Total 50,737 skills, AI & Machine Learning has 8499 skills
Showing 12 of 8499 skills
CrewAI architecture decisions and project scaffolding. Use when starting a new crewAI project, choosing between LLM.call() vs Agent.kickoff() vs Crew.kickoff() vs Flow, scaffolding with 'crewai create flow', setting up YAML config (agents.yaml, tasks.yaml), wiring @CrewBase crew.py, writing Flow main.py with @start/@listen, or using {variable} interpolation.
When the user wants to build or improve a sales bot's ability to recover prospects who stopped responding mid-conversation. Also use when the user mentions "ghost recovery," "unresponsive prospects," "conversation dropoff," "re-engagement sequences," or "dead conversation revival."
When the user wants to build or improve a sales bot's ability to test individual message variants. Also use when the user mentions "message testing," "A/B testing messages," "variant testing," "message optimization," or "reply testing."
Research Solana/crypto startup opportunities using builder project history, crypto archives, investor theses, and market signals. Answers questions conversationally by default; runs the full 8-step deep research workflow on explicit opt-in ("vet this idea", "deep dive").
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
End-to-end setup for making a Telnyx AI assistant call a phone number. Covers provisioning a phone number, creating a TeXML application, assigning the number, configuring telephony settings, whitelisting destination countries, and triggering outbound calls via scheduled events. Use this skill (not telnyx-ai-assistants-python) when the task involves an AI assistant placing, making, or triggering an outbound phone call to a user.
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.
AI agents on the Teneo Protocol network for real-time data queries.
Verify AI claims against source documents using the DeepCitation API
Build and deploy AI agents with CloudBase Agent SDK (TypeScript & Python). Implements the AG-UI protocol for streaming agent-UI communication. Use when deploying agent servers, using LangGraph/LangChain/CrewAI adapters, building custom adapters, understanding AG-UI protocol events, or building web/mini-program UI clients. Supports both TypeScript (@cloudbase/agent-server) and Python (cloudbase-agent-server via FastAPI).
Use after analyze-and-document has generated CLAUDE.md for an AI Studio project. Installs project-level Claude Code configuration — rules, skills, settings, and optionally agents, hooks, and MCP servers — into the .claude/ directory so that all future sessions have the right guardrails and workflows.
MuJoCo MJCF XMLフォーマットリファレンス。geomタイプ、joint、option、asset、動的XML生成パターン。mujoco mjcf xml reference geom joint body