953 boosters for "Agent" — open source, verified from GitHub, ready to install
"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",
Quoroom is an experimental open-source framework for building local AI agent systems with a Queen-Workers-Quorum architecture, designed for researchers and developers exploring multi-agent AI patterns with Claude and other LLMs.
1. Preserve custom rules from existing .gitignore 3. On auth error (401), retry with 4. Re-add preserved custom rules
A planning-first agent that generates structured task breakdowns before execution, helping developers clarify scope and avoid missteps on complex code operations. Ideal for teams using Claude in agentic workflows.
This skill documents how to run the Parchi relay daemon, connect the browser extension as an agent, and use the CLI to drive browser automation. 1. Build everything: 2. Start the relay daemon (terminal A):
"name": "axlabs-mckinsey-pptx", "description": "McKinsey-style PPTX generator with 40 production-ready slide templates (executive summary, BCG matrix, KPI dashboard, roadmap, org chart, historic-forecast, etc.). Ships with a subagent that picks the right template for each slide, defends its choice,
"name": "langchain-skills", "description": "Agent skills for building agents with LangChain, LangGraph, and Deep Agents", "name": "LangChain",
45 AI-powered skills for affiliate marketers. Each skill automates a specific workflow (content creation, program research, SEO, outreach, analytics, etc.) using live data from the Affitor API.
"description": "AI-native job queue for Bun — skills, MCP server (73 tools), custom agent, SQLite persistence, cron, priorities, retries, DLQ, webhooks", "name": "egeominotti" "homepage": "https://github.com/egeominotti/bunqueue",
"name": "atlas-mcp-server", "version": "2.8.15", "description": "ATLAS (Adaptive Task & Logic Automation System): An MCP server enabling LLM agents to manage projects, tasks, and knowledge via a Neo4j-backed, three-tier architecture. Facilitates complex workflow automation and project management thr
1. Write Simple, Clear Code 3. Project Structure 4. Development Practice
BlockRun MCP Server enables Claude users to access 30+ AI models through x402 micropayments without requiring API keys. This benefits developers who want flexible model access and cost-efficient AI inference across multiple providers.
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.
Heuristic scoring (no AI key configured).
Working directory: the repository root (). All fixes happen on the branch. After fixing: 2. (specific files only)
Pulse Radar uses LLM pipeline to transform raw Telegram messages into structured knowledge. Core philosophy: Messages individually are noise; batched extraction reveals patterns. 1. JSON-only output — explicitly state "respond with ONLY JSON"
name: sona-learning-optimizer description: SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation type: adaptive-learning
This booster provides expert guidance for developing, debugging, and optimizing Azure AI Document Intelligence applications, covering architecture, security, best practices, and deployment patterns. Developers building document processing solutions on Azure will benefit from its comprehensive troubleshooting and design pattern knowledge.
VTCode is a system prompt designed to enhance a semantic AI coding agent for terminal-based development environments, providing clearer instructions and error handling for developers using Claude, Cursor, Windsurf, or ChatGPT.
"name": "ensue-memory", "description": "Persistent memory layer for AI agents via Ensue Memory Network", "email": "founders@ensue.dev"
"name": "codeguard-security", "description": "Security code review skill based on Project CodeGuard's comprehensive security rules. Helps AI coding agents write secure code and prevent common vulnerabilities.", "name": "Project CodeGuard",
Accessible workspace directory: !!<<<<||||workspace_dir||||>>>>!! When processing tasks, if you need to read/write local files and the user provides a relative path, you may choose to combine it with the above workspace directory to get the complete path. If you believe the task is completed, you ca
