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概要と使いどころ
Run /scope to turn a product idea into a living, coarse scope in docs/scope/ and keep it current: plan a new product, plan the next slice, enroll one named feature, or run with no argument to reconcile after shipping and queue what is next. Seeds WHAT to build; /architect designs, /develop builds.
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。
Run /debug to find and fix a bug's root cause: a test failing for an unclear reason, /check verify finding a failure, or behavior being wrong. Runs a reproduce, localize, hypothesize, test, fix, verify loop, makes the minimal fix, and hands a regression test to /test. No features, no extra refactors.
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。
Run /develop to build a feature, UI or backend, from an approved design, a page, component, API, service, or data slice. If something load bearing is undecided and no spec records it, it stops and routes you to /architect; otherwise it reads the spec plus AGENTS.md, builds, and advances the scope.
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。
Run /test to write a test suite for code you just built or changed, after implementing a feature, route, or fix. Targets uncommitted changes automatically, reads test preferences.json for your framework (asks and saves it if absent), and picks the right strategy per file: happy path, edge cases, error states, accessibility.
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。
Run /architect when choosing between approaches, designing a feature or page, picking a tech stack, or when /develop says a decision is owed, anytime a load bearing technical decision is unmade. Asks deep questions, recommends an answer, and writes a build spec to docs/specs/. Owns all spec 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。
Audits, preserves and recovers local Git state before cleanup: unpushed or wrong-branch commits, dirty worktrees, duplicate clones, stashes, dangling commits, squash-merge uncertainty. Use when the user fears lost work, wants to recover a commit, asks if a branch is actually merged, asks whether a branch, worktree or clone is safe to delete, or wants one clean main (误删分支 / 还有没有丢的东西). Not for GitHub PR operations.
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。
Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt). Use when requirements lack triggers or measurable outcomes, or the user asks to "optimize my prompt" / "improve this requirement" / "make this more specific".
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 what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and tool definitions are packed into the window. Produces a context inventory with a keep/cut/restructure verdict per component, ordering and caching fixes, and a token budget. For wording-level prompt tuning use prompt-optimizer.
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。
Analyse competitor moves and translate them into strategic implications for your product roadmap. Use when a competitor announces a new feature, pricing change, partnership, or strategic shift, or when producing a periodic competitive intelligence report. Produces a categorised signal analysis with reactive-vs-proactive assessment, threat ratings, specific roadmap implications, and recommended responses with owners. For a recurring whole-market briefing use competitive-intelligence-monitor instead.
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。
Run a fast, honest competitive scan — the dimension table built from public evidence (sites, docs, pricing pages, changelogs, reviews), the claims-vs-observed discipline, and the so-what synthesis that ends in moves, not a landscape mural. Use when asked what are competitors doing, quick scan of these three rivals, how does our pricing/feature set compare, or prep the competitive slide honestly. Produces the evidence-based comparison table, the marketing-vs-reality flags, the so-what synthesis, and the staleness date.
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。
Simplify code that works — remove speculative abstraction, dead flexibility, and needless indirection while keeping behaviour identical and verified. Use when a feature has just landed ('now simplify it'), when AI-generated code arrives over-engineered, when a file has grown hard to follow, or as the cleanup pass before review. Produces a smaller, flatter version with identical behaviour, plus a ledger of what was removed and why it was safe. For finding bugs use code-review-checklist / ai-code-review — this skill assumes it works and makes it simple.
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。
Activate a 4-stage coding discipline framework that forces Claude to plan before coding, isolate changes on a branch, write tests first, and self-review output twice before presenting it. Use when starting a complex coding task, when past Claude sessions produced broken first drafts, or when you want to prevent rework cycles. Produces a confirmed written plan, isolated feature branch, test-first implementation, and a double-reviewed output with a correctness and code-quality checklist.
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。
Write a human changelog from the REAL commit history — read the actual commit range via the GitHub connector, not a template. Use when asked to write the changelog for this release, what changed since the last tag, draft release notes from my commits, or summarise this range for users in Cowork. Reads commits/PRs between two refs via the GitHub connector, groups them into user-facing changes (features / fixes / breaking), translates commit-speak into human benefit, and produces a changelog artifact ready for the release.
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 statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.
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。
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring 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。
Run a blameless postmortem for an incident caused by an AI agent or LLM feature — hallucinated facts shipped to users, runaway tool use, prompt injection, cost blowouts, or wrong actions taken autonomously. Use when asked to write up an AI incident, analyse why an agent did something wrong, or produce corrective actions after an LLM failure. Produces a structured postmortem with trace reconstruction, a root-cause layer analysis, and corrective actions including a permanent regression case. For non-AI production incidents use incident-postmortem.
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。
Specify the tracing, metrics, and alerting for an AI agent or LLM feature in production. Use when asked what to log for an LLM app, design agent tracing or spans, define quality and cost monitors, or answer 'how do we know if the agent is misbehaving?'. Produces an observability spec with a trace schema, metric definitions with owners and alert thresholds, sampling and retention policy, and a privacy note for logged content.
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。
Write a short, punchy announcement designed to be shared as an image or social card. Use when asked to announce a launch, milestone, feature, hire, funding, or win — something to post on LinkedIn/X/Slack. Produces a tight, visually-structured announcement (headline, one-liner, 2-3 proof points, CTA) that looks great exported as a PNG card from the playground.
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。
Decode a 401k or workplace retirement plan — the real cost of its funds, the match's fine print, vesting math, and the plan features worth using or avoiding. Use when someone asks 'is my 401k any good', 'decode my 401k plan', 'which funds should I look at', or 'what fees am I paying'. Produces a fee decode in dollars-over-time, match and vesting math, a fund-lineup triage by cost, and the questions for HR or the plan administrator.
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。
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
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。
Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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。
Help users move from reactive feature-building to strategic resource allocation by applying rigorous scoring, financial modeling, and portfolio balancing.
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。
Help users conduct high-impact customer interviews that move beyond surface-level feature requests to identify root emotional frustrations and specific causal triggers.
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。
Debugging-method traps: run the discriminating experiment first, read build logs bottom-up, treat an empty actual as unwritten, audit shared handles before removing a feature, read commit history critically. Use when a fix is not converging.
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。