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概要と使いどころ
Product marketing strategy, positioning, and competitive intelligence for B2B SaaS. Use when defining ICP, developing positioning (April Dunford method), creating messaging hierarchy, building competitive battlecards, planning sales enablement, sizing market opportunity (TAM/SAM/SOM), mapping PMF spectrum, designing growth loops, writing PR/FAQ working backwards docs, or creating a product marketing context document.
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。
Helps users report issues with GTM skills — incorrect advice, broken activation, missing context, or unexpected behavior. Guides through gathering the right details (skill name, prompt used, actual vs expected output, context files present) and generates a properly formatted GitHub issue. Use when users say: 'this skill gave wrong advice', 'report a bug', 'file an issue', 'skill isn't working', 'wrong output', 'skill didn't activate', 'bad recommendation', 'incorrect behavior', or 'something is broken'.
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 whenever a user wants to create a presentation outline, slide deck structure, storyboard slides, or plan a deck for any context (talk, boardroom, email report). Also use when the user mentions "help me structure my presentation", "slide outline", "storyboard my deck", "presentation flow", "plan my slides", "key message for my deck", "pyramid principle for slides", "what slides should I include", or "help me tell a story with slides". This skill walks through a complete, interactive process in two phases: Phase 1 (setup → key message → storyboard) saves a slide-outline.md; Phase 2 (interactive per-slide copy drafting → polish) saves a slide-copy.md that is linked from slide-outline.md. Trigger even when the user only mentions a vague intent like "I need to present X to my team" or "help me put together a board deck".
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。
Prepare a meeting-ready estate and tax brief for a household — opens with what was discussed and actioned since the last meeting (Wealthbox/Redtail/Salesforce), pulls the estate plan from Wealth.com (structure, key documents and whether each is signed, balance sheet) plus the prior-year tax look-back and that year's tax constants, and cross-checks it against how accounts are titled and beneficiaried at the custodian and portfolio platforms (Schwab, Orion/Addepar, iCapital) — surfacing unfunded trusts, titling mismatches, and beneficiary gaps as a prioritized flags list. The only write is creating advisor-approved follow-up tasks in the CRM (optional, approved first). Never drafts trust language, gives legal advice, retitles an account, or changes a beneficiary. Triggers on "estate brief", "estate meeting brief", "estate and tax brief", "estate plan review for [client]", "check [client]'s trust funding", "are [client]'s beneficiaries up to date", or "does [client]'s estate plan match their accounts".
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。
Front-door router for the product-shots ecosystem. Clarifies underspecified visual creation requests through a 4-stage state machine, injects platform visual DNA (Amazon + 7 social platforms) and industry visual DNA (7 industries), enforces a unified Negative Constraints prompt patch, then routes to one of five downstream business skills (product-shots-main-image / product-shots-detail-page / product-shots-multi-angle / product-shots-ad-creative / product-shots-social-post). Use when the user says "I need a product image", "design something for my listing", "I need content for Instagram", "make an ad for me", "make a cover image", "create a post", "做一个商品图", "帮我做个详情页", "帮我做个广告图", "做一个社媒图", "做一张图", "帮我设计", "做个封面" — i.e. any underspecified visual creation request that needs clarification before generation. This is the intent-routing hub of the product-shots ecosystem.
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。
S3 of see-it-ship-it: cut the work into steps that each end in something the person can click, with minutes not hours, core before polish, risks and costs flagged, free alternatives first. Internal planning is written in iLang; the person never sees it.
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。
Cross-cutting: when something breaks. Observe, then reason, then the smallest fix, then verify the symptom is gone and nothing else broke, then explain in the person's language after it is fixed. Never show a raw error to a beginner; never blame them.
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。
Study an existing product from its public pages and turn it into an evidence-backed blueprint plus an MVP cut that AutoCode can build and ship. Use this whenever the user wants something "like X", "similar to X", "my own version of X", "a clone of X", names a competitor they admire, or pastes a product URL and asks to build it — even when they never say "research" or "analyze". Public sources only (official site, docs, pricing, changelog, public repos, app-store listings, public reviews); no logins, captures, decompiling, or copying of code, copy, images or brand.
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。
Decomposition methodology for pm agent — turns an approved ARCH document into a Beads task list with explicit dependencies, time-boxes, and acceptance criteria. The pipeline can only orchestrate work it can see; this skill defines what "seeable work" looks like.
