Total 55,565 skills, AI & Machine Learning has 9241 skills
Showing 12 of 9241 skills
Optional Stage 0 of the feature workflow — clarify vague ideas through dialogue until they are ready to enter the design phase. The role of AI is a thinking partner: dig out the real problem the user wants to solve (instead of sticking to the first solution they blurt out), actively evaluate the solution when the user brings it up, and propose better alternatives if necessary. After the discussion, output {slug}-brainstorm.md to document the results. Trigger scenarios: The user says "I have an unclear idea", "Let's brainstorm first", "The feature direction is still undecided", or the user brings a specific solution but wants to hear other ideas first. Skip this stage and proceed directly to design if the idea is already clear and the user does not want to discuss the solution further. This stage also does not handle bugs and refactoring.
A method for iteratively improving text instructions for agents (skills / slash commands / task prompts / CLAUDE.md sections / code generation prompts) by having unbiased executors run them, then evaluating from both perspectives (executor self-report + instruction-side metrics). Repeat until improvement plateaus. Use immediately after creating or significantly revising a prompt or skill, or when you suspect the reason an agent isn't behaving as expected is due to ambiguity in the instructions.
Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API.
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
Compatibility router for the shared optimization knowledge base and the language-specific optimization catalog skills. Use when: (1) selecting which optimization catalog skill to load, (2) the implementation language is not fixed yet, (3) a workflow still references the legacy optimization-catalog skill name, (4) deciding whether a finding is shared or language-specific, (5) updating the generalized knowledge-base structure.
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
PR-backed and current-main optimization manual for the `MiniMaxAI/MiniMax-M2` series, including M2, M2.1, M2.5, M2.7, and M2.7-highspeed. Use when Codex needs to recover, extend, or audit MiniMax-specific optimizations, TP QK norm/all-reduce behavior, parser contracts, distributed runtime behavior, quantized loading, or backend-specific validation.
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
Prompting techniques for AI image generation and editing models on Replicate. Use when writing prompts for image models or building image generation features.
Operate LM Studio's `lms` CLI and local/remote LM Studio servers for model discovery, server status checks, model loading, endpoint smoke tests, and downstream OpenAI-compatible wiring. Use when the user mentions LM Studio, `lms`, a local model server, `/v1/models`, a remote LM Studio host, or wants to connect another tool to LM Studio; even if they only ask to test a local OpenAI-compatible endpoint or choose the correct loaded-model identifier. Triggers on: lmstudio, lm studio, lms, local model server, LM Studio API, LM Studio endpoint, /v1/models, connect Strix to LM Studio, load model in LM Studio.
Skill Index and Orchestration Center — Automatically routes to the correct skill combination and orchestrates execution order based on user intent. Triggered when users ask questions involving stock quotes, cryptocurrencies, technical indicators, financial news, research report generation, or any scenario that requires skill invocation. Also triggered when users ask "Where does the data come from?", "What can you do?", or "What features do you have?"
Amazon Sagemaker integration. Manage data, records, and automate workflows. Use when the user wants to interact with Amazon Sagemaker data.