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Found 3,118 Skills
Operational patterns, templates, and decision rules for time series forecasting (modern best practices): tree-based methods (LightGBM), deep learning (Transformers, RNNs), future-guided learning, temporal validation, feature engineering, generative TS (Chronos), and production deployment. Emphasizes explainability, long-term dependency handling, and adaptive forecasting.
Convert addresses to coordinates (geocoding) and coordinates to addresses (reverse geocoding). Use for location data enrichment or address validation.
Use when configuring QueryClient, implementing mutations, debugging performance, or adding optimistic updates with @tanstack/react-query in Next.js App Router. Covers factory patterns, query keys, cache invalidation, observer debugging, HydrationBoundary, multi-layer caching. Keywords TanStack Query, useSuspenseQuery, useQuery, useMutation, invalidateQueries, staleTime, gcTime, refetch, hydration.
Interact with Google's Gemini model via CLI. Use when needing a second opinion from another LLM, cross-validation, or leveraging Gemini's Google Search grounding. Supports multi-turn conversations with session management.
Three.js 3D building system with spatial indexing, structural physics, and multiplayer networking. Use when creating survival games, sandbox builders, or any game with player-constructed structures. Covers performance optimization (spatial hash grids, octrees, chunk loading), structural validation (arcade/heuristic/realistic physics modes), and multiplayer sync (delta compression, client prediction, conflict resolution).
BDD-Driven Mathematical Content Verification Skill Combines Behavior-Driven Development with mathematical formula extraction, verification, and transformation using: - Cucumber/Gherkin for specification - RSpec for implementation verification - mathpix-gem for LaTeX/mathematical content extraction - Pattern matching on syntax trees for formula validation Enables iterative discovery and verification of mathematical properties through executable specifications.
AI content generation with OpenAI and Claude, callAIWithPrompt usage, prompt storage in app_settings, structured outputs, response format validation, multi-criteria scoring, rate limiting, JSON schema, and AI API best practices. Use when generating content, creating prompts, scoring articles, or working with OpenAI/Claude APIs.
Create client-side forms with react-hook-form, shadcn/ui form components, and server action integration for Next.js/Supabase applications. Use when building forms with validation, error handling, and loading states ('create a form', 'build the settings form', 'add form validation', 'wire up the edit form'). Generates complete form components with Zod schemas, toast feedback, and data-test attributes. Do NOT use for server-side logic (use server-action-builder or service-builder), database schemas (use postgres-expert), or E2E tests (use playwright-e2e).
Creates comprehensive disaster recovery procedures with automated backup scripts, restore procedures, validation checks, and role assignments. Use for "database backup", "disaster recovery", "data restore", or "DR planning".
Bootstrap Python MCP server projects and workspaces on macOS using uv and FastMCP with consistent defaults. Use when creating a new MCP server from scratch, scaffolding a single uv MCP project, scaffolding a uv workspace with package/service members, initializing pytest+ruff+mypy defaults, creating README.md, initializing git, running initial validation checks, or starting from OpenAPI/FastAPI with MCP mapping guidance.
Scaffolds an xUnit integration test project for validating Oracle-to-PostgreSQL database migration behavior in .NET solutions. Creates the test project, transaction-rollback base class, and seed data manager. Use when setting up test infrastructure before writing migration integration tests, or when a test project is needed for Oracle-to-PostgreSQL validation.
Exploratory Data Analysis skill for CSV and parquet datasets with deterministic profiling, drift/anomaly scans, contract generation and validation, and optional memory writeback into skill-system-memory. The implementation is Polars-first (lazy scan for large files and early `--sample` head), includes high-cardinality guards for profile/importance/contract flows, and supports categorical correlation with Cramer's V. Use when building or reviewing tabular fraud/risk/data-quality workflows, profiling new datasets, checking leakage or drift, or saving/validating data contracts.