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Found 4,631 Skills
Plan a non-trivial code change end-to-end — size triage (XS–XL), slicing strategy, optional parallel subagent dispatch, per-slice Implement → Test → Verify → Commit discipline. Use for any multi-file change, refactor across files, executing a planned task from any planning source, cross-cutting modification (analytics sweep / i18n / library migration), or when about to write more than ~100 lines. 也用于增量实现 / 切片落地 / 推进已规划任务 / 跨切面改动。Skip only for trivial XS edits and pure documentation / configuration changes.
Build, refactor, debug, or review a Convex backend inside a Next.js app. Use when the user mentions Convex, `convex/nextjs`, `npx convex dev`, `NEXT_PUBLIC_CONVEX_URL`, `useQuery`, `useMutation`, `usePaginatedQuery`, schema/indexes, auth, App Router server components/actions, realtime data, chat, notifications, collaborative features, or deploying Convex with Vercel. Also use when deciding whether Convex is a good fit for a Next.js app that needs reactive shared state. Do not use for generic frontend-only Next.js work or non-Convex backends unless the task is specifically about adopting, migrating to, or evaluating Convex.
Searches the web via Exa’s Search API and returns source URLs (optionally with highlights, full text, summaries, and subpages). Use when the user asks to “search with Exa”, “use Exa”, “find sources/URLs”, “do web research”, “retrieve webpage text”, “get highlights/summaries”, “filter by domain/date/category”, or needs fresh results (news, real-time lookups).
Generate project-specific design system rules for Figma-to-code workflows. Useful for capturing tokens, naming, and lint rules in one source.
Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.
Search existing local, marketplace, GitHub, and web skill sources before creating a new skill. Use when the user wants to create, build, fork, or find a skill for a workflow.
Buffett-style stock screener — "What would Buffett buy now?" Generates 3–5 candidate stocks from a market / sector / preference query via a two-layer model: hard quant filter (ROE 5y ≥15%, debt/asset ≤50%, FCF positive 3y, listed ≥5y, gross margin ≥30%) → qualitative moat scoring (moat 35% / capital allocation 20% / earnings predictability 20% / valuation 15% / runway 10%). Longbridge CLI first, MCP fallback, WebSearch for gaps only. Output: candidate cards with moat-type tag, quantitative highlights, verdict (🟢 likely buy / 🟡 wait for price / 🔴 not at this price), deep-dive CTA to `longbridge-buffett-moat-analyzer`. Mandatory holding-period education + data-source appendix. Disqualifies airlines, pre-revenue biotech, ST, listing<5y. Triggers: "巴菲特会买什么", "巴菲特选股", "巴菲特风格的股票", "护城河选股", "宽护城河股票", "价值投资选股", "10年不动的股票", "定价权强的公司", "巴菲特會買什麼", "巴菲特選股", "護城河選股", "寬護城河股票", "Buffett screener", "what would Buffett buy", "wide-moat screener", "quality compounder screen", "Berkshire-style screen", "pricing-power screen".
Scans any project repository and generates a "Source of Truth" documentation set in the core-knowledge folder, covering architecture, business logic, feature flags, deployment, and any cloud/serverless integrations.
Adding or changing routes in `apps/api`. One source of truth (`defineApiEndpoint` + a Zod schema) becomes an HTTP endpoint, an OpenAPI operation, an MCP tool, and a TS SDK method — descriptions and contracts must be written with all four readers in mind.
Manage and secure company devices with MDM solutions — enroll macOS, Windows, iOS, and Android devices, enforce security policies, and automate software deployment. Use when setting up device management for a growing team.
Agent-native CLI for the s&box game engine (Facepunch Studios, Source 2): project management, scene/prefab editing, material/sound/localization configs, C# code generation, asset graph queries, project validation, and editor launch.
Diagnose landing page → conversion path problems for Google Ads traffic. Separates tracking failures from UX/path failures — the two most commonly confused sources of "low conversion rate." Pulls ad and campaign data via MCP, then reviews landing pages via browser or URL fetch to assess message match, load issues, form friction, and conversion path completeness. Produces a landing-review draft when problems are found.