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概要と使いどころ
Establish channels and processes for capturing and processing post-launch feedback during PRD v0.9 Go-to-Market. Triggers on requests to set up feedback systems, capture user input, or when user asks "how do we collect feedback?", "feedback loop", "user research", "post-launch feedback", "customer feedback", "NPS", "voice of customer". Outputs CFD- entries specialized for post-launch feedback capture.
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。
Orchestrate go-to-market strategy by running positioning, offer construction, and channel allocation as a sequenced workflow during PRD v0.9 Go-to-Market. Triggers on requests to plan launch, define GTM strategy, or when user asks "how do we launch?", "go-to-market", "launch plan", "marketing strategy", "GTM", "launch this product". Chains three framework-grounded sub-skills (Dunford → Hormozi → ORB) and reconciles their outputs.
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。
Define product positioning using April Dunford's 5-step framework during PRD v0.9 Go-to-Market. Triggers on requests to position the product, define category, frame against competitors, or when user asks "what category are we?", "how do we position?", "Dunford positioning", "obviously awesome", "market frame of reference", "positioning statement". Outputs GTM-* entries with Type=Positioning and BR-POS-* positioning rules.
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 operational playbooks for incident response, deployments, and maintenance during PRD v0.8 Deployment & Ops. Triggers on requests to create runbooks, document procedures, or when user asks "how do we handle incidents?", "runbook", "operational procedures", "on-call guide", "incident response", "maintenance procedures". Outputs RUN- entries with step-by-step operational procedures.
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。
Assess competitor defensibility and define our own moat strategy during PRD v0.3 Commercial Model. Triggers on requests to analyze competitor moats, define our defensibility, assess switching costs, identify vulnerabilities, find wedge opportunities, or when user asks "what's our moat?", "how defensible are they?", "where can we compete?", "switching costs?", "defensibility", "who to target". Consumes Competitive Landscape (v0.2) CFD- entries. Outputs CFD- entries for competitor moats and BR- entries for targeting rules and our defensibility strategy.
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 working on the Bolt repository and you need to configure or compile code correctly and efficiently. This skill defines the canonical Bolt build workflow, including when and how to run `make`, `cmake --build ...`, and when build reconfiguration is necessary due to added files and test or build target changes.
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 the current plan test-first, the Canon TDD way, driving to a single finishing condition — an empty test list. Derives a Canon TDD test list from the plan, shows it before any code, writes one failing test at a time, and keeps the list visible as it evolves. Use when the user asks to implement, build, or execute the current/approved 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。
Редактура текстов на русском языке по методу Людмилы Сарычевой — от смысла к словам, а не наоборот. Use this skill whenever the user asks to edit, review, improve, shorten, or polish any Russian-language text — articles, posts, emails, landing copy, docs. Триггеры: «отредачь», «отредактируй», «почисти текст», «сократи», «перепиши по-человечески», «убери канцелярит», «сделай проще», «причешите текст», «дай фидбек на текст», «разбери мой текст», «слишком водянисто», «звучит как робот». Also trigger when the user pastes a Russian draft and asks what's wrong with it or how to make it better. Для написания текста с нуля см. skill statya; для текстов интерфейса — ux-copy.
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。
This skill should be used when the user asks to "find references", "참고할 만한 거", "similar implementation", "오픈소스 있나", "how others do this", "레퍼런스 찾아줘", or needs to find existing patterns (internal) and open-source examples (external) for implementing a feature.
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。
Analyze an open-source project from What/Why perspective (not how-it's-implemented). Use when the user says "/analyze-oss", "분석해줘 이 오픈소스", "이 레포 뭐하는거야", "analyze this repo", "what does X do", "이거 왜 쓰는거야", "이 라이브러리 분석", provides a GitHub URL and wants understanding, or asks to deeply understand an OSS project's purpose, value, target users, and usage flow. Clones the repo to ~/opensource-analysis/<repo-name>/ (git pull if already exists), dispatches parallel subagents per analysis lens, then synthesizes a What/Why-focused report in chat. Supports optional user-specific follow-up questions.
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。
Explain a body of work plainly so a person actually understands it. Runs the `how` and `why` skills and weaves what they find into one clear explanation. Use for 'teach me this', 'help me really understand X', 'explain this change or subsystem to me'.
