「tia」の検索結果

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.

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

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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.

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。

Implements agents using Deep Agents. Use when building agents with create_deep_agent, configuring backends, defining subagents, adding middleware, or setting up human-in-the-loop workflows.

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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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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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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。

Create Agent Skills with best practices, structure, validation, and testing. Use when designing or refining skills, prompts, references, or supporting files.

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

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 wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

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Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.

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

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Use when the user wants to pressure-test a product, internal-tool, or OSS concept against Amazon's Working Backwards PRFAQ gauntlet before committing to a spec. Triggers on: "work backwards", "write a PRFAQ", "press release first", "is this idea worth building", "pressure-test this concept", "filter this before brainstorm", "is this a real product". Also catches solution-first pitches ("I want to build X that does Y") and technology-first pitches ("use AI to...") that need customer-first filtering. Produces a binary pass/fail verdict, not a polished doc. Hardcore coaching — direct, skeptical, concrete. On pass, hands off to brainstorm-beagle with a concept brief. Does NOT write code, plan implementation, scaffold projects, or draft specs.

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。

Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality.

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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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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.

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Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.

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Use when reviewing, critiquing, or stress-testing an existing strategy document. Evaluates seven dimensions — diagnosis quality, guiding policy strength, action coherence, assumption exposure, falsifiability — with optional 7S, Five Forces, Balanced Scorecard, and Hoshin Kanri lenses. Triggers on: review my strategy, poke holes in this plan, what's weak here, strategy audit, red team this. Does NOT build strategy (use strategy-interview) or brainstorm project ideas (use brainstorm-beagle).

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。

Optimize prompts for code-related tasks following prompt-engineering best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.

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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。

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。

Respond to review comments on a PR after evaluation and fixes

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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.

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Repo-wide structural-maintainability review — code-judo restructurings, 1k-line file guard, anti-spaghetti branching, canonical-layer enforcement, anti-magic abstractions, explicit type/boundary contracts.

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。