Agent Skills Hub

querying-json

Efficiently extract, filter, and transform specific fields from JSON files using jq, saving up to 95% of context window usage compared to reading full files.

qras

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.

clarity-gate

Enforce epistemic quality in RAG systems with pre-ingestion verification. Ensures documents are properly qualified and structured before knowledge base entry.

elevenlabs

Convert clinical text to natural, empathetic speech using ElevenLabs for patient instructions, medication reminders, and accessible health content.

openai-agent-sdk-skill

Build AI agents, multi-agent systems, and workflows using the OpenAI Agents SDK for TypeScript/JavaScript. Supports tools, handoffs, guardrails, MCP, and realtime voice.

doc-reader

Read and navigate external documentation efficiently using llms.txt, MCP search, and smart parsing strategies.

juliaz-tool-builder

Expert guidance for designing and implementing high-quality tool schemas and descriptions for Julia's agent systems, ensuring reliable tool execution and reducing model hallucinations.

chatkit-frontend

Implement production-ready AI chat interfaces using OpenAI ChatKit React components. Features include hook configuration, streaming, theming, conversation history, and custom tool integration for Next.js applications.

cn-daily-tools

An all-in-one Chinese daily utility toolkit: weather, currency exchange, news, and package tracking. Zero configuration, no API keys required.

markdown-token-optimizer

Analyzes markdown files to identify token-wasting patterns, providing actionable suggestions to optimize documentation for LLM consumption and token efficiency.

librarian

Fetch, index, and search developer documentation from GitHub and websites to provide AI agents with accurate, grounded, and version-specific code context.

context-fundamentals

Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.