Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.
日本語の概要は準備中です。原文の説明を表示しています。
Guide the creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when the user wants to build an MCP server to integrate an external API or service, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.
These bias toward caution over speed — use judgment on trivial tasks.
[REDACTED] and retain only the minimum location, type, and remediation evidence.Build the smallest correct TypeScript or Python MCP server in three phases: select a compatible protocol contract, implement only the required surfaces, then verify it with deterministic protocol tests.
2025-11-25 at immutable tag commit 38c84e9f93ad191d9eb26d92b945d17bd0efcaf3 as the verified baseline observed on 2026-07-28, while checking compatibility with the target project's actual pinned SDK.Origin; bind local servers to loopback; and require HTTPS and authentication for remote access.initialize must be the first protocol operation. It exchanges protocol version and capabilities; after success, the client sends notifications/initialized.initialize with a supported version. The client continues only if it supports that counteroffered version; otherwise it disconnects. Genuinely incompatible version negotiation must fail cleanly without entering operation. Invoke optional operations only when negotiated, and do not send optional notifications unless the peer advertised the corresponding capability.listChanged accurately and claim resource subscription support only when subscriptions and their lifecycle are implemented.tools/call envelope with isError: true for expected validation, upstream API, execution, or business failures that the model can inspect and correct.Test the generated MCP project without external network or LLM calls. Cover:
listChanged and resource subscriptions when exposed.isError: true execution failures, including malformed requests and timeout behavior.Origin, authentication, protocol-version, session, and content-type behavior when HTTP is selected.Prefer SDK in-memory transport or paired transport test utilities when available. Otherwise, run an isolated subprocess or ephemeral loopback server and clean it up reliably. Use the MCP Inspector only as supplemental manual debugging after automated tests, never as the sole gate.
This is a modified port of the upstream mcp-builder skill from anthropics/skills. The original ships bundled reference files (reference/mcp_best_practices.md, reference/node_mcp_server.md, reference/python_mcp_server.md, reference/evaluation.md) which are not included in this port. The reviewed upstream snapshot is pinned in UPSTREAMS.json.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.
日本語の概要は準備中です。原文の説明を表示しています。
Audit and improve project-rules files (AGENTS.md, CLAUDE.md, .agents/instructions, local overrides) so the agent keeps accurate project context. Use when the user asks to check, audit, review, update, improve, or fix their AGENTS.md or CLAUDE.md, mentions "project rules maintenance" or "agent context optimization", or when the codebase has changed enough that the rules file may be stale. Scans the repository for every rules file, grades each against a quality rubric, outputs a quality report, and applies targeted edits only after user approval.
日本語の概要は準備中です。原文の説明を表示しています。
Capture learnings from the current session into the project-rules file (AGENTS.md, CLAUDE.md, or local override) so future sessions benefit. Use when the user says "revise the rules", "update AGENTS.md / CLAUDE.md with what we just learned", "save this to project memory", "remember this for next time", or at the end of a productive session when valuable context has emerged that is not yet documented. This complements agents-md-improver — improver audits, while this one captures.
日本語の概要は準備中です。原文の説明を表示しています。
Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside frontend-design.
日本語の概要は準備中です。原文の説明を表示しています。
Design a feature architecture by analyzing existing codebase patterns and conventions, then provide a comprehensive implementation blueprint with specific files to create or modify, component designs, data flows, and a build sequence. Use this skill when the user asks for an architecture design, an implementation plan for a non-trivial feature, or when dispatched as a sub-task during feature-dev architecture phase.
日本語の概要は準備中です。原文の説明を表示しています。
Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".
日本語の概要は準備中です。原文の説明を表示しています。