Borrowing it
Nothing to install: this file belongs to movie-reservation-platform-lab/movie-recommendation-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/movie-reservation-platform-lab/movie-recommendation-mcp/main/.ai/skills/python-control-flow/SKILL.mdgit clone --depth 1 https://github.com/movie-reservation-platform-lab/movie-recommendation-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/skills/movie-reservation-platform-lab/movie-recommendation-mcp/python-control-flow)<a href="https://agentmods.dev/skills/movie-reservation-platform-lab/movie-recommendation-mcp/python-control-flow"><img src="https://agentmods.dev/badge/skills/movie-reservation-platform-lab/movie-recommendation-mcp/python-control-flow/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/skills/movie-reservation-platform-lab/movie-recommendation-mcp/python-control-flow"><img src="https://agentmods.dev/badge/skills/movie-reservation-platform-lab/movie-recommendation-mcp/python-control-flow.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 | $0.00055 | $0.00844 |
| Opus 5.5 | $0.00022 | $0.00338 |
| Sonnet 5.5 | $0.00011 | $0.00169 |
| Haiku 4.5 | $0.00006 | $0.00084 |
Grade A, and why
python-control-flow 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 15d 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.
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 ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 15d ago First seen · 92 lines · 55 tokens per session scan A c37cf03ad623
python-control-flow is a skill published in the GitHub repository movie-reservation-platform-lab/movie-recommendation-mcp (0 stars, last pushed 16d ago), with no licence file. It adds 55 tokens to every session and 844 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-13.
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