Total 55,607 skills, AI & Machine Learning has 9242 skills
Showing 12 of 9242 skills
Build a production-ready multilabel classifier on tabular data using XGBoost wrapped in MultiOutputClassifier. Use when each row can have multiple labels simultaneously (tags, attributes, gene functions, content moderation categories, multi-disease detection). Covers hamming loss, per-label metrics, label co-occurrence, MultiOutputClassifier vs ClassifierChain, and per-label SHAP. Default to this for any tabular multilabel problem.
Generate AI video from static images using Kling 3.0, Hailuo, Luma Ray3, Runway Gen-4.5, and 8 other tools. Covers free vs paid tools, prompt writing (motion-only), camera control, and face stability. Use when user asks to animate an image, create AI video, or convert photo to video.
oh-my-agent project setup verification and configuration
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested. Integrated into Cavekit: enabled by default for build, inspect, and subagent phases via caveman_mode config. See scripts/bp-config.sh for caveman_mode and caveman_phases.
Detecting whether agent iterations are converging toward a stable solution or hitting a ceiling. Covers convergence signals, ceiling detection, non-convergence diagnosis, test pass rate as a convergence metric, and forward progress tracking for large projects. Trigger phrases: "convergence", "is the agent converging", "ceiling detection", "when to stop iterating", "diminishing returns"
Use when context is growing large (50k+ tokens), performance is degrading, instructions are being ignored mid-conversation, or planning multi-agent workflows. Triggers on "lost context", forgotten instructions, or sessions exceeding 30 minutes.
Stability AI integration. Manage data, records, and automate workflows. Use when the user wants to interact with Stability AI data.
Command-line interface for Novita AI - An OpenAI-compatible AI API client for DeepSeek, GLM, and other models.
Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
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.