plan
Structured 6-phase workflow for planning and implementing features, skills, and architectural changes with automated tool discovery and safety verification.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
268 skills found
Structured 6-phase workflow for planning and implementing features, skills, and architectural changes with automated tool discovery and safety verification.
Full-stack application orchestrator that analyzes natural language requests to determine tech stacks, scaffold projects, and coordinate specialized development agents.
Autonomous recursive execution engine for indiiOS that manages task completion, state verification, and error handling.
A team of 6 specialist PMO agents for portfolio governance, resource planning, risk analysis, and executive reporting. Dispatch to handle complex multi-project oversight and strategic coordination.
A local RAG semantic memory system using Qdrant and Ollama. Ideal for recalling workspace files, notes, project decisions, and user preferences with high-relevance vector search.
Diagnose and debug Agent-to-Agent (A2A) communication, including orchestrator routing, transport connectivity, agent status, and log analysis for multi-agent systems.
Break down complex development requests into sequenced, actionable tasks for multi-agent delegation in Claude Code environments.
Automate social media content publishing using agent-browser to draft posts on platforms like Xiaohongshu, X, Weibo, WeChat, and Juejin directly from your browser.
Optimize agent context windows through KV-caching, observation masking, summarization-based compaction, and context partitioning to reduce costs and latency.
Enriches vague prompts by performing codebase research and asking targeted questions to clarify user intent before execution.
Autonomous pattern detection and skill recommendation engine that monitors project memory, logs, and task lists to evolve your AI agent's capabilities automatically.
A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.