「beagle」の検索結果

Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.

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Reviews pytest test code for async patterns, fixtures, parametrize, and mocking. Use when reviewing test_*.py files, checking async test functions, fixture usage, or mock patterns.

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。

Comprehensive Go web development persona enforcing zero global state, explicit error handling, input validation, testability, and documentation conventions. Use when building Go web applications to ensure production-quality code from the start.

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。

Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configuration and usage mistakes.

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Explanation documentation patterns for understanding-oriented content - conceptual guides that explain why things work the way they do. Use when writing an explanation doc, conceptual guide, understanding/background doc, design rationale, or architecture explanation, or when asked how/why something works. Builds on the docs-style core writing principles.

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。

Verify documentation coverage and generate missing docs interactively

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Analyzes feedback logs to identify patterns and suggest improvements to review skills. Use when you have accumulated feedback data and want to improve review accuracy.

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Review implementation plans for parallelization, TDD, types, libraries, and security before execution

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Core technical documentation writing principles for voice, tone, structure, and LLM-friendly patterns. Use when writing or reviewing any documentation.

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。

Vercel AI SDK for building chat interfaces with streaming. Use when implementing useChat hook, handling tool calls, streaming responses, or building chat UI. Triggers on useChat, @ai-sdk/react, UIMessage, ChatStatus, streamText, toUIMessageStreamResponse, addToolOutput, onToolCall, sendMessage.

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Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features. Catches common mistakes in state management, graph structure, and async patterns.

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Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.

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generate release notes for changes since a given tag

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Applies fixes from a prior review-llm-artifacts run, with safe/risky classification. Respects verify-llm-artifacts output when present to skip false positives.

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Confirms or rejects findings from review-llm-artifacts before deletes or risky refactors. Loads review-verification-protocol-style checks per finding. Use after a review run, when the user wants to reduce false positives, before fix-llm-artifacts on dead code, or when validating a full-project scan.

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。

Fetch unresolved review comments from a PR and evaluate with receive-feedback skill

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。

commit and push all local changes to remote repo

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Mandatory verification steps for all code reviews to reduce false positives. Load this skill before reporting ANY code review findings.

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Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

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Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).

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Use when you want to generate Architecture Decision Records from this session. Triggers on "write ADRs", "document our decisions", "create decision records", "record the choices we made". Also useful after design discussions where decisions were reached but not documented. Does NOT extract decisions alone (use adr-decision-extraction) or provide MADR template (use adr-writing). Orchestrates the full workflow: subagent extraction, user confirmation, parallel generation, and verification.

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Use when you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.

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。

Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.

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Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.

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。