Borrowing it
Nothing to install: this file belongs to quality-screener/quality-screener-mcp-server. 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/quality-screener/quality-screener-mcp-server/main/CLAUDE.mdgit clone --depth 1 https://github.com/quality-screener/quality-screener-mcp-serverWrote 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/quality-screener/quality-screener-mcp-server/claude-md)<a href="https://agentmods.dev/instructions/quality-screener/quality-screener-mcp-server/claude-md"><img src="https://agentmods.dev/badge/instructions/quality-screener/quality-screener-mcp-server/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/quality-screener/quality-screener-mcp-server/claude-md"><img src="https://agentmods.dev/badge/instructions/quality-screener/quality-screener-mcp-server/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 | $0.01836 | $0.01836 |
| Opus 5 | $0.00918 | $0.00918 |
| Sonnet 5 | $0.00367 | $0.00367 |
| Haiku 4.5 | $0.00184 | $0.00184 |
Grade A, and why
quality-screener-mcp-server 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 10d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md / AGENTS.md
Guidance for AI coding agents working in this repository. AGENTS.md is a symlink to
this file. The workspace root (../CLAUDE.md) owns the cross-repo rules: worktrees,
memory, and the REST contract with the quality-screener backend.
Critical rules
- No business logic here. This is a thin, stateless façade. If a change needs
computation, it belongs behind a REST endpoint in
quality-screener/. Payload shaping (_slim_score_rows,normalize_config) is the one allowed exception — see Adapters. - Branch off
main(this repo's default), in a worktree under../worktrees/quality-screener-mcp-server/<branch-slug>/. - The backend ships first. Tools talk to the deployed API; a tool released ahead of its endpoint is broken in production.
Commands
Requires uv. Python >=3.11. No linter or type checker
is configured.
uv sync # install deps (incl. dev group)
uv run pytest # full suite; no live backend needed
uv run pytest tests/test_tool_filters.py::test_screen_share_builds_full_url
uv run qscreener-mcp # stdio transport (default)
# HTTP mode, mirroring the remote deployment:
QSCREENER_MCP_TRANSPORT=streamable-http QSCREENER_MCP_PORT=8080 \
QSCREENER_API_URL=http://localhost:8001 uv run qscreener-mcp
Architecture
An MCP façade over the Quality Screener REST API (the "stobot" backend), with no
dependency on the backend Python package — every tool builds a request and returns the
decoded JSON. Extending it means adding an @mcp.tool() that delegates to _guard.
Four modules under qscreener_mcp/:
server.py— the whole tool surface (~760 lines). TheFastMCPinstance, every@mcp.tool(), token resolution, and themain()transport entry point. Config (_API_URL,_WEBSITE_URL,_PUBLIC_URL,_MCP_HOST) resolves at import time from env vars, so tests patch the module attribute (monkeypatch.setattr(server, "_WEBSITE_URL", ...)) rather than the env var.constants.py— everything fixed at build time: header names, env-var names and defaults, on-disk filenames, user-facing messages, and theToolenum of canonical tool names. Env values are deliberately not resolved here.client.py—ApiClient, a minimal httpx wrapper. Attaches the bearer token asX-Stobot-CLI-Token, adds the analytics headers, decodes JSON (Nonefor empty bodies), raisesApiErroron any non-2xx. Self-contained copy of the backend CLI's client — keep it dependency-free.oauth.py—StobotOAuthProviderfor HTTP mode. OAuth state is in-memory (_pending,_codes) → single-process only. Registered clients persist to~/.config/qscreener/mcp_clients.json.
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.
- 10d ago First seen · 130 lines · 1,836 tokens per session scan A ae2f04a25eac
quality-screener-mcp-server CLAUDE.md is an instructions file published in the GitHub repository quality-screener/quality-screener-mcp-server (0 stars, last pushed 6d ago), licensed MIT. It adds 1,836 tokens to every session, about $0.0092 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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