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Found 1,228 Skills
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
IDA Pro Python scripting for reverse engineering. Use when writing IDAPython scripts, analyzing binaries, working with IDA's API for disassembly, decompilation (Hex-Rays), type systems, cross-references, functions, segments, or any IDA database manipulation. Covers ida_* modules (50+), idautils iterators, and common patterns.
Best practices for Pandas data manipulation, analysis, and DataFrame operations in Python
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.
Professional PDF solution. Create PDFs using HTML+Paged.js (academic papers, reports, documents). Process existing PDFs using Python (read, extract, merge, split, fill forms). Supports KaTeX math formulas, Mermaid diagrams, three-line tables, citations, and other academic elements. Also use this skill when user explicitly requests LaTeX (.tex) or native LaTeX compilation.
A Python package useful for chemistry (mainly physical/analytical/inorganic chemistry). Features include balancing chemical reactions, chemical kinetics (ODE integration), chemical equilibria, ionic strength calculations, and unit handling. Use when working with chemical equations, reaction balancing, kinetic modeling, equilibrium calculations, speciation, pH calculations, ionic strength, activity coefficients, or chemical formula parsing.
The industry standard library for machine learning in Python. Provides simple and efficient tools for predictive data analysis, covering classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
Use when generating a new SDK from an OpenAPI spec. This is the PRIMARY skill for SDK generation. Triggers on "create SDK", "generate SDK", "new SDK", "quickstart", "TypeScript SDK", "Python SDK", "Go SDK", "Java SDK", "generate TypeScript", "generate Python", "generate Go", "make SDK", "build SDK", "SDK from OpenAPI", "SDK from spec", "initialize SDK project".
LangChain workflows for `create_agent`, LCEL chains, `bind_tools`, middleware, and structured output with production-safe orchestration. Use when implementing or refactoring LangChain application logic in Python or TypeScript.
System architecture guidance for Python/React full-stack projects. Use during the design phase when making architectural decisions — component boundaries, service layer design, data flow patterns, database schema planning, and technology trade-off analysis. Covers FastAPI layer architecture (Routes/Services/Repositories/Models), React component hierarchy, state management, and cross-cutting concerns (auth, errors, logging). Produces architecture documents and ADRs. Does NOT cover implementation (use python-backend-expert or react-frontend-expert) or API contract design (use api-design-patterns).
Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns. Use PROACTIVELY for FastAPI development, async optimization, or API architecture.
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.