766 boosters for "agent" — AI-graded, open source, ready to install
A DevOps agent specialized in managing GCP infrastructure, Docker operations, and system troubleshooting—ideal for teams handling cloud deployments and containerized applications. Developers and DevOps engineers benefit from automated infrastructure management and debugging capabilities.
An ML engineer agent that handles end-to-end production ML workflows including model serving, feature engineering, A/B testing, and monitoring for TensorFlow/PyTorch deployments. Ideal for teams building scalable ML systems who need guidance on MLOps best practices and production readiness.
A security and compliance agent that helps teams audit, implement, and review security controls across authentication, authorization, encryption, and major compliance frameworks (GDPR, HIPAA, SOC2, PCI-DSS, ISO27001). Essential for security teams, DevOps engineers, and compliance officers building secure systems.
MCP Integrator is a specialized agent for configuring and connecting Claude to external tools and services via the Model Context Protocol (MCP), enabling developers to extend Claude's capabilities with databases, APIs, GitHub, Slack, and custom integrations.
Peepit MCP Server enables AI agents to capture and analyze macOS screenshots with smart window targeting and AI-powered image analysis, solving the critical problem of giving Claude visual perception of the desktop environment.
An MCP server enabling developers to register and manage .agent identities through the Department of Machine Verification system, integrating with Claude Code and Claude Desktop for identity management workflows.
fin-in-flow provides Cursor IDE rules for coordinating multi-agent AI workflows with production-ready code generation across Planner, Executor, and Auto modes. It benefits developers seeking structured AI-assisted development with MCP tool integration.
Helps developers plan and set up Git worktrees for parallel task execution by generating organized branch and worktree paths based on AGENTS.md conventions. Useful for teams managing multiple concurrent development efforts.
A persistent memory agent that maintains project context across sessions by recording decisions, errors, and preferences. Developers benefit by avoiding repeated mistakes and maintaining consistency in multi-session projects.
A practical guide to writing maintainable, debuggable LLM agent code by addressing unique failure modes like non-determinism, opaque tool calls, and prompt-logic coupling. Developers building Claude-based agents will benefit from these patterns.
A JavaScript-based system for dynamically creating and modifying AI agents without recompilation, enabling local LLM developers to test and share agent configurations interactively.
A guide for implementing custom agents compatible with MCProbe's testing framework, enabling developers to test proprietary, legacy, or specialized agent architectures beyond standard implementations.
A system prompt for Ember, an AI assistant designed to operate within containerized environments with full command execution and credential access capabilities. Primarily useful for developers building or testing emberd agent infrastructure.
Copilot instructions for a full-stack wealth portfolio RAG agent that enables natural language querying across MongoDB and MySQL databases using LangChain and OpenAI. Developers building AI-assisted financial applications benefit from this structured guidance on integrating multi-database queries with modern AI frameworks.
A DevOps specialist agent that automates Docker containerization, CI/CD pipelines, cloud deployments, and infrastructure management across AWS/GCP/Azure. Essential for teams managing application infrastructure and deployment automation.
Sam is a Senior QA Engineer agent specializing in test automation, accessibility validation, and performance testing for video streaming applications, helping teams achieve enterprise-level quality with 90% coverage and WCAG compliance.
GoAgent automates the creation of git worktrees for feature branches, enabling developers to work on isolated features without switching branches. Useful for teams practicing feature-branch workflows who want faster context switching within Claude Code.
MESSENGERMIKE is a notification agent that sends iMessage alerts to a specific phone number via the CIRCE framework. It's designed for developers who need to integrate Mac-based notifications into autonomous agent workflows.
This booster guides AI agents through a structured process to create comprehensive project specifications autonomously, enabling them to understand scope, architecture, and constraints before implementation. Developers and AI systems benefit by having clear, actionable project blueprints that facilitate autonomous work and reduce ambiguity.
A comprehensive Android platform specialist agent that teaches core components (Activities, Fragments, Services, Lifecycle, Intents, Permissions) across 78 hours of structured content. Ideal for developers building Android apps who need expert guidance on system-level architecture and lifecycle management.
A production-grade specialist for designing and implementing multi-agent systems with orchestration, coordination, and workflow management. Ideal for developers building complex distributed agent architectures.
A Git history analysis specialist agent that uncovers why code exists and how it evolved by examining commits, blame annotations, and development patterns. Developers benefit by quickly understanding architectural decisions and past implementation attempts without manually digging through git logs.
A deployment orchestration agent that automates code shipping to dev/beta/production environments with non-blocking operations and parallel execution guarantees. Useful for teams automating CI/CD workflows and seeking efficient, safe multi-environment deployments.
A QA specialist agent that enforces comprehensive testing without mocks, orchestrating browser automation and coverage validation across projects. Developers building quality-critical applications benefit from systematic test enforcement and multi-agent coordination.
