Total 58,116 skills, AI & Machine Learning has 9659 skills
Showing 12 of 9659 skills
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Use when billing for AI model token usage — setting up @commet/ai-sdk tracked() middleware, configuring balance consumption model plans with AI model pricing, tracking input/output/cache tokens, cost calculation with margins, or building AI products that need usage-based billing.
Create and scaffold new skills with proper frontmatter, directory structure, and validation. Use when the user asks to build a new capability, integrate a new API, or extend the system with a repeatable workflow.
A comprehensive development team tailored for beginners, consisting of product managers, architects, designers, developers, and testers, guiding you through the entire process from concept to launch.
Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart auto-capture.
Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.
Dense vector embeddings, semantic search, RAG pipelines, and reranking via Together AI. Generate embeddings with open-source models and rerank results behind dedicated endpoints. Reach for it whenever the user needs vector representations or retrieval quality improvements rather than direct text generation.
Answers AI agent evaluation methodology questions with practical, opinionated guidance grounded primarily in Microsoft's agent evaluation ecosystem (MS Learn, Eval Scenario Library, Triage & Improvement Playbook, Eval Guidance Kit) supplemented by select industry sources.
When the user wants to forecast using deep learning, LSTMs, transformers, or neural networks. Also use when the user mentions "neural network forecasting," "LSTM," "GRU," "transformer forecasting," "attention mechanisms," "seq2seq," "temporal convolution," "deep learning time series," or complex non-linear patterns. For traditional forecasting, see demand-forecasting. For general ML, see ml-supply-chain.
agent-team: Reset blocked, in-progress, or failed work to pending.
Save session context, decisions, progress, and plans to the Claude Brain Logseq graph. Triggers: "save to brain", "save this", "remember this", "store this decision", "log this", "save progress", "before I quit", "wrap up". Don't fire for read operations (use brain-load) or status checks (use brain-status).
Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns