Borrowing it
Nothing to install: this file belongs to akitaonrails/llm-coding-benchmark. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/akitaonrails/llm-coding-benchmark/master/CLAUDE.mdgit clone --depth 1 https://github.com/akitaonrails/llm-coding-benchmarkWrote 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/akitaonrails/llm-coding-benchmark/claude-md)<a href="https://agentmods.dev/instructions/akitaonrails/llm-coding-benchmark/claude-md"><img src="https://agentmods.dev/badge/instructions/akitaonrails/llm-coding-benchmark/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/akitaonrails/llm-coding-benchmark/claude-md"><img src="https://agentmods.dev/badge/instructions/akitaonrails/llm-coding-benchmark/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.07516 | $0.07516 |
| Opus 5.5 | $0.03006 | $0.03006 |
| Sonnet 5.5 | $0.01503 | $0.01503 |
| Haiku 4.5 | $0.00752 | $0.00752 |
Grade E, and why
llm-coding-benchmark CLAUDE.md scanned grade E with 2 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 today.
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.
Harvests environment variableshighData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
- Do not run `env`, `printenv`, `cat .env`, or `grep ENV_VAR_NAME` patterns that would dump secret values into the visible output. Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- **`--force` does NOT wipe `results/<slug>/project/`** — a forced re-run starts with the previous run's completed app in the workspace and the model just polishes it (contaminated result: K3 "finished" in 8 min at 625K The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- today Changed · +10 lines · +113 tokens per session b13ddb8886a6
- 6d ago Changed · +15 lines · +227 tokens per session ad5d7233b7c6
- 2mo ago First seen · 169 lines · 7,176 tokens per session scan E 55597d0ed45b
llm-coding-benchmark CLAUDE.md is an instructions file published in the GitHub repository akitaonrails/llm-coding-benchmark (363 stars, last pushed yesterday), with no licence file. It adds 7,516 tokens to every session, about $0.0301 per session on Opus 5.5. A static security scan graded it E with 2 findings (harvests environment variables, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
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Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
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AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.