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Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.

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.

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。

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

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

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。

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。

generate release notes for changes since a given tag

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。

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。

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。

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.

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.

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。

Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.

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。

Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.

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。

Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.

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。

Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.

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。

Use when the user has a fuzzy idea and wants to shape it into a concrete project spec before planning or building. Triggers on: "brainstorm this", "I have an idea for...", "help me think through this project", "what should I build", "spec this out". Also catches vague feature descriptions needing structured questioning to clarify scope. Does NOT write code, plan implementation, review strategy docs, or run strategy interviews — produces a WHAT/WHY spec through dialogue, not a HOW plan.

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。

Use when comparing two or more code implementations against a spec or requirements doc. Triggers on "which repo is better", "compare these implementations", "evaluate both solutions", "rank these codebases", or "judge which approach wins". Also covers choosing between competing PRs or vendor submissions solving the same problem. Does NOT review a single codebase for quality — use code review skills instead. Does NOT evaluate strategy docs — use strategy-review. Requires a spec file and 2+ repo paths.

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。

Use when you need a bite-sized, TDD-driven implementation plan but do NOT have a brainstorm-beagle spec to plan against. quick-plan reconstructs intent from the current conversation, fans out domain-expert exploration subagents across the codebase, and synthesizes the same plan format write-plan produces — without requiring `.beagle/concepts/<slug>/spec.md`. Triggers on: "quick plan", "plan this out", "plan what we just discussed", "turn this into an implementation plan", "plan this without a spec", "I don't have a spec, just plan it", "write-plan but no spec". Make sure to use this skill whenever the user wants an implementation or TDD plan and there is no spec to plan against — even if they just say "plan it" after discussing a feature. Writes to `.beagle/plans/<slug>/plan.md`. If a finalized spec already exists at `.beagle/concepts/<slug>/spec.md`, prefer write-plan. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.

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。

Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: "resolve the open questions", "close the gaps in this spec", "research the open items", "finalize my spec", "make this spec implementation-ready", "answer the TBDs". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwise), proposes answers one at a time for user approval, then rewrites the spec in place so it arrives at planning with no known gaps. Does NOT write code, design implementation, or create plans — it only produces a complete spec.

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。