FastMCP is a Python framework for building Model Context Protocol applications, which connect language models with tools and data through servers and clients. Developers use it to expose Python functions as tools, connect to local or remote MCP servers, and build interactive tool interfaces. The catalogue includes eight skills, three instructions, two rules, one setting, and one hook for FastMCP.
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 skills add PrefectHQ/fastmcp --skill code-reviewgit clone --depth 1 https://github.com/PrefectHQ/fastmcpWrote 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/prefecthq/fastmcp/code-review)<a href="https://agentmods.dev/skills/prefecthq/fastmcp/code-review"><img src="https://agentmods.dev/badge/skills/prefecthq/fastmcp/code-review/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/prefecthq/fastmcp/code-review"><img src="https://agentmods.dev/badge/skills/prefecthq/fastmcp/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 88 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00010 | $0.00194 |
| Opus 5.5 | $0.00004 | $0.00078 |
| Sonnet 5.5 | $0.00002 | $0.00039 |
| Haiku 4.5 | $0.00001 | $0.00019 |
Grade A, and why
code-review 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 22d 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
Code Review Skill
This skill guides you through conducting thorough code reviews.
Review Checklist
When reviewing code, consider:
Correctness
- Does the code do what it's supposed to do?
- Are edge cases handled?
- Are there any obvious bugs?
Maintainability
- Is the code easy to understand?
- Are variable and function names descriptive?
- Is there appropriate documentation?
Performance
- Are there any obvious performance issues?
- Are expensive operations cached when appropriate?
- Are database queries efficient?
Security
- Is user input validated?
- Are there any injection vulnerabilities?
- Are secrets properly managed?
Giving Feedback
- Be specific and actionable
- Explain why something should change
- Suggest alternatives, don't just criticize
- Acknowledge good work too
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.
- 22d ago Changed · +12 lines · -22 tokens per session 7179571522e2
- 23d ago Changed · +2 lines db335aab7c60
- 1mo ago Changed · -75 lines · -11 tokens per session c996217ffb59
- 1mo ago First seen · 102 lines · 43 tokens per session scan A 5ae40fc0c173
code-review is a skill published in the GitHub repository PrefectHQ/fastmcp (28,127 stars, last pushed today), licensed Apache-2.0. It adds 10 tokens to every session and 194 once invoked, about $0.0000 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 2026-08-30.
Other skills, from other repositories
code-review
A code-review and quality-analysis skill written in Chinese. It is intended to inspect code, find potential problems, and suggest improvements.
code-review
Automated code review with checklist and quality gates.
review-loop
Iterative code review — fix P1+P2 until clean, report P3 as an offer.
code-reviewer
A code-review workflow written in Chinese that checks software changes for correctness, security, performance, and maintainability. It also defines a report format for critical issues and suggestions.
code-review
Code review for issues, style consistency, best practices—use for full code evaluation; not for debugging specific errors or fixing individual style violations.
code-review
Reviews code changes using CodeRabbit AI. Use when user asks for code review, PR feedback, code quality checks, security issues, or wants autonomous fix-review cycles.