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Found 2,042 Skills
Use when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean / Six Sigma / Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations.
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
Build AI agent UIs using the AG-UI protocol with pydantic-ai (Python backend) and CopilotKit (React frontend). Use when creating agentic chat interfaces, human-in-the-loop workflows, generative UIs with state management, tool-based rendering, shared state between frontend and backend, or predictive state updates. Covers FastAPI integration, state events (StateSnapshotEvent, StateDeltaEvent, CustomEvent), useCoAgent hooks, useCopilotAction for tool rendering, and real-time agent-frontend synchronization.
Find focused, runnable Deepgram recipes for a specific feature × language. Use whenever someone wants a minimal working code snippet for ONE feature (transcribe URL, diarize, smart-format, voice agent connect, etc.) rather than a full starter app. Recipes are under 50 lines, read DEEPGRAM_API_KEY from env, and ship with a runnable example_test. Covers Python, JavaScript, Go, .NET, Java, Rust, and the Deepgram CLI.
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
Guidance for converting COBOL programs to modern languages (Python, Java, etc.) while preserving exact behavior and data format compatibility. This skill should be used when modernizing legacy COBOL applications, converting COBOL business logic to modern languages, or ensuring byte-for-byte output compatibility between COBOL and its replacement.
FastAPI framework mechanics and advanced patterns. Use when configuring middleware, creating dependency injection chains, implementing WebSocket endpoints, customizing OpenAPI documentation, setting up CORS, building authentication dependencies (JWT validation, role-based access), implementing background tasks, or managing application lifespan (startup/shutdown). Does NOT cover basic endpoint CRUD or repository/service patterns (use python-backend-expert) or testing (use pytest-patterns).
Comprehensive BDD testing with Cucumber and Gherkin syntax. Use when writing feature files (.feature), step definitions, hooks, or implementing Behaviour-Driven Development. Covers Gherkin keywords (Feature, Scenario, Given/When/Then, Background, Scenario Outline, Rule), step definition patterns for Ruby/JavaScript/Java/Python, hooks (Before/After/BeforeAll/AfterAll), tags, data tables, doc strings, and best practices. Triggers on cucumber, gherkin, BDD, feature files, step definitions, acceptance testing, executable specifications.
Aspire orchestration for cloud-native distributed applications in any language (C#, Python, Node.js, Go). Handles dependency management, local dev with Docker, Azure deployment, service discovery, and observability dashboards. Use when setting up microservices, containerized apps, or polyglot distributed systems.
Use Desktop Commander MCP (typically tools like `mcp__desktop-commander__*`) to manage local files and long-running processes: read/write/search files, apply precise edits, work with Excel/PDFs, run terminal commands and interact with REPLs (Python/Node/SSH/DB), inspect/terminate processes, and review tool call history. Use when the task requires doing real work on the machine (editing code/configs, searching a repo, analyzing CSV/Excel, generating/modifying PDFs, running commands with streaming output).
A Pythonic interface to the HDF5 binary data format. It allows you to store huge amounts of numerical data and easily manipulate that data from NumPy. Features a hierarchical structure similar to a file system. Use for storing datasets larger than RAM, organizing complex scientific data hierarchically, storing numerical arrays with high-speed random access, keeping metadata attached to data, sharing data between languages, and reading/writing large datasets in chunks.
HTTP API testing for TypeScript (Supertest) and Python (httpx, pytest). Test REST APIs, GraphQL, request/response validation, authentication, and error handling.