Total 58,118 skills, AI & Machine Learning has 9659 skills
Showing 12 of 9659 skills
Build, scaffold, extend, deploy, and troubleshoot event-driven AI agents and scheduled serverless agent apps on Azure Functions using azurefunctions-agents-runtime. Use when the user wants a scheduled agent, morning briefing, daily digest, timer agent, inbox summary, email or Teams briefing, background AI workflow, connector-triggered agent, event-driven AI automation, HTTP/chat agent, webhook-style agent, or Azure Functions hosted agent. Covers .agent.md, agents.config.yaml, Foundry gpt-4.1/gpt-5.x model choice, dynamic sessions for code execution and web browsing, built-in chat/API/MCP endpoints, remote MCP servers, Connector Namespaces, Office 365 or Teams MCP tools/triggers, custom Python tools, Agent Skills, azd deployment, local.settings.json, Application Insights, local development, and troubleshooting.
Production-ready AI agent templates for OpenClaw - 205+ SOUL.md configs across 24 categories for autonomous agents
Set up automated agent-driven development with Ralph. Run AI agents in a loop to implement features from user stories, verify acceptance criteria, and log progress for the next agent.
Use when "SHAP", "Shapley values", "feature importance", "model explainability", or asking about "explain predictions", "interpretable ML", "feature attribution", "waterfall plot", "beeswarm plot", "model debugging"
Agno AI agent framework. Use for building multi-agent systems, AgentOS runtime, MCP server integration, and agentic AI development.
Create personalized workout plans and provide exercise guidance
Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling
Guide developers through creating ChatGPT apps. Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/widgets, debugging, running dev servers, deploying and connecting apps to ChatGPT. Use when a user wants to create or update a ChatGPT app / MCP server for ChatGPT, or use the Skybridge framework.
Dynamic, reflective problem-solving through structured sequential thoughts with support for branching, revision, and adaptive depth. Use this skill when: (1) Breaking down complex problems into steps, (2) Planning and design with room for revision, (3) Analysis that might need course correction, (4) Problems where the full scope is not clear initially, (5) Multi-step solutions requiring maintained context, (6) Situations where irrelevant information must be filtered out, (7) Any task benefiting from hypothesis generation, verification, and iterative refinement. Triggers: think through, step by step, break this down, sequential thinking, reason through, analyze step by step, think carefully, or when a problem clearly benefits from structured multi-step reasoning.
Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation
/cs:onboard — Founder interview that populates ~/.claude/company-context.md. The first command to run when starting with c-level-agents.
Enable the GitHub CLI (`gh`) in Claude Code cloud sessions and GitHub Copilot coding agent environments. Use this skill when: (1) setting up a project so cloud AI agents can use `gh` for PRs, issues, and releases, (2) configuring setup scripts or SessionStart hooks for `gh` installation, (3) adding `copilot-setup-steps.yml` for GitHub Copilot agents, (4) troubleshooting `gh` auth failures in cloud sessions, or (5) configuring `GH_TOKEN` for headless environments. Triggers on: "enable gh", "github integration", "Claude Code cloud setup", "copilot setup steps", "gh auth in cloud", "gh not working in cloud", "setup script", or any request involving GitHub CLI access from cloud-based AI coding agents.