3,490 件 ・ 関連度順
概要と使いどころ
Watch an open PR — fix failing CI, handle the straightforward review comments, and drive it to a mergeable state. Claude Code analog of Cursor's built-in /babysit. Use after opening a PR when the user wants the agent to shepherd it without re-prompting.
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 implementing any feature or bugfix, before writing implementation 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。
Feishu/Lark CLI - official open-source CLI tool from Feishu for AI Agents. Provides 200+ commands across 12 business domains: IM, Docs, Sheets, Base (Bitable), Calendar, Video Meeting, Mail, Tasks, Wiki, Drive, Contacts, Search. Supports both user identity and bot identity authentication. Use when user wants to operate Feishu/Lark resources.
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。
Web data extraction using 55+ Apify Actors for AI-driven scraping. Supports Instagram, Facebook, TikTok, YouTube, Google, and more. Auto-selects best Actor for the task. Structured output in JSON/CSV with rate limiting and ethical scraping guidelines.
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。
DiDi ride-hailing service skill. Supports taxi booking, price estimation, route planning (driving/transit/walking/cycling), order management, driver location tracking, and scheduled rides. Use when user expresses any transportation need including ride-hailing, route queries, or commuting.
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 writing new features, fixing bugs, or refactoring code. Enforces test-driven development with 80%+ coverage including unit, integration, and E2E tests.
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。
适用于 Claude Code 会话的正规评测框架（Evaluation Framework），实现了评测驱动开发（Eval-Driven Development, EDD）原则
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。
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
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。
Go testing patterns including table-driven tests, subtests, benchmarks, fuzzing, and test coverage. Follows TDD methodology with idiomatic Go practices.
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 primary aims, secondary aims, and testable hypotheses from broad biomedical research ideas. Use this skill when a user needs to convert a loose study idea into a tighter protocol-framing structure with clear aim hierarchy, hypothesis discipline, and separation between hypothesis-driven and exploratory components. Always keep aims answerable, non-overlapping, and aligned to the intended evidence type and study 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。
Use this bioinformatics data analysis skill to construct a database-driven lncRNA-mRNA regulatory network from target lncRNA and/or gene lists by projecting shared miRNA evidence from local ceRNA reference tables. It does not infer networks from expression matrices.
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 evaluating investments through Zhang Lei / Hillhouse Capital's long-term research-driven value lens: deep research, do time's friend, invest in great companies, and actively help them grow.
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 evaluating crypto sectors through a Delphi-style thematic research lens: sector maps, tokenomics, narrative timing, and research-driven crypto allocation.
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 evaluating crypto markets through an Arthur Hayes-style liquidity lens: dollar liquidity, funding, risk appetite, cycle psychology, and macro-driven crypto positioning.
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 evaluating Michael Burry-style contrarian, asymmetric, unpopular trades: deep mispricing, short theses, complex assets, forced consensus errors, and catalyst-driven downside/upside.
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 evaluating crypto protocols through a Paradigm-style research lens: mechanism design, protocol fundamentals, L2/MEV/incentives, and research-driven crypto investing.
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 evaluating Joel Greenblatt-style special situations: spinoffs, restructurings, merger securities, forced selling, magic formula quality/value screens, and event-driven mispricing.
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 evaluating Hindenburg-style investigative short reports: evidence chains, promoter networks, stock promotion, related parties, disclosure failures, and catalyst-driven short narratives.
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 project-local verification skill that drives your app the way a user does — any language, framework, or platform. Use for /create-verification-skill, "make a control skill for this repo", or when a project has no scripted way to prove UI/CLI/service behavior.
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 to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with ≤ 1 chart and < 10 rows (use xlsx).
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 the user wants to build a financial model — 3-statement model, DCF valuation, LBO, SaaS unit economics, sensitivity / scenario analysis, debt schedule, or fundraising projections — in Excel. Trigger on: 'financial model', '3-statement model', 'P&L + BS + CF', 'DCF', 'WACC', 'NPV', 'terminal value', 'LBO', 'debt schedule', 'cash sweep', 'MOIC', 'IRR / XIRR', 'sensitivity table', 'scenario analysis', 'ARR model', 'unit economics', 'CAC / LTV', 'cap table forecast'. Output is a single formula-driven .xlsx. This skill is a scene layer on top of officecli-xlsx — it inherits every xlsx v2 rule (4-color code, visual floor, number formats, cache-drift, Known Issues, Delivery Gate minimum cycle). DO NOT invoke for a simple budget tracker, CSV dump, or operational KPI sheet — route those to officecli-xlsx base.
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。
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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 deterministic, verifiable data visualizations with D3.js (v6). Generate standalone HTML/SVG (and optional PNG) from local data files without external network dependencies. Use when tasks require charts, plots, axes/scales, legends, tooltips, or data-driven SVG output.
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。
Drive feature development using Outside-In TDD with Hexagonal Architecture. Design emerges through inline code, in-memory fakes, interface extraction, and deferred I/O. Use when building features, writing tests, or structuring backend services. Triggers on: TDD, outside-in, hexagonal, ports and adapters, emergent design, acceptance test, component test, walking skeleton, in-memory fakes, component, contract test, adapter, fast tests, sub-second feedback. Language-agnostic (Go, Rust, Python, TypeScript, Java, C#).
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。