Agent Skills Hub

paper-search-usage

Search, locate, and retrieve academic papers across arXiv, PubMed, IEEE, Scopus, ACM, and Semantic Scholar.

openclaw-pr-maintainer

Automated OpenClaw repository maintainer: triage, label, and validate PRs/issues using gitcrawl and GitHub CLI.

podcast-downloader

Search, browse, and download podcast episodes from Apple Podcasts via the iTunes Search API. Ideal for archiving audio content, batch downloading, and retrieving rich metadata for research or personal media libraries.

rag-implementation

Build production-grade RAG systems using vector databases, semantic search, and LangGraph to ground LLMs in external knowledge.

backend-rag-implementation

Build RAG systems to ground LLMs in proprietary data. Includes vector database integration, embedding strategies, hybrid search, and advanced retrieval patterns for FastAPI backends.

design-system-implementation

Enforce strict UI adherence to your project's design system tokens, components, and layout patterns for consistent frontend implementation.

analytics-interpretation

Analyze GA4 and GSC performance data with automated benchmarks, status indicators, and actionable content optimization insights.

deepwiki

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

web-search

Autonomous research specialist for verified information gathering, source evaluation, and structured synthesis.

drawnote-skill

An automated visual note and flowchart generator. Converts text or keywords into styled diagrams, mind maps, and handwritten notes exported as images without requiring file-reading permissions.

trulens-evaluation-workflow

A systematic workflow to instrument, evaluate, and monitor LLM applications using TruLens, supporting frameworks like LangChain, LangGraph, and LlamaIndex.

scikit-learn

Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.