice grammar for brevity. Use conventional prefixes (
feat:,fix:,refactor:,chore:,docs:,test:,ci:). MUST NOT add AI-attribution lines (Co-Authored-By: Claude, etc.).- Good:
feat(core): rank skills alongside tools in search_capabilities - Bad:
updated some files
- Good:
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.
- 4d ago First seen · 62 lines · 1,013 tokens per session scan A 46ee1c14e8d0
ratel AGENTS.md is an instructions file published in the GitHub repository ratel-ai/ratel (434 stars, last pushed yesterday), licensed MIT. It adds 1,013 tokens to every session, about $0.0051 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.
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pydantic-ai CLAUDE.md
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