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Found 11 Skills
This skill should be used when the user wants to "develop an agent", "build an agent using ADK", "run the agent locally", "debug agent code", "test an agent", "deploy an agent", "publish an agent", "monitor an agent", or needs the ADK (Agent Development Kit) development lifecycle and coding guidelines. Entrypoint for building ADK agents. Always active — provides the full workflow (scaffold, build, evaluate, deploy, publish, observe), code preservation rules, model selection guidance, and troubleshooting steps for ADK or any agent development.
Expert guidance on the Google Agent Development Kit (ADK) for Python. Use this skill when the user asks about building agents, using tools, streaming, callbacks, tutorials, deployment, or advanced architecture with the Google ADK in Python.
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
Amazon Bedrock AgentCore platform for building, deploying, and operating production AI agents. Covers Runtime, Gateway, Browser, Code Interpreter, and Identity services. Use when building Bedrock agents, deploying AI agents to production, or integrating with AgentCore services.
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.
Use when the user wants to manage Valet agents, channels, connectors, organizations, or secrets via the valet CLI. Handles creation, deployment, linking, teardown, and all multi-step workflows. Also use when asked to "create an agent", "deploy an agent", "design an agent", "build me an agent that...", "create a connector", "set up a webhook", or anything involving the Valet platform or any request to create and deploy AI agents. Also use when asked to "learn from this session", "capture this workflow", "save this as an agent", "make this repeatable", or when writing SOUL.md files.
Step-by-step guide for openclaw users to build, deploy, and monetise an AI agent with aixyz. Covers everything from zero: installing Bun, scaffolding an agent, choosing a deployment option, getting a crypto wallet, funding it for on-chain registration, and marketing your agent once it is live.
Provision dedicated AI agents on AgentBox via x402 payment ($5 USDC on Solana). Use when creating cloud instances running OpenClaw AI gateways with HTTPS and web terminal. Requires Node.js and a Solana wallet.json with USDC funds. Covers: provisioning new instances, polling status, interacting via OpenAI-compatible chat completions, extending, and listing instances.
Overview The Messari Tracker Agent serves as a direct bridge to Messari’s institutional-grade data sources, allowing users to extract BTC and ETH data without manual searching or fragmented data sourc
Wrap an existing Python agent as an Agent Stack service using agentstack-sdk server wrapper, without changing business logic.
Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.