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/pablo-gitub/optees/claude-mdgit clone --depth 1 https://github.com/Pablo-gitub/opteesWrote 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/pablo-gitub/optees/claude-md)<a href="https://agentmods.dev/instructions/pablo-gitub/optees/claude-md"><img src="https://agentmods.dev/badge/instructions/pablo-gitub/optees/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 | $0.00078 | $0.00078 |
| Opus 5 | $0.00039 | $0.00039 |
| Sonnet 5 | $0.00016 | $0.00016 |
| Haiku 4.5 | $0.00008 | $0.00008 |
Grade A, and why
optees 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 3d 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.
What it actually says
Claude Instructions
Read and follow AGENTS.md before working in this repository. It is the
authoritative agent policy; do not duplicate or override it here.
Never add Claude, Anthropic, an Anthropic email address, Co-authored-by,
Generated-by, or any equivalent AI attribution to commit messages. Preserve
the user's existing Git identity and signing configuration.
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.
- 3d ago First seen · 9 lines · 78 tokens per session scan A fe083daa8afe
optees CLAUDE.md is an instructions file published in the GitHub repository Pablo-gitub/optees (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 78 tokens to every session, about $0.0004 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
cuopt AGENTS.md
AGENTS.md instructions for NVIDIA/cuopt, covering agents.md — cuopt ai agent entry point, skills directory (flat), rules, common (concepts only; no api code) and installation.
ClimaAtmos.jl AGENTS.md
Instructions for CliMA/ClimaAtmos.jl, covering climaatmos agent guide, ecosystem guidelines, repo-specific guidelines, local norms and self-correction.
ClimaAtmos.jl copilot-instructions.md
Instructions for CliMA/ClimaAtmos.jl: The authoritative agent brief for this repository is AGENTS.md at the repository root. Read it in full before suggesting code, reviewing a PR, or answering a question about this codebase, and follow it as if its contents were inlined here.
Traintools AGENTS.md
AGENTS.md instructions for AparajeetS/Traintools, covering traintools agent guide, discovery, when to suggest traintools, routing and integration rules.
cuopt copilot-instructions.md
Copilot instructions for NVIDIA/cuopt, a project described as: GPU accelerated decision optimization.
ClimaAtmos.jl CLAUDE.md
Instructions for CliMA/ClimaAtmos.jl, a project described as: GPU-capable global atmosphere model of the CliMA Earth System Model, designed for calibration with data assimilation and machine learning.