subagents-orchestration-guide
Orchestrates multi-agent development workflows, managing task decomposition, requirement analysis, and quality assurance for complex software projects.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
361 skills found
Orchestrates multi-agent development workflows, managing task decomposition, requirement analysis, and quality assurance for complex software projects.
Validates Claude Code plugins against architectural standards, checking manifest files, frontmatter, and tool invocation patterns to ensure high-quality, compliant plugin development.
Collaborative PR review using a swarm of three specialized AI agents (Correctness, Health, UX) that discuss findings and reach consensus before posting a structured summary with inline comments.
Evidence-based debugging for Python, Node.js, and Java applications using runtime execution traces and diagnostic MCP tools.
Framework for building AI agents that persist state across multiple context windows, enabling them to complete complex, multi-day coding tasks without losing progress or context.
Audit and validate Claude Code plugins for structural integrity, manifest compliance, and best practice adherence to ensure reliable agent and skill performance.
Expert systematic test design using BVA, equivalence partitioning, decision tables, and combinatorial testing to maximize coverage and minimize redundancy.
Unified AI gateway for building full-stack apps and automating tasks. Access 100+ AI models for content generation, web scraping, app deployment, and Stripe payments with a single API key.
Defense-in-depth protection for Claude Code. Manage security hooks to block dangerous commands, enforce file access controls, and protect sensitive paths across global or project-specific scopes.
Easily configure and add Model Context Protocol (MCP) servers to various AI coding clients like Cursor, Claude, VS Code, and more using an interactive or automated command-line interface.
A powerful CLI for converting web content and search results into LLM-friendly formats like Markdown, text, or HTML using the Jina AI Reader API.
Generate finite-difference stencils, select optimal numerical schemes for PDEs/ODEs, and perform truncation error analysis to improve simulation accuracy.