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概要と使いどころ
Fetch transcripts from YouTube videos. Use when user says "get transcript", "fetch transcript", "YouTube transcript", "pull captions", "what does this video say", or pastes a YouTube URL and wants the text content extracted. Also use when user wants to research or summarize a YouTube video.
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。
Obsidian Claw is an ontology-building skill that transforms chaotic notes, raw ideas, voice dumps, and meeting transcripts into a structured, interlinked knowledge base inside Obsidian. Use this skill whenever the user wants to: add new notes to their Obsidian vault and auto-link them to existing ones, build a concept graph or knowledge map, surface forgotten related discussions ("you talked about this with investor X 3 months ago"), extract entities and relationships from unstructured text, or turn scattered thoughts into wiki-style atomic notes. Also trigger when the user mentions "second brain", "Zettelkasten", "PKM", "personal knowledge management", "note graph", "knowledge graph", "link my notes", "organize my vault", "connect ideas", or asks Claude to "remember" something across sessions using Obsidian. If the user is doing anything with Obsidian notes and wants intelligence layered on top, use this 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。
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
Close the loop on a Caveman learn report — review the ranked token sinks and apply cost-lowering fixes (trim config, offload recurring context to cavemem) with per-edit consent. Use when the user runs "caveman learn", asks to lower their agent's token cost, wants to trim a heavy CLAUDE.md, or wants to offload context they re-paste every session into cavemem.
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 this skill when authoring INVEST-compliant Salesforce user stories from gathered requirements: shaping the As-A/I-Want/So-That stem, writing Given-When-Then acceptance criteria, sizing complexity (S/M/L/XL), splitting stories that are too large for a sprint, and emitting handoff metadata that names the next downstream agent. Trigger keywords: user story, INVEST, story splitting, story sizing, As-A I-Want So-That, story handoff, recommended_agents, backlog item shape. NOT for requirements elicitation or stakeholder interviews (use admin/requirements-gathering-for-sf). NOT for the Given-When-Then technique itself in depth (use admin/acceptance-criteria-given-when-then). NOT for UAT test case design (use admin/uat-test-case-design). NOT for backlog prioritization (use admin/moscow-prioritization-for-sf-backlog).
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 a fully customized Amazon Connect AI agent PoC bundle (Lambda handlers, OpenAPI spec, AI agent prompt, Contact Flows, CloudFormation/CDK infrastructure, FAQ knowledge base) from a ~15-minute structured interview — or from a requirements doc, a flow sketch, or an existing flow/prompt you want to improve. Use when the user wants to build, scaffold, prototype, or refine an Amazon Connect contact center, an AI voice/chat agent on Amazon Connect, or any contact-center use case that mentions Connect, Lex, Wisdom, Q in Connect, "AI agent domain", or Agentic CX Designer (ACXD, Connect Customer, the Agentic CX block — the ACXD runtime target). Also triggers on "I have a requirements document", "turn this flow sketch into a Contact Flow", or "improve my existing Contact Flow / AI prompt". Produces 6 asset packages on local disk.
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。
Cold-start brownfield reconnaissance — reconstruct the WHOLE game design from an existing codebase when there is no GDD yet. Optionally accepts a free-text game description, notes, or reference links to guide and enrich the scan. Use when the user wants to build the game design from the code, e.g. "create a GDD from this codebase", "reconstruct the design from our code — it's a roguelike deck-builder", "build a design doc from the existing project, here are some refs". Scans the project (engine auto-detected), extracts a system roster + dependency graph and content-type schemas / resources / entities into one passive .unikit/gamedesign/RECON.md, tags every fact provenance:extracted-from-code, and writes a mandatory Intent Gap for what code cannot know (pillars, fantasy, the "why"). Writes nothing else and calls no skill. STRICTLY whole-project cold-start — if a GDD already exists and the user wants a specific part investigated, use /unikit-gd-explore (code lens); to author the GDD use /unikit-gd-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。
Add a short, project-specific rule, convention, or override to .unikit/RULES.md — the quick-capture inbox for this project's rules (common rules always load; topic files in .unikit/rules/ load when the work matches), automatically loaded by /unikit-implement (later promotable via /unikit-memory migrate-rules). Works only with a rule typed as a prompt — it does NOT read files, folders, URLs, or PDFs. Cross-checks against the knowledge base (RULES_INDEX.md) to avoid duplicating core/stack entries. Use for fast, one-line conventions and corrections: "add a rule", "remember this", "convention", "always do X", "never use Y", "from now on do Z", or when the user corrects agent behavior and wants it remembered; also "split the rules into topics" (optimise), "clean up the rules" (prune), "shorten RULES.md" (compact). If the user points to a source (file, folder, URL, PDF, article, book) or wants to research/document framework usage, use /unikit-memory; for architecture decisions use ARCHITECTURE.md.
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 or update a strategic project roadmap with major milestones. Generates .unikit/ROADMAP.md — a high-level checklist of project goals. Use when user says "roadmap", "project plan", "milestones", "what to build next", "strategic goals", or wants to see big-picture progress. Also use when the user asks to check which milestones are done or wants to plan the overall project direction.
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。
Manage the project's TODO list in .unikit/TODO.md — capture work to remember for later, not to do right now. Use it to add a deferred item, mark a todo complete, or show the list. The point is to park tasks you don't want to act on in the moment but don't want to forget. Trigger on "add a todo", "add this to the todo list", "put X on the todo list", "remind me to...", "don't forget to...", "note this for later", "we should do this later", "mark this todo done", "what's on the todo list", "what's left to do". This is only for parking reminders — to actually plan or do the work now, use /unikit-plan, /unikit-implement, or /unikit-devcontext.
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。
When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' 'who is my target audience,' 'describe my product,' 'ICP,' 'ideal customer profile,' or wants to avoid repeating foundational information across marketing tasks. Use this at the start of any new project before using other marketing skills — it creates `.agents/product-marketing.md` that all other skills reference for product, audience, and positioning context.
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。
When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.
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 this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.
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。
Routes LLM requests to a local model first (Ollama, LM Studio, llamafile), validates the response quality, and escalates to cloud only when the local result fails. Tracks local vs escalated vs cloud outcomes in a persistent dashboard. Use when: (1) user asks to run a task with a local model first, (2) user wants to reduce cloud API costs or keep requests private, (3) user wants post-outcome quality validation before committing to a local result, (4) user asks to see token savings or the routing dashboard, (5) any request where local-vs-cloud routing should be decided automatically with a quality gate. Supports Ollama, LM Studio, and llamafile as local providers.
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。
Accept or apply for a task on OpenAnt. Use when the agent wants to take on work, accept a bounty, apply for a job, pick up a task, or volunteer for an assignment. Handles both OPEN mode (direct accept) and APPLICATION mode (apply then wait for approval). Covers "accept task", "take this task", "apply for", "pick up work", "I want to do this".
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-driven development with a red-green-clean loop, running mix credo on the whole project at the end of every vertical slice so standards violations are fixed in the slice that introduced them instead of piling into a refactor at the end. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.
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 building a frontend and the user wants a distinctive / animated / "wow" UI element in React + Tailwind - e.g. a laptop-opening-on-scroll, 3D tilt card, spotlight hero, animated beams/background, marquee, bento grid, interactive globe, meteor/particle background, shimmer button, or scroll/reveal motion. Fetches the showpiece component LIVE at build time from code libraries (Aceternity, Magic UI, Cult UI, ReactBits, 21st.dev) via their shadcn registry command (or WebFetch fallback), then adapts it to brand tokens. Also covers plain components (button, form, modal, table, etc.) as a fallback. Triggers on "make it pop", "something flashy", "animated [X]", "like [cool site]", "build a [showpiece] section". NOT for non-React stacks. Inspiration galleries (Dribbble/godly) are visual reference only - never code.
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。
Triage a bug or issue by exploring the codebase to find root cause, then create a GitHub issue with a TDD-based fix plan. Use when user reports a bug, wants to file an issue, mentions "triage", or wants to investigate and plan a fix for a problem.
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-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.
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。
Find 10x product opportunities and high-leverage improvements. Use when user wants strategic product thinking, mentions '10x', wants to find high-impact features, or says 'what would make this 10x better', 'product strategy', or 'what should we build next'.
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。
Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.
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。
Delegate implementation to Codex, grok, or cursor-agent, then review the diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants Codex, the grok 后端, or cursor-agent to write code, mentions handing off / delegating implementation to one of those backends, asks to work through a plan or spec unit-by-unit with an implementation backend doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer runs in the real environment behind backend-specific git and publication boundaries, must not be pointed at a full test suite by default, and its self-report is a claim rather than evidence. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the engineering-mode skill when it is installed.
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。
Delegate implementation work to a dedicated implementer subagent, then review its diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants a separate model to write code, mentions handing off / delegating implementation, asks to work through a plan or spec unit-by-unit with a subagent doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer self-reports success, must not run the full test suite by default, must not touch git, and its report is a claim rather than evidence. Cursor port of implementation-loop. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the cursor-engineering-mode skill (it ships in this same plugin).
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。