342 boosters for "agents" — open source, verified from GitHub, ready to install
"name": "dotnet-skills", "description": "Comprehensive .NET development skills and agents for Claude Code - covering C#, F#, Akka.NET, Aspire, testing frameworks, and specialized tools", "name": "Aaron Stannard",
Review the most recent commit (or the commit specified in ) in a single pass, using FXA-specific knowledge. Use Read and Grep to examine the changed files and their surrounding context. Look at imports, callers, and related types to understand the full picture before judging. Evaluate the diff throu
Use this agent when documentation in the `architecture/` directory needs to be updated or created for a specific file after implementing a feature, fix, refactor, or behavior change. Launch one instance of this agent per file that needs updating. This agent maintains the *contents* of architecture documentation files — it does not decide which files exist or how the directory is organized.\n\nExamples:\n\n- Example 1:\n Context: A developer just finished implementing OPA policy evaluation in the sandbox system.\n user: "I just finished implementing the OPA engine in crates/openshell-sandbox/src/opa.rs. Update architecture/sandbox.md to reflect the new policy evaluation flow."\n assistant: "I'll launch the arch-doc-writer agent to update the sandbox architecture documentation with the new OPA policy evaluation details."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/sandbox.md>\n\n- Example 2:\n Context: A refactor changed how the HTTP CONNECT proxy handles allowlists.\n user: "The proxy allowlist logic was refactored. Please update architecture/proxy.md."\n assistant: "Let me use the arch-doc-writer agent to synchronize the proxy documentation with the refactored allowlist logic."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/proxy.md>\n\n- Example 3:\n Context: After implementing a new CLI command, the assistant proactively updates docs.\n user: "Add a --rego-policy flag to the CLI."\n assistant: "Here is the implementation of the --rego-policy flag."\n <implementation complete>\n assistant: "Now let me launch the arch-doc-writer agent to update the CLI architecture documentation with the new flag."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/cli.md>\n\n- Example 4:\n Context: A user wants high-level overview documentation for a non-engineering audience.\n user: "Update architecture/overview.md with a non-engineer-friendly explanation of the sandbox system."\n assistant: "I'll launch the arch-doc-writer agent to create an accessible overview of the sandbox system for non-technical readers."\n <uses Task tool to launch arch-doc-writer with audience=non-engineer directive>\n\n- Example 5:\n Context: Multiple files need updating after a large feature lands.\n user: "I just landed the network namespace isolation feature. Update architecture/sandbox.md and architecture/networking.md."\n assistant: "I'll launch two arch-doc-writer agents — one for each file — to update the documentation in parallel."\n <uses Task tool to launch arch-doc-writer for architecture/sandbox.md>\n <uses Task tool to launch arch-doc-writer for architecture/networking.md>
"name": "antigravity-skills", "description": "Professional Agent Skills collection for full-stack development, logic planning, and multimedia processing.", "email": "guanyangsunlight@gmail.com",
Browse once, cache the APIs, reuse them instantly. First call discovers and learns the site's APIs (~20-80s). Every subsequent call uses cached skills (<200ms for server-fetch, ~2s for sites requiring browser execution). When the task touches docs, install guidance, eval claims, landing-page copy, r
"$schema": "https://anthropic.com/claude-code/marketplace.schema.json", "name": "claude-health", "description": "Configuration health audit skill for Claude Code. Audits CLAUDE.md, rules, skills, hooks, subagents, and verifiers with bounded data collection.",
"version": "0.25.0", "description": "Datadog API CLI with 49 command groups, 300+ subcommands. Skills and domain agents for monitoring, logs, APM, security, and infrastructure.", "email": "support@datadoghq.com"
An orchestrator booster that automatically fetches GitHub issues, spawns AI sub-agents to implement fixes, opens pull requests, and manages review feedback. Ideal for teams looking to automate bug triage and fix workflows.
"name": "research-companion", "description": "Strategic research thinking agents — idea evaluation, project triage, and structured brainstorming inspired by Carlini's research methodology", "name": "Andre Huang",
"name": "langchain-skills", "description": "Agent skills for building agents with LangChain, LangGraph, and Deep Agents", "name": "LangChain",
"name": "ensue-memory", "description": "Persistent memory layer for AI agents via Ensue Memory Network", "email": "founders@ensue.dev"
"name": "power-bi-agentic-development", "version": "0.19.0", "description": "A marketplace for skills and tools for agentic Power BI development.",
Graphlit MCP Server integrates the Graphlit knowledge graph platform with Claude, enabling developers to build AI agents with RAG capabilities, document parsing, and intelligent retrieval. It's ideal for teams building document-heavy AI applications, knowledge management systems, and enterprise AI agents.
"description": "Open-source cross-agent memory layer for coding agents across Cursor, Claude Code, Codex, Windsurf, Gemini CLI, Copilot, Kiro, OpenCode, Antigravity, and Trae via MCP.", "sideEffects": false, "memorix": "./dist/cli/index.js"
Sub-Agents is a lightweight framework for decomposing complex tasks into specialized child agents that collaborate under a parent orchestrator agent. Developers building multi-agent systems in Python will benefit from this pattern for creating modular, reusable agent hierarchies.
LC-StudyLab provides Cursor IDE rules and documentation links for LangChain v1.0, enabling developers to quickly reference core components and advanced features while coding with LangChain's full ecosystem.
A PHP SDK for building AI agents with structured outputs and multi-agent orchestration, enabling developers to decompose complex tasks into specialized subagents with isolated contexts and independent execution.
A Python SDK for building production-ready agentic AI applications with Claude, featuring agents, handoffs, and guardrails for complex multi-agent workflows. Developers building AI agents and multi-tool systems will benefit from this lightweight, easy-to-learn framework.
A specialized diagnostic tool for data engineers to systematically investigate Airflow DAG failures, identify root causes, and implement prevention strategies. Ideal for complex pipeline debugging scenarios requiring deep analysis beyond basic log inspection.
Human MCP enables Claude coding agents to leverage human-like capabilities including vision, debugging, and multimodal interactions through the Model Context Protocol. Developers building AI coding assistants and agents will benefit from enhanced human-centered debugging and visual analysis features.
"name": "clawdstrike", "name": "Backbay Labs", "email": "hello@backbay.io"
A system prompt that transforms an AI assistant into a job search agent capable of managing Notion databases, filtering job opportunities, and automating application workflows. Useful for job seekers and recruiters seeking to streamline application tracking.
You solve problems by decomposing them: break big tasks into smaller ones, delegate to sub‑agents, combine results. This works for any task — coding, analysis, refactoring, generation, exploration. Your original prompt is also available as a file at — use it when you need to manipulate the question
"version": "2.30.0", "description": "The missing DevOps layer for coding agents. Flow, feedback, and memory that compounds between sessions.", "name": "Boden Fuller",
