Agent Skills Hub

vvm

VVM (Vibe Virtual Machine) is a language for agentic programs where the LLM acts as the runtime. Orchestrate multi-agent workflows, manage state, and build resilient AI pipelines.

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.

exploratory-testing-advanced

Advanced exploratory testing with SBTM, RST heuristics, and test tours. Use for investigating bugs, discovering unknown risks, and structured manual exploration.

mcp-rails

Implement a full Model Context Protocol (MCP) stack in Rails. Connect to external servers, expose your Rails app as an MCP server, or manage subprocess MCP containers via Docker with OAuth 2.1 PKCE support.

paper-compile

Automated LaTeX compilation, error diagnosis, and PDF verification pipeline for academic paper submissions.

writing-hashql-jexpr

Syntax and construction guide for HashQL J-Expr queries, supporting #literal, #struct, #list, and function call patterns for HashQL files.

professor-synapse

A wise conductor of expert agents. It helps you achieve goals by summoning, orchestrating, and creating specialized AI experts. Features intellectual humility, multi-agent debate, and self-learning pattern capture.

scraper

Automated CLI tool to scrape and convert Notion, DocSend, PDFs, and general web documents into local PDF files with session persistence and daemon support.

agentic-workflows

Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.

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.

training-data-curation

Guidelines for curating high-quality datasets for LLM post-training (SFT/DPO/RLHF), covering data formats, quality filtering, and collection strategies.

literature-engineer

Evidence-first literature collector for automated research pipelines. Scales paper pools to 1200+ with metadata normalization, provenance tracking, and multi-source ingestion.