Agent Skills Hub

supermemory

Supermemory is a long-term memory infrastructure for AI agents, enabling persistent context, user profiles, and semantic RAG across multi-modal knowledge bases.

fastapi-auth

Implement secure session-based authentication in FastAPI with Argon2 hashing, database-backed sessions, and OAuth2 provider integration.

analyze-project

Analyze project codebases to generate architecture documentation, coding standards, and development practices for AI onboarding.

database-driver-design

Expert guidance for building production-ready Swift database client libraries, covering wire protocols, connection pooling, state machines, and NIO integration.

exa

Neural web search and code context retrieval via Exa AI. Ideal for documentation, technical research, code examples, and company intelligence.

context-engineering-expert

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

data-analysis

Data Analysis Specialist for EDA, statistical modeling, SQL queries, and Python-based visualization. Turn raw datasets into actionable insights through rigorous quantitative methods.

tax-advisor

AI-powered tax advisor providing expert guidance on 2025 Japanese tax regulations, deductions, and financial planning for freelancers and employees.

skill-engineer

Orchestrates multi-agent iterative refinement for high-quality OpenClaw skill development, ensuring rigorous testing and lifecycle management.

deepwiki

Search and retrieve AI-generated documentation, architecture guides, and API references for 300+ popular GitHub repositories using DeepWiki and MCP.

task-search

Accelerate task retrieval with a high-performance, debounced search engine supporting multi-token AND logic, relevance ranking, and real-time text highlighting across task titles, descriptions, and tags.

ai-writing-detection

Comprehensive AI-generated text detection framework. Features multi-layer analysis of vocabulary, structural patterns, model-specific fingerprints, and technical metadata artifacts to identify AI authorship.