Agent Skills Hub

personal-productivity

Help users manage time and tasks effectively. Use for overcoming overwhelm, improving focus, balancing responsibilities, and increasing personal productivity.

respond

Manually finalize and submit AI agent responses to Claude. Use when automatic synchronization fails or to manually curate findings.

xurl

Unified CLI tool to read, query, discover, and write AI agent conversations using the agents:// URI scheme across multiple coding agents and providers.

archon

Interactive Archon integration for knowledge base and project management. Features RAG-powered semantic search, website crawling, document versioning, and hierarchical task management via REST API.

dev-workflow

Full-stack SDLC agent workflow managing the entire production lifecycle from intake and planning to automated testing, CI/CD, and infrastructure deployment using MCP tools.

physics-student-focus

Teacher-focused student profiling tool: OCR answer sheets, summarize performance, and update student profiles with targeted physics learning goals.

oh-my-claude

Activates Prometheus planning mode for structured requirement gathering, codebase research, and task planning within Claude Code.

structured-outputs-advisor

Expert advisor for implementing Anthropic's structured outputs. Choose between JSON mode and strict tool use for guaranteed schema compliance and validated agentic workflows.

mcaf-architecture-overview

Maintains a centralized architecture overview with Mermaid diagrams to document system boundaries, module dependencies, and interface contracts for onboarding and refactoring.

context-engineering-expert

Advanced context engineering system for orchestrating AI agents, memory management, and token optimization to improve long-term persistence and project intelligence.

iterate

Autonomous improvement loop for codebase optimization. Automatically modifies, measures, and iterates on code based on a specific goal and mechanical metric.

massive-context-mcp

Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.