Loading...
Loading...
Found 2,223 Skills
Interprets authoritative specs and helps design a new implementation collaboratively, preserving required business, API, and database contracts while exploring architecture, stack, and delivery options with the user. Use when the user wants to start a new project from frozen specs, discuss implementation approaches, or plan an incremental rebuild without depending on the legacy codebase.
Select and configure evaluation metrics for an AI agent. Guides through metric selection using use-case recommendations, custom LLM-based metric creation with prompt engineering, and agent default attachment. Use when user says "set up metrics", "configure metrics", "create a metric", "what metrics should I use", "add evaluation criteria", or "customize scoring".
Expert in deploying and using Hermes HUD Web UI for monitoring AI agent memory, sessions, costs, and health
Expert in OpenClaw Studio - web dashboard for managing OpenClaw Gateway, agents, chat, approvals, and jobs
Official Lark/Feishu plugin for OpenClaw that enables AI agents to interact with Lark workspaces including messages, docs, bases, calendars, and tasks
Provision a zero-config, no-signup Upstash Redis database for an AI agent via a single POST to `https://upstash.com/start-redis`. Use when an agent needs scratch Redis for short-term memory, conversation history, sub-agent work queues, or ranked recall and the user has not provided credentials. The database lives 3 days unless the user claims it.
Secure browser SSO and OAuth2 authentication proxy that lets AI agents access authenticated APIs without exposing credentials
MCP server for computer use & browser automation - screenshot, OCR, click, type, find_text, Chrome/Electron CDP, template matching on macOS, Windows & Android
Connect Jira, Confluence, and Compass to AI agents and IDEs using Atlassian's remote MCP server with OAuth 2.1 or API token authentication.
Team composition for writing workflows: which agents to spawn, how many, what focus areas to assign, and how to scale effort. Use when composing critic panels, dispatching researchers, staffing draft/revise loops, or setting up brainstorm fan-outs.
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.
Use this skill whenever the user is working with the Pydantic AI framework — including building AI agents, defining structured outputs with Pydantic models, wiring up tools/function calling, configuring model providers (OpenAI, Anthropic, Gemini, etc.), managing dependencies via agent context, handling streaming responses, or debugging agent runs. Trigger this skill even for adjacent tasks like "how do I make my agent return JSON", "set up a multi-step agent", "add a tool to my agent", or "validate LLM output with Pydantic" — any time Pydantic AI is mentioned or implied as the target framework.