Borrowing it
Nothing to install: this file belongs to tianhuil/webfetch-camouflage-mcp. 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/tianhuil/webfetch-camouflage-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/tianhuil/webfetch-camouflage-mcpWrote 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/tianhuil/webfetch-camouflage-mcp/agents-md)<a href="https://agentmods.dev/instructions/tianhuil/webfetch-camouflage-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/tianhuil/webfetch-camouflage-mcp/agents-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/tianhuil/webfetch-camouflage-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/tianhuil/webfetch-camouflage-mcp/agents-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 | $0.00260 | $0.00260 |
| Opus 5 | $0.00130 | $0.00130 |
| Sonnet 5 | $0.00052 | $0.00052 |
| Haiku 4.5 | $0.00026 | $0.00026 |
Grade A, and why
webfetch-camouflage-mcp 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 9d 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
Rules for python repos
General
- Use
uvfor package manager.
Coding Style
- Prefer to write functional code. Prefer list comprehensions for map, flatmap, and filter:
[func(x) for x in xs if condition(x)]. - Prefer to not mutate variables, even though Python allows mutation (pretend variables are all using typescript
constnotlet). - Explicitly type where practicable using pyright. Look at the libraries and import and use those types; do not make up types.
- Always avoid using type
Anytype. - Add docstring to every class and function.
- Place
try/exceptonly at the root calling function. Do not placetry/exceptin each intermediate or leaf function. - If a library is missing a pyright type (e.g.
requests), add the types as a development dependency (e.g.types-requests). - For a multiline string, use
textwrap.dedentand format nicely.
Validation
Whenever you make a change, run ruff and pyright to confirm tests pass.
Use git diff and review code changes for following the rules under "Coding Style" section.
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.
- 9d ago First seen · 22 lines · 260 tokens per session scan A afe95f0fb801
webfetch-camouflage-mcp AGENTS.md is an instructions file published in the GitHub repository tianhuil/webfetch-camouflage-mcp (1 stars, last pushed 6mo ago), licensed MIT. It adds 260 tokens to every session, about $0.0013 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.
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