Total 30,708 skills, AI & Machine Learning has 4959 skills
Showing 12 of 4959 skills
Develop, debug, and optimize SGLang LLM serving engine. Use when the user mentions SGLang, sglang, srt, sgl-kernel, LLM serving, model inference, KV cache, attention backend, FlashInfer, MLA, MoE routing, speculative decoding, disaggregated serving, TP/PP/EP, radix cache, continuous batching, chunked prefill, CUDA graph, model loading, quantization FP8/GPTQ/AWQ, JIT kernel, triton kernel SGLang, or asks about serving LLMs with SGLang.
Write, debug, and optimize CUTLASS and CuTeDSL GPU kernels using local source code, examples, and header references. Use when the user mentions CUTLASS, CuTe, CuTeDSL, cute::Layout, cute::Tensor, TiledMMA, TiledCopy, CollectiveMainloop, CollectiveEpilogue, GEMM kernel, grouped GEMM, sparse GEMM, flash attention CUTLASS, blackwell GEMM, hopper GEMM, FP8 GEMM, blockwise scaling, MoE GEMM, StreamK, warp specialization CUTLASS, TMA CUTLASS, or asks about writing high-performance CUDA kernels with CUTLASS/CuTe templates.
Write, debug, and optimize Triton and Gluon GPU kernels using local source code, tutorials, and kernel references. Use when the user mentions Triton, Gluon, tl.load, tl.store, tl.dot, triton.jit, gluon.jit, wgmma, tcgen05, TMA, tensor descriptor, persistent kernel, warp specialization, fused attention, matmul kernel, kernel fusion, tl.program_id, triton autotune, MXFP, FP8, FP4, block-scaled matmul, SwiGLU, top-k, or asks about writing GPU kernels in Python.
Writes rigorous mathematical proofs for ML/AI theory. Use when asked to prove a theorem, lemma, proposition, or corollary, fill in missing proof steps, formalize a proof sketch, 补全证明, 写证明, 证明某个命题, or determine whether a claimed proof can actually be completed under the stated assumptions.
Read, write, and browse the AgentHub message board for agent coordination.
AI-powered analysis of Trump's social media posts to predict stock market movements using 31.5M brute-force tested rules
Sub-millisecond VM sandboxes for AI agents using copy-on-write KVM forking via Zeroboot
OpenClaw-RL framework for training personalized AI agents via reinforcement learning from natural conversation feedback
Fully local multi-agent swarm intelligence simulation engine using Neo4j + Ollama for public opinion, market sentiment, and social dynamics prediction.
Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.
Get a deep critical review of research from GPT via Codex MCP. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.
Workflow 1: Full idea discovery pipeline. Orchestrates research-lit → idea-creator → novelty-check → research-review to go from a broad research direction to validated, pilot-tested ideas. Use when user says "找idea全流程", "idea discovery pipeline", "从零开始找方向", or wants the complete idea exploration workflow.