> browse/registry

> mcp-builder

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

> polyglot-test-agent

Generates comprehensive, workable unit tests for any programming language using a multi-agent pipeline. Use when asked to generate tests, write unit tests, improve test coverage, add test coverage, create test files, or test a codebase. Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more. Orchestrates research, planning, and implementation phases to produce tests that compile, pass, and follow project conventions.

> dataverse-python-usecase-builder

Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

> eval-driven-dev

Add instrumentation, build golden datasets, write eval-based tests, run them, root-cause failures, and iterate — Ensure your Python LLM application works correctly. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM. Use for making sure an LLM application works correctly, catching regressions after prompt changes, fixing unexpected behavior, or validating output quality before shipping.

> dataverse-python-production-code

Generate production-ready Python code using Dataverse SDK with error handling, optimization, and best practices

> bigquery-pipeline-audit

Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.

> python-mcp-server-generator

Generate a complete MCP server project in Python with tools, resources, and proper configuration

> dataverse-python-quickstart

Generate Python SDK setup + CRUD + bulk + paging snippets using official patterns.

> folder-structure-blueprint-generator

Comprehensive technology-agnostic prompt for analyzing and documenting project folder structures. Auto-detects project types (.NET, Java, React, Angular, Python, Node.js, Flutter), generates detailed blueprints with visualization options, naming conventions, file placement patterns, and extension templates for maintaining consistent code organization across diverse technology stacks.

> dataverse-python-advanced-patterns

Generate production code for Dataverse SDK using advanced patterns, error handling, and optimization techniques.

> comment-code-generate-a-tutorial

Transform this Python script into a polished, beginner-friendly project by refactoring the code, adding clear instructional comments, and generating a complete markdown tutorial.

> pydantic-ai

Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.

> astropy

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

> pipecat-friday-agent

Build a low-latency, Iron Man-inspired tactical voice assistant (F.R.I.D.A.Y.) using Pipecat, Gemini, and OpenAI.

> agent-framework-azure-ai-py

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.

> apify-actorization

Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

> async-python-patterns

Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

> atheris

Atheris is a coverage-guided Python fuzzing framework built on libFuzzer for finding bugs, crashes, and security vulnerabilities in pure Python code and Python C extensions. It provides AddressSanitizer integration for detecting memory corruption, buffer overflows, and use-after-free errors. Assists with writing fuzz harnesses, configuring sanitizers, managing corpora, running fuzzing campaigns, and setting up Docker-based fuzzing environments. Covers instrumentation of Python imports, parallel

> testing-handbook-skills

Application security testing toolkit from the Trail of Bits Testing Handbook. Helps the agent set up fuzzing campaigns, write fuzz harnesses, run coverage-guided fuzzers (libFuzzer, AFL++, cargo-fuzz, Atheris, Ruzzy), and triage crashes. Covers memory-safety sanitizers (AddressSanitizer, UBSan, MSan), static analysis with Semgrep and CodeQL, cryptographic validation using Wycheproof test vectors, and constant-time verification. Use when testing C, C++, Rust, Python, or Ruby code for vulnerabilit

> tmux

Remote-control tmux sessions for interactive CLIs by sending keystrokes, capturing pane output, and managing terminal multiplexer windows. Enables parallel coding-agent orchestration, background process management, and REPL interaction via sockets. Use when the agent needs to launch, monitor, or coordinate long-running terminal processes, run multiple agents in parallel, interact with a Python REPL, or scrape live shell output from a persistent session.