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Found 9,315 Skills
WNBA data via ESPN public endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, win probability, injuries, transactions, futures, team/player stats, leaders, and news. Zero config, no API keys. Use when: user asks about WNBA scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, team/player statistics, or WNBA news. Don't use when: user asks about NBA (use nba-data), college basketball (use cbb-data), or other sports.
Generates woodworking cut lists from OpenSCAD furniture designs using the woodworkers-lib library. Automates panel dimension extraction from ECHO output for furniture, cabinets, wardrobes, and shelving units. Use when designing furniture with plywood/MDF panels, generating cut lists for CNC routing or manual cutting, or preparing data for sheet optimization tools. Triggers on "generate cut list", "extract panel dimensions", "furniture cut list", "woodworking ECHO output", or when working with planeLeft/planeRight/ planeTop/planeBottom/planeFront/planeBack modules. Works with .scad files using woodworkers-lib library.
Implements Google Cloud Pub/Sub integration in Python by configuring topics, subscriptions, publishing/subscribing, dead letter queues, and local emulator setup. Use when building event-driven architectures, implementing message queuing, or managing high-throughput systems. Triggers on "setup Pub/Sub", "publish messages", "create subscription", "configure DLQ", or "test with emulator". Works with google-cloud-pubsub library and includes reliability, idempotency, and testing patterns.
Expert at detecting stories that are too big and applying splitting heuristics. Use when user describes work that seems large, mentions multiple features in one story, or needs help breaking down requirements. Detects linguistic red flags (and, or, manage, handle) and suggests concrete splitting strategies. Use when: - Story has obvious red flags: "and", "or", "manage", "handle", "including" - User describes multiple features bundled together - Story feels vague or too large - User asks "how to split this story" Do NOT use when: - Story is already small and focused (< 1 day work) - Feature needs layered analysis without obvious split points (use hamburger-method instead) - User asks HOW to implement (use micro-steps-coach instead)
TanStack Table v8 headless data tables for React. Covers column definitions, sorting, filtering (fuzzy/faceted), server-side pagination with TanStack Query, infinite scroll, virtualization (TanStack Virtual), column/row pinning, row expanding/grouping, column resizing, and reusable Shadcn-styled components. Prevents 15 documented errors including infinite re-renders, React Compiler incompatibility, and server-side state mismatches. Use when building data tables, fixing table performance, implementing server-side pagination, adding filtering/sorting, or debugging table state issues.
Use when asked to "7 Powers", "build a competitive moat", "analyze defensibility", "find sustainable advantage", "economic moats", or "Hamilton Helmer framework". Helps identify durable competitive advantages. The 7 Powers framework (created by Hamilton Helmer) reveals the economic structures that protect business value from competition.
Inspects, filters, and maps z-schema validation errors for application use. Use when the user needs to handle validation errors, walk nested inner errors from anyOf/oneOf/not combinators, map error codes to user-friendly messages, filter errors with includeErrors or excludeErrors, build form-field error mappers, use reportPathAsArray, interpret SchemaErrorDetail fields like code/path/keyword/inner, or debug why validation failed.
Enable developers to learn and use Chainlink Runtime Environment (CRE) quickly by referencing filtered CRE docs. Trigger when user wants onboarding, CRE workflow generation (in TypeScript or Golang or other supported languages), workflow guidance, CRE CLI and/or SDK help, runtime operations advice, or capability selection
Production-ready starter project for React + Cloudflare Workers + Hono with core services (D1, KV, R2, Workers AI) and optional advanced features (Clerk Auth, AI Chat, Queues, Vectorize). Complete with planning docs, session handoff protocol, and enable scripts for opt-in features. Use when: starting new full-stack project, creating Cloudflare app, scaffolding web app, AI-powered application, chat interface, RAG application, need complete starter, avoid setup time, production-ready template, full-stack boilerplate, React Cloudflare starter. Prevents: service configuration errors, binding setup mistakes, frontend-backend connection issues, CORS errors, auth integration problems, AI SDK setup confusion, missing planning docs, incomplete project structure, hours of initial setup. Keywords: cloudflare scaffold, full-stack starter, react cloudflare, hono template, production boilerplate, AI SDK integration, workers AI, complete starter project, D1 KV R2 setup, web app template, chat application scaffold, RAG starter, planning docs included, session handoff, tailwind v4 shadcn, typescript starter, vite cloudflare plugin, all services configured
This skill should be used when the user asks to "simulate attacks", "build attack trees", "model exploit chains", "score exploitability", or is running PASTA stage 6. Also triggers when the user asks about attack scenarios, red team simulation, DREAD scoring, or detection gap analysis in a threat modeling context. Part of the PASTA threat modeling methodology (Stage 6 of 7).
This skill should be used when the user asks to "check for non-repudiation privacy risks", "analyze excessive audit logging", "find privacy issues related to accountability", "check for forced identity linking", or mentions "non-repudiation" in a privacy context. Maps to LINDDUN category N. This is the INVERSE of STRIDE repudiation -- here too much proof is the threat.
Refactor Django/Python code to improve maintainability, readability, and adherence to best practices. Transforms fat views into Clean Architecture with Use Cases and Services. Applies SOLID principles, Clean Code patterns, Python 3.12+ features like type parameter syntax and @override decorator, Django 5+ patterns like GeneratedField and async views. Fixes N+1 queries, extracts business logic from views, separates Read/Write serializers, and converts exception-based error handling to explicit return values. Use when refactoring Django code, applying Clean Architecture, or modernizing legacy Django projects.