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Found 1,560 Skills
Strategic guidance for choosing and implementing testing approaches across the test pyramid. Use when building comprehensive test suites that balance unit, integration, E2E, and contract testing for optimal speed and confidence. Covers multi-language patterns (TypeScript, Python, Go, Rust) and modern best practices including property-based testing, test data management, and CI/CD integration.
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
Mass spectrometry toolkit (OpenMS Python). Process mzML/mzXML, peak picking, feature detection, peptide ID, proteomics/metabolomics workflows, for LC-MS/MS analysis.
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.
Backend development agent for Resume Matcher. Handles FastAPI endpoints, Pydantic schemas, TinyDB operations, LiteLLM integration, and Python service logic. Use when creating or modifying backend code.
Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction. Originally from OpenAI's curated skills catalog.
Audit, compare, and document Opik integrations across Python SDK, TypeScript SDK, and OTEL/API backend. Use when adding a new integration, reviewing an existing one, or generating first-pass integration docs and gap analysis.
Turso (Limbo) database helper — an in-process SQLite-compatible database written in Rust. Formerly known as libSQL / libsql. Replaces @libsql/client, libsql-experimental for Turso use cases. Works in Node.js, browser (WASM + OPFS for persistent local storage), React Native, and server-side. Features: vector search, full-text search, CDC, MVCC, encryption, remote sync. SDKs: JavaScript (@tursodatabase/database), Browser/WASM (@tursodatabase/database-wasm), React Native (@tursodatabase/sync-react-native), Rust (turso), Python (pyturso), Go (tursogo). This skill contains all SDK documentation needed to use Turso — do NOT search the web for Turso/libsql docs.
Generate a custom trace annotation web app for open coding during LLM error analysis. Use when the user wants to review LLM traces, annotate failures with freeform comments, and do first-pass qualitative labeling (open coding). Also use when the user mentions "annotate traces", "trace review tool", "open coding tool", "label traces", "build an annotation interface", "review LLM outputs", or wants to manually inspect pipeline traces before building a failure taxonomy. This skill produces a tailored Python web application using FastHTML, TailwindCSS, and HTMX.
Expert patterns for HubSpot CRM integration including OAuth authentication, CRM objects, associations, batch operations, webhooks, and custom objects. Covers Node.js and Python SDKs. Use when: hubspot, hubspot api, hubspot crm, hubspot integration, contacts api.
Setup Spanora AI observability in any project (JavaScript/TypeScript or Python). Use when user asks to "add spanora", "setup spanora", "integrate spanora", "add AI observability", "monitor LLM calls with spanora", "track AI costs", or mentions spanora in the context of adding observability to their project. Detects the language and installed AI SDKs (Vercel AI, Anthropic, OpenAI, LangChain) and configures the optimal integration pattern.