18 skills found
ashishps1 / learn-ai-engineeringLearn AI and LLMs from scratch using free resources
Upsonic / UpsonicBuild autonomous AI agents in Python.
huang-sh / pytorch-fsdpAn open-source, model-agnostic AI workbench for scientific discovery.
majiayu000 / llm-pipelinePydantic-AI agents, RAG, embeddings for Pulse Radar knowledge extraction.
airmcp-com / mcp-standardsSelf-learning AI standards system - learns from corrections and auto-updates CLAUDE.md
NirDiamant / GenAI_Agents50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
datawhalechina / easy-vibe💻 The first course for AI-native product builders.
openklas / openklasMCP server for University Learning Management System (KLAS).
supatest-ai / cursor-rules-generatorcursor-rules-generator
symgraph / BinAssistMCPBinary Ninja plugin to provide MCP functionality.
remete618 / widemem-aiNext-gen AI memory layer with importance scoring, temporal decay, hierarchical memory, and YMYL prioritization
liwala / shealself-healing and self-learning loop for coding agents
CyranoB / web-foragerA search-and-fetch toolkit for AI agents, available as an MCP server and as standalone Agent Skills
NirDiamant / agents-towards-productionEnd-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
daseinpbc / SPL-FRAMEWORKSUBSUMPTION PATTERN LEARNING (SPL) MULTI-AGENT FRAMEWORK: Hierarchical foundation model agent architecture that reduces costs by 10-50x through intelligent suppression of expensive foundation model calls. Grounded in R. Arkin's behavior-based robotics and R.
Dicotomico23 / HIF_NetworkAnomalyDetectionResearch project based on the Hybrid Isolation Forest approach by Pierre-François Marteau for developing Intrusion Detection Systems (IDS) on networks.
vllm-project / semantic-routerA programmable Mixture-of-Models router for heterogeneous LLM inference
uiuc-kang-lab / hyperparamsGuide for hyperparameter selection — learning rate formulas, LoRA rank, batch size, group size, schedules, and model-specific tuning
