Total 57,011 skills, AI & Machine Learning has 9481 skills
Showing 12 of 9481 skills
Opik observability for LLM agents — Agent Configuration, Local Runner (opik connect), Evaluation Suites, threads, integrations. Use for "configure my agent", "connect my agent", "evaluate my agent" or "integrate with Opik".
Generates a Jupyter notebook that evaluates a fine-tuned SageMaker model using LLM-as-a-Judge. Use when the user says "evaluate my model", "how did my model perform", "compare models", or after a training job completes. Supports built-in and custom evaluation metrics, evaluation dataset setup, and judge model selection.
Add LangWatch tracing and observability to your code. Use for both onboarding (instrument an entire codebase) and targeted operations (add tracing to a specific function or module). Supports Python and TypeScript with all major frameworks.
Alibaba Cloud Bailian Knowledge Base Retrieval Tool. Use Alibaba Cloud Bailian SDK to query and retrieve knowledge base content. Use when: User needs to query knowledge base, retrieve document content, or answer questions based on knowledge base. Prerequisites: (1) Install npm packages (2) Configure Alibaba Cloud credentials (via Alibaba Cloud CLI or environment variables). (3) Need to activate Bailian service.
Use Claude Code's full tool system with any OpenAI-compatible LLM — GPT-4o, DeepSeek, Gemini, Ollama, and 200+ models via environment variable configuration.
Query the official CrewAI documentation for answers. Use when the user has a CrewAI question that isn't fully covered by the getting-started, design-agent, design-task skills — e.g., specific API details, configuration options, advanced features, troubleshooting errors, enterprise features, tool references, or anything where the latest docs are the best source of truth.
A prompt repetition technique for improving LLM accuracy. Achieves significant performance gains in 67% (47/70) of 70 benchmarks. Automatically applied on lightweight models (haiku, flash, mini).
AI prompt orchestration CLI using reusable Patterns. Use for YouTube summarization, document analysis, content extraction, code explanation, writing assistance, and any AI task via stdin/stdout piping across 20+ providers.
Fine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.
Use when running Ralph-style iterative autonomous development. Triggers on /ralph or /loop commands, when autonomous iterative development is needed, when a project has specs and an implementation plan ready for iterative execution, or when deterministic context loading with subagent delegation and dual-condition exit gates is required. Orchestrates PLANNING, BUILDING, and STATUS cycles.
Use when a task has multiple independent subtasks that can be executed concurrently by separate agents. Triggers when decomposed work has 2+ subtasks with no data dependencies, when subtasks operate on different files or codebase sections, when serial execution time would be significantly longer than parallel, or when independent analyses or deliverables need concurrent generation.
Find prompt and model quality issues using real conversation data, with specific optimization recommendations. Can implement prompt fixes and model switches directly in your codebase.