skill-from-github
Create high-performance AI skills by reverse-engineering successful GitHub projects and proven open-source methodologies.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
205 skills found
Create high-performance AI skills by reverse-engineering successful GitHub projects and proven open-source methodologies.
Build production-grade RAG systems using vector databases, semantic search, and LangGraph to ground LLMs in external knowledge.
Manage long-running PapersFlow DeepScan research workflows with asynchronous monitoring, live progress tracking, and automated report generation.
A comprehensive guide and reference for building, orchestrating, and deploying AI agents using the Google Agent Development Kit (ADK).
Search and retrieve AI-generated documentation, architecture guides, and API references for 300+ popular GitHub repositories using DeepWiki and MCP.
Collaborative UI design, wireframing, and Tailwind-first code polish to build distinctive, high-quality interfaces without AI slop.
Explains complex concepts using master teaching frameworks like Feynman, Socratic, and Cognitive Load theory to ensure deep, clear understanding.
Autonomous improvement loop for codebase optimization. Automatically modifies, measures, and iterates on code based on a specific goal and mechanical metric.
Build RAG systems to ground LLMs in proprietary data. Includes vector database integration, embedding strategies, hybrid search, and advanced retrieval patterns for FastAPI backends.
Professional-grade spreadsheet automation for Claude: create, edit, analyze, and visualize Excel and CSV files with rigorous formula integrity and financial formatting standards.
Genomic file toolkit for NGS data processing. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using Pysam with a Pythonic interface to htslib.
Build modular FastAPI applications using Clean Architecture, including domain-driven design, dependency injection, repository patterns, and testing strategies for scalable Python backend services.