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 for 'why does X work this way', 'why we picked Y', '为什么这么设计', design rationale, regressions, or where a magic number came from. Fans out one investigator per evidence category — in this repo that starts with the local decision record (ADRs, docs/design, the daemon issue store, the changelog, wisp) plus git history, and adds MCP-backed categories when the session has them — then returns a cited read with the gaps named. Use `how` for what the code does at runtime.
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 an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.
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。
Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.
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 structured data markup (JSON-LD) for rich results, AI search visibility, and enhanced SERP features. Supports Article, FAQ, HowTo, Product, Review, LocalBusiness, Event, BreadcrumbList, Person, Organization, and more. Use when the user needs schema markup, structured data, rich snippets, JSON-LD, or wants to improve search appearance.
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。
Writes and rewrites the words on marketing pages: homepage, landing page, pricing, feature, product and about pages, including headlines, button text, section copy, title tag and meta description. Starts from the reader's own language and the proof the business can show, edits copy where it lives in the repository, and flags every claim that needs evidence. Use when someone says "write copy for this page", "rewrite our homepage", "this page sounds generic", "headline ideas", "better CTA", "landing page copy for this ad" or "pricing page wording". Email copy belongs to email-sequence, popups to popup-cro, and line-editing a finished draft to copy-editing.
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。
Designs the public interface of a module before any implementation exists: writes the caller scenarios first, produces three candidates with genuinely different shapes, type-checks each one against the scenarios, scores them on measurable criteria and records the decision. Use when someone says "design the API for this module", "what should this signature look like", "compare interface options", "this function is awkward to call", "show me alternatives before I build it", or is about to add a module, library entry point, endpoint or command that other code will depend on.
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。
Build heads-up display (HUD) dashboards for AI coding agents — show context usage, active tools, running sub-agents, and task progress in real-time. Use when: monitoring AI agent activity, building developer tools for AI-assisted coding, creating status dashboards for agent workflows.
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。
Builds a content strategy a small team can execute: audience needs backed by counted evidence, an audit of what is already published, a topic map with one page per search intent, a scored backlog, a 90-day calendar sized to real capacity, and the numbers that will show whether it worked. Use when someone asks "what should we write about", "plan our blog", "content strategy", "content plan for next quarter", "topic clusters", "content audit", "our blog traffic is falling", or wants a pile of content ideas turned into a prioritised 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。
Audits and reorganises what an AI coding agent carries in its context window: instruction files, path-scoped rules, skills, subagents, hooks and the running conversation. Use when someone says "the agent ignores our CLAUDE.md", "set this repo up for Claude Code / Codex / Cursor", "my AGENTS.md is too long", "quality drops in long sessions", "it keeps inventing APIs", "what should go in a skill versus the rules file", or asks how to hand work to a subagent. Grounded in the documented loading behaviour of Claude Code, with the equivalents for Codex, Gemini CLI and Cursor.
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。
Chromatic is a visual testing service from the Storybook team that snapshots every story in a cloud browser and shows visual diffs for review on each pull request. Use when the user wants visual regression testing with Storybook, or mentions "chromatic," "visual regression," "Storybook testing," "UI review," "visual diff," or "component snapshot testing." For general screenshot comparison, see percy.
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。
Context7 fetches current, version-specific documentation and code examples for software libraries and hands them to an AI coding agent, so generated code matches the library version in use instead of stale training data. Use when someone says "use context7", "get the latest docs for this library", "look up the current API", "the agent keeps using a deprecated method", "set up Context7 in Claude Code or Cursor", or asks how to call a framework whose API changed recently. Covers the ctx7 CLI, the MCP server and its two tools, library IDs and version pinning, API keys and rate limits, and the REST API.
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。
Translates performance test results into infrastructure scaling recommendations with cost estimates. Use when someone has load test data and needs to know what infrastructure changes are required to handle target traffic. Trigger words: capacity planning, scaling plan, infrastructure sizing, how many pods, RDS sizing, handle more traffic, scale for launch.
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。
Cerebras Inference is a hosted LLM API that runs open models on Cerebras wafer-scale chips at thousands of tokens per second, through an OpenAI-compatible endpoint. Use when a user wants the lowest-latency chat, code completion or agent loop, asks how to call the Cerebras API or SDK, needs streaming, tool calling, strict JSON schema output or reasoning control, or hits a "model not found" error from a retired Cerebras model ID.
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。