「fea」の検索結果

Conduct a structured competitive analysis comparing UX patterns, features, strengths, and gaps across rival products.

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, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't converting,' 'improve conversions,' 'why isn't this page working,' 'my landing page sucks,' 'form abandonment,' 'nobody's converting,' 'low conversion rate,' or 'this page needs work.' Use this even if the user just shares a URL and asks for feedback. For post-signup activation, see onboarding.

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。

Google API integration for blog performance: PageSpeed Insights, CrUX Core Web Vitals with 25-week history, Search Console performance, URL Inspection, Indexing API, GA4 organic traffic, NLP entity analysis for E-E-A-T, YouTube video search for embedding, and Google Ads Keyword Planner. Progressive feature availability based on credential tier (API key, OAuth/service account, GA4, Ads). Shares config with claude-seo at ~/.config/claude-seo/google-api.json. Use when user says "google data", "page speed", "core web vitals", "search console", "indexation", "GA4", "keyword research", "nlp entities", "blog performance", "youtube search", "google api setup".

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 end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

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Read the feature roadmap, find the next unplanned phase, brainstorm it, and update the roadmap with the resulting document name

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 making a small, quick change — a bug fix, typo, minor feature, tweak, or anything the user calls "vibe coding" — that looks like 1-3 files in the same module

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。

Turn a feature request into a minimal, file-level implementation plan before any 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。

Use when 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets. Each prompt edit is a git-SHA-versioned, numbered experiment with a hypothesis; tweets are attributed to the version live at post time and judged on median impressions per tweet. Also mines per-tweet impression data for content-feature signals (angle / length / topic) that feed the next prompt edit. North-star = impressions per tweet. Triggers — "改 X 的 prompt", "X 内容改进", "哪版 prompt 最好", "什么内容 impression 高", "improve my tweets", "A/B test the X prompt", "/wjs-x-improving-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。

Operate the user's existing, logged-in Chrome locally or on an explicitly connected remote machine. Read complete page content with actionable refs and links, perform grouped actions, and use screenshots in persistent browser sessions. Skip static public pages and pure REST APIs.

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Build production-grade frontend interfaces with distinctive aesthetics and working code. Use for UI components, pages, or frontend features. Don't use for backend/API work or usability-only audits.

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 or improve code — one skill, four modes: bug/security review (default), performance, clean-code audit, slop cleanup. Pass mode:review|perf|clean|cleanup or infer. Don't use for writing features or generating tests (use test-coverage).

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。

Validate app/startup ideas with market, feasibility, commercial, and open-source competitor analysis. Use when asked to evaluate, validate, or score a product idea. Don't use for PRDs, go-to-market plans, or investor decks.

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AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score 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。

Applies the product philosophy and leadership frameworks of Steve Jobs, co-founder and CEO of Apple Inc. Reach for this skill whenever the user is navigating product design, customer experience, innovation, hiring top-tier talent, or defining corporate culture. Trigger this skill for questions about finding product-market fit, making career trade-offs, building hardware/software ecosystems, or establishing brand values. Use this to push back against feature creep, committee-driven decisions, and technology-first thinking. Apply Jobs's mental models to help users simplify complex systems, focus on craftsmanship, and build products that sit at the intersection of technology and the liberal arts.

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Applies the pragmatic, financially-grounded reasoning of Marian Ilitch (co-founder of Little Caesars Pizza). Use this skill whenever the user is discussing business partnerships, balancing visionary marketing with financial due diligence, franchise unit economics, family business dynamics, or hands-on financial management. Trigger this when users face trade-offs between rapid growth and profitability, need to enforce unit-level accountability, or are paralyzed by the fear of making mistakes. Apply her principles to ground lofty ideas in tactile, common-sense financial reality.

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 reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.

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。

Turn a feature request into a minimal, file-level implementation plan before any 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。

Suede-affiliated in-product monetization design for paywall screens, feature gates, trial-expiry states, usage-limit prompts, and free-to-paid upgrade moments. Use when the user needs trigger timing, message structure, plan presentation, or experiment design after users have experienced value. NOT FOR: public pricing pages (use suede-site-alchemy), tier architecture (use suede-pricing), or cancellation and save flows (use suede-churn-prevention).

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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。

Perform QA testing on running web applications using cmux_browser. Covers functional testing, accessibility auditing, edge case testing, and structured reporting with evidence. **Triggers — use this skill when:** - User says "test this", "QA this", "QA this PR", "check the app" - User says "run QA", "verify the build", "test the UI" - User asks to "check if it works", "validate the feature" - User says "acceptance testing", "smoke test", "regression test" - User asks to "find bugs", "test for bugs", "break it" - User wants to "check accessibility", "run axe", "a11y audit" **Covers:** Any web application accessible via HTTP. Uses cmux_browser (Playwright under the hood) for all browser interactions.

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。

Parse git history and produce or update a CHANGELOG.md following the Keep a Changelog convention. Supports Conventional Commits, basic prefix conventions, and unstructured commit messages. Intelligently categorizes changes, detects breaking changes, links to PRs/issues, and handles both initial generation and incremental updates. **Triggers — use this skill when:** - User asks to "generate", "create", "update", or "write" a changelog - User mentions "CHANGELOG", "changelog", "release notes" - User says "document changes", "what changed since last release" - User wants to "prepare a release" and needs a changelog entry - User asks to "clean up" or "reformat" an existing changelog **Covers:** Any git-based project. Handles Conventional Commits (feat/fix/chore), Angular convention, basic prefixes (Add/Fix/Remove), and freeform commit messages. Outputs Keep a Changelog format with optional Common Changelog enhancements.

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。

Structured planning mode for AI agents working on coding and development tasks. Produces a comprehensive plan BEFORE any code is written or changed, including task decomposition, architecture decisions, risk assessment, and approval gates. Use this skill whenever the user says "/plan", "plan mode", "plan first", "think before coding", "make a plan", or asks the agent to plan before executing. Also trigger when the user describes a complex multi-step development task and would clearly benefit from structured planning before implementation — especially for tasks involving new features, refactors, migrations, infrastructure changes, or multi-file edits. When in doubt, plan. A few minutes of planning prevents hours of rework.

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 up-to-date documentation for any library, framework, API, or service into context. Use when the user wants to look up API references, check function signatures or required fields, find feature-specific docs, or verify how an external tool actually works. Triggers for queries about third-party libraries like Stripe, SQLAlchemy, Tailwind, FastAPI, shadcn, Drizzle, Hono, Better Auth — any time the answer lives in official docs rather than in the project codebase. Use this instead of guessing from trained knowledge, which is stale.

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。

Fix bugs and broken behavior when there is enough evidence to act on a repair path. Use for errors, crashes, incorrect results, API failures (500, 404, 403), CORS problems, database exceptions, broken rendering, duplicated or wrong data, off-by-one mistakes, timezone/date bugs, broken forms, config-caused runtime failures, and regressions. Trigger when the user wants the bug repaired and the conversation already contains a clear failing area, a reproducible failing test, a concrete error path, or a prior diagnosis to implement. Do NOT use for new features, pure explanation, architecture discussion, broad research, or bug reports where the main need is figuring out why the behavior happens — use diagnose for that.

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。