Agent Skills Hub

kreuzberg

High-performance document intelligence library for extracting text, tables, code, and metadata from 91+ file formats, with OCR and LLM-ready output.

supabase-extract-anon-key

Extracts Supabase anonymous API keys from client-side source code to facilitate RLS testing and security auditing.

test-data-management

Strategic test data generation, management, and privacy compliance for scalable, secure, and realistic quality engineering workflows.

research

Rigorous research skill that enforces source verification via WebFetch and content analysis to prevent hallucinated citations.

codeql

Perform deep security analysis on codebases using CodeQL for interprocedural data flow, taint tracking, and automated vulnerability detection across multiple languages.

single-cell-rna-qc

Automated single-cell RNA-seq quality control pipeline following scverse best practices. Performs MAD-based outlier detection, cell filtering, and diagnostic visualization for .h5ad and .h5 datasets.

libragent-quality

Comprehensive code quality validation for LibrAgent, covering TypeScript frontend and Rust/Tauri backend via automated linting, formatting, type checking, and build verification.

ziwei-iztro-reader

Ziwei Doushu charting and layered interpretation engine. Analyzes natal, yearly, monthly, and daily horoscopes using structured data, offering systematic, evidence-based astrological insights.

light-curve-preprocessing

Preprocessing and cleaning astronomical light curves using Lightkurve. Tools for outlier removal, flattening, trend detrending, and quality flag handling for time-series analysis.

literature-engineer

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

mcp-builder

A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.

rag-engineer

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