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 images, websites, and Figma files to extract their design and generate a `design.md` with token system, component inventory, and reconstruction notes. Use this skill whenever the user wants to understand, document, replicate, or audit the design of something visual: a screenshot, a URL, a Figma link, a Pinterest reference, a mockup, a competitor's site, a component, a dashboard, a landing page. Also when they ask 'extract the design system from X', 'document the style of Y', 'analyze this visually', 'convert this image into tokens', 'help me replicate this design', 'what palette does this site use', 'how is this built'. Also for single elements: 'copy this navbar', 'recreate this illustration', 'give me a prompt to regenerate this graphic' — element mode outputs a focused element.md, with token-grounded image-model prompts when the element is visual art. If the user brings any visual source and wants to understand it at a design level — this skill should activate.
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。
Light Blender scenes professionally — three-point setups, HDRI environments, studio/cinematic/dramatic configurations, light groups, color temperature, soft vs hard shadows. Use whenever the user asks to "light the scene", "set up lighting", "make it look cinematic / dramatic / studio / outdoor / sunset", "add a key light", "use HDRI", or any lighting-related request. Make sure to use this skill even if the user does not say "light" — also covers "make it look professional", "studio shot", "moody atmosphere", "golden hour", "rim light". Pairs with blender-materials (lighting reveals materials) and blender-cameras (lighting + composition together = shot).
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。
Determine whether front/side/back/top reference templates can be satisfied by one rigid 3D Blender model, choose a canonical source policy, and emit constraints before rebuilding. Use when multiview bboxes, silhouettes, part counts, or depth ratios contradict each other.
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 self-refinement loop when a Blender result is subpar, user expectations are not met, validation fails, or repeated issues reveal missing skill depth. The loop diagnoses the gap, decides whether to repair the artifact or acquire/refine a generic skill, sanitizes lessons for reusable publication, updates docs/versioning, validates, and prepares stage/commit/push when explicitly requested.
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 the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural expansion, and in-body verification. Use this skill for literature-finding and paper-retrieval tasks of any kind, including clinical, biomedical, drug, gene, disease, and other life-science questions, whether the answer is a single paper or a full multi-paper set. The index is reached only through the `firecrawl_research_*` MCP tools or the `firecrawl research` CLI subcommands. Calling `firecrawl_search` with its `categories` option set to `["research"]` is a different feature — it filters ordinary web search to research-affiliated websites (the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites) and returns page results from them, without querying the paper records in this index.
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。
Forget data stored in Cognee memory. Use when the user asks to forget, delete, or remove something from memory (e.g. "forget what we talked about tennis", "delete that from memory"). Finds the documents in the current dataset whose raw content matches what the user wants forgotten and deletes them.
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。
Diagnoses slow services and regressions via profiling and tail-latency math. Use when diagnosing slow services, profiling hot paths, or judging whether an optimization is real.
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。
Mines recent papers, blogs, and newsletters for methods worth stealing, with graded evidence. Use when finding recent papers or techniques for RAG, evals, or agents.
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。
Tests whether data supports a claim: A/B significance, peeking, sample size, eval error bars, multiple comparisons. Use when deciding if results are significant.
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。
Grounding-theory primitives for common ground and repair. Use when deciding whether to ask a clarifying question, confirming a handoff was understood, or fixing multi-turn drift.
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 whether scores, benchmarks, LLM judges, or surveys measure what we think. Use when a benchmark is saturated, a score unreliable, or judge agreement untrustworthy.
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。
Proves protocols and agent workflows with TLA+, P, Alloy, Z3, Dafny, or Lean. Use when proving protocols correct, writing TLA+ specs, or verifying LLM proofs.
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。
Tests whether changes caused a metric move without a clean A/B test: DiD, synthetic control, IV, RDD. Use when asking did a launch cause lift.
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 an agent must stand up the OpenClaw↔Cognee integration with FalkorDB as the vector + graph store. This skill is SELF-CONTAINED — it tells you to generate every file (Dockerfile, sitecustomize.py, docker-compose.yaml) from the exact contents below, build a custom Cognee image that loads the FalkorDB adapter, run Cognee + FalkorDB together, self-verify the adapter registered, and point the OpenClaw cognee plugin at it (including per-agent graphs). No other files in this directory are required.
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 configuring the Cognee connection, checking whether Cognee is ready or connected, or diagnosing a local or cloud Cognee instance from Antigravity.
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。