Agent Skills Hub

jina-cli

A powerful CLI for converting web content and search results into LLM-friendly formats like Markdown, text, or HTML using the Jina AI Reader API.

tavily-tools

Unified Python CLI for Tavily AI operations including web search, URL extraction, site crawling, link mapping, and automated deep research reports.

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.

higress-auto-router

Manage automatic model routing for Higress AI Gateway via CLI. Configure triggers for intelligent model selection based on request content.

brave-search

Headless web search and content extraction using Brave Search API. Perform documentation lookups, factual research, and web data retrieval without a browser.

rag-engineer

Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.

rag-implementation

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

semantic-compression

Aggressively prune grammatical scaffolding and filler text from inputs to optimize LLM token usage while retaining core semantic content.

maestro

Intelligent RAG-based gateway that routes coding tasks to specialized Swift/iOS expertise without context window bloat. Uses MCP to retrieve precise patterns from 100+ indexed skills.

google-search

Advanced Google search using a real, JavaScript-rendered Chrome browser. Ideal for scraping full page content, site-specific queries, and time-filtered results.

upstash-vector-db-skills

Upstash Vector DB setup, semantic search, namespaces, and embedding models. Ideal for building high-performance vector search features in Next.js 16/Vercel projects.

basic-usage

Essential guide to llmemory for document storage and search: installation, database setup with pgvector, document ingestion, hybrid/semantic retrieval, and building RAG systems with multi-tenant support.