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/celinval/snapcrab/agents-mdgit clone --depth 1 https://github.com/celinval/snapcrabWhat 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 | $0.01627 | $0.01627 |
| Opus 5 | $0.00813 | $0.00813 |
| Sonnet 5 | $0.00325 | $0.00325 |
| Haiku 4.5 | $0.00163 | $0.00163 |
Grade A, and why
snapcrab 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
General guidelines
This document captures code conventions for the snapcrab project. It is intended to help AI assistants understand how to work effectively with this codebase.
Inspired by nextest's AGENTS.md.
For humans
We welcome LLM-assisted contributions that abide by the following principles:
- Aim for excellence. Use LLMs as a quality multiplier. Invest the time savings in improving rigor beyond what you'd do alone. Refactor for clarity. Tackle the tedious parts. Aim for zero bugs.
- Review like a mentor. Treat LLM output as you would code from someone you're mentoring. Read every line, question design decisions, and find ways to break it.
- Write tests. Use LLMs to produce thorough tests — edge cases, boundary conditions, failure modes. Tests are where AI assistance pays off the most.
- Improve docs and errors. Use LLMs to write clear documentation and actionable error messages. Users should never be left wondering what went wrong or what to do next.
- Your code is your responsibility. Do not submit a first draft. If your PR shows signs of not being reviewed, we may decline it outright.
For LLMs
Correctness over convenience
- Model the full error space. No shortcuts or simplified error handling.
- Handle edge cases, including platform differences and overflow conditions.
- Use the type system to encode correctness constraints.
- Prefer compile-time guarantees over runtime checks where possible.
Production-grade engineering
- Test comprehensively, including edge cases and boundary conditions.
- Pay attention to what test facilities already exist and reuse them.
- Getting the details right is really important.
Documentation
- Keep documentation concise. Provide relevant information without overwhelming the reader — don't repeat yourself.
- Doc comments (
///) must start with a brief one-line summary. This first line is the title — keep it short and descriptive. - Use inline comments to explain "why," not just "what."
- Don't add narrative comments in function bodies. Only comment what is non-obvious or needs a deeper "why" explanation.
- Consider pulling the code to a separate function if it becomes too complex.
- Module-level documentation (
//!) should explain purpose and responsibilities. - Always use periods at the end of code comments.
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 · 180 lines · 1,627 tokens per session scan A e67e4025a4da
snapcrab AGENTS.md is an instructions file published in the GitHub repository celinval/snapcrab (10 stars, last pushed 5d ago), licensed Apache-2.0. It adds 1,627 tokens to every session, about $0.0081 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
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).
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.
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.
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 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.