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/ymm-oss/fsl/claude-mdgit clone --depth 1 https://github.com/ymm-oss/fslWrote 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/ymm-oss/fsl/claude-md)<a href="https://agentmods.dev/instructions/ymm-oss/fsl/claude-md"><img src="https://agentmods.dev/badge/instructions/ymm-oss/fsl/claude-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 | $0.00615 | $0.00615 |
| Opus 5 | $0.00308 | $0.00308 |
| Sonnet 5 | $0.00123 | $0.00123 |
| Haiku 4.5 | $0.00061 | $0.00061 |
Grade A, and why
fsl CLAUDE.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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@AGENTS.md
Claude Code instructions
Sources of truth
- The working tree and verified repository artifacts are current state; conversation history is not.
- The native Rust workspace is authoritative. Treat
src/fslc/as a frozen compatibility/LSP surface unless the requested outcome explicitly crosses that boundary. - Accepted decisions live in
docs/DESIGN-*.md. Current task state lives in.claude/work/active.mdwhen that local file exists. - Do not treat proposals, plans, auto memory, or an earlier session's claims as implemented behavior.
Starting or resuming work
- Inspect
git status --shortand the relevant implementation before editing. - If
.claude/work/active.mdexists, compare it with the working tree and report stale claims. - For a substantial task, use
/task-startto create or refresh the task packet before implementation. - State the requested outcome, affected authority surface, invariant, and narrow verification plan.
- Do not read unrelated directories without a concrete reason.
Implementation policy
- Prefer the smallest contract-preserving change. Do not add fallback behavior or compatibility work unless the requirement or an established public contract calls for it.
- For new language behavior, change the Rust implementation first. Change the frozen Python reference only when an accepted compatibility decision requires both implementations to move.
- Preserve the runtime/solver dependency boundary and symbolic/concrete/BFS agreement.
- Keep CLI stdout machine-readable where the command contract requires JSON; diagnostics and progress belong on the established channel.
- Never suppress an error, weaken a spec, hand-edit a generated snapshot, or expand an allowlist merely to make a check green.
Verification and review
- Run the narrowest relevant Rust test or native
fslccommand first. - Inspect the diff and exercise the changed contract with positive, negative, and boundary evidence.
- Run broader Rust, compatibility, or browser gates in proportion to the affected surface.
- Use the specialized FSL reviewers after semantics, coupled language files, or specs change.
- To review a pull request that resolves an issue, use
/pr-review; it orchestrates the specialized reviewers and never merges. - Before ending or compacting substantial work, run
/checkpointand preserve exact test outcomes.
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 · 53 lines · 615 tokens per session scan A cf08548b14ec
fsl CLAUDE.md is an instructions file published in the GitHub repository ymm-oss/fsl (23 stars, last pushed today), licensed Apache-2.0. It adds 615 tokens to every session, about $0.0031 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
mem0 AGENTS.md
AGENTS.md instructions for mem0ai/mem0, covering agents.md, do not, where to look, repository map and setup.
pydantic-ai AGENTS.md
AGENTS.md instructions for pydantic/pydantic-ai, covering your primary responsibility is to the project and its users, gathering context on the task, ensuring the task is ready for implementation, philosophy and requirements of all contributions.
parlant CLAUDE.md
Instructions for emcie-co/parlant: This is the main repo of Parlant (https://parlant.io).
redis-vl-python CLAUDE.md
Claude Code instructions for redis/redis-vl-python, covering claude.md - redisvl project context, frequently used commands, development workflow, redis setup and documentation.
dify AGENTS.md
AGENTS.md instructions for langgenius/dify, covering agents.md, repository gotchas and frontend workflow.
dify CLAUDE.md
Claude Code instructions for langgenius/dify, a project described as: Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.