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.
Borrowing it
Nothing to install: this file belongs to PrefectHQ/fastmcp. 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/PrefectHQ/fastmcp/main/.agents/skills/triage/SKILL.mdgit 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/triage)<a href="https://agentmods.dev/skills/prefecthq/fastmcp/triage"><img src="https://agentmods.dev/badge/skills/prefecthq/fastmcp/triage/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/triage"><img src="https://agentmods.dev/badge/skills/prefecthq/fastmcp/triage.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 | $0.00032 | $0.00775 |
| Opus 5.5 | $0.00013 | $0.00310 |
| Sonnet 5.5 | $0.00006 | $0.00155 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
triage 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 7d 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
Find worthwhile work
Read AGENTS.md and the contribution policy in the development guide. Honor the requested scope. For "since the last patch," identify the release timestamp, then look for open issues created or substantively updated afterward. Use a bounded inventory internally; report truncation if it affects coverage.
Start from an inventory, then link each candidate to the PRs that mention it:
gh release list --repo PrefectHQ/fastmcp --limit 1
gh issue list --repo PrefectHQ/fastmcp --state open --limit 30 \
--json number,title,author,createdAt,updatedAt,labels,comments
gh pr list --repo PrefectHQ/fastmcp --state all --search "<issue-number> in:body" \
--json number,title,state,author
Where search, GraphQL or --paginate are blocked (cloud sessions), page repo-scoped REST by hand (gh api "repos/PrefectHQ/fastmcp/pulls?state=all&per_page=100&page=N"), dump it once, and join locally.
Pick work with concrete user impact, a supported contract being violated, a tractable causal fix, and a change FastMCP wants to carry. Apply triage and merge decisions before ranking anything: labels and confident diagnoses are claims, and the behavior may already be fixed or decided. Mark each pick want / marginal / don't want. Do not rank solely by recency, comment count, or how small a proposed patch looks. Automated dashboard churn is not itself useful work.
For promising candidates:
- Read the report, comments, maintainer decisions, and prior bot investigation.
- Search related issues and PRs in all states, including gate-closed contributions. Closure alone says nothing about a fix's merit.
- Trace the relevant code and git history. Distinguish fixed on main, released on the affected line, and merely proposed.
- Verify the claimed contract and enough evidence to explain the cause. A proposed solution is a lead, not a diagnosis. Consider compatibility before calling a fix straightforward.
For a cleanup pass, the mechanical wins are open PRs whose linked issue is already closed, issues still open after a merged "fixes #N" (check for a revert first), and competing PRs for one issue.
Lead with the strongest pick as soon as evidence supports it: link, what breaks, why it matters, where the cause is, and the next action. Include a few alternatives when useful. Do not turn selection into an exhaustive audit or a full review of every linked PR. Save continuation notes only when needed.
When the user chooses implementation, use fix-issue. For an assignment decision on an external contribution, use review-issue. Triage alone does not authorize public mutations or implementation.
For a batch of maintainer decisions, save a self-contained proposal naming each target, exact action or patch, pinned revisions, evidence and executed checks, remaining questions, and dependencies between items. Include contributor ownership and the canonical issue or PR for proposed closures. Use upstream EGM in the maintainer session when independent batch review is requested. A Cloud node returns its assigned stage's declared output; the maintainer-facing session assembles the proposal and obtains sign-off.
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.
- 7d ago Changed · +2 lines 2b343bc1f7da
- 18d ago Changed · +4 lines 7c902027cb0d
- 1mo ago Changed 28da7b07be31
- 1mo ago Changed · +10 lines 44bd406f46f5
- 1mo ago First seen · 22 lines · 32 tokens per session scan A aface6146d53
triage is a skill published in the GitHub repository PrefectHQ/fastmcp (28,322 stars, last pushed today), licensed Apache-2.0. It adds 32 tokens to every session and 775 once invoked, about $0.0001 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 2026-09-12.
triage compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| triagePrefectHQ/fastmcp | Skill | 28k | today | Apache-2.0 | A | 32 | Claude Code, Codex |
| mcp-apps-builderShubhamsaboo/awesome-llm-apps | Skill | 144k | yesterday | Apache-2.0 | A | 139 | Claude Code, Codex |
| iterate-implementationhumanlayer/humanlayer | Skill | 13k | yesterday | Apache-2.0 | A | 16 | Claude Code, Codex |
| agentseek-lifecycleob-labs/agentseek | Skill | 205 | 29d ago | Apache-2.0 | A | 54 | Codex |
| guardrails-developer-guideNVIDIA-NeMo/Guardrails | Skill | 7.4k | yesterday | none | A | 92 | Claude Code, Codex |
| triaging-incidentsjschuller/mcp-server-servicenow | Skill | 27 | 2d ago | MIT | A | 89 | Claude Code |
| langsmith-traceob-labs/agentseek | Skill | 205 | 29d ago | Apache-2.0 | C | 54 | Claude Code, Codex |