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Found 182 Skills
Build and deploy autonomous AI agents using the OpenServ SDK (@openserv-labs/sdk). IMPORTANT - Always read the companion skill openserv-client alongside this skill, as both packages are required to build and run agents. openserv-client covers the full Platform API for multi-agent workflows and ERC-8004 on-chain identity. Read reference.md for the full API reference.
Google Agent Development Kit (ADK) for Python. Capabilities: AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration (Google Search, Code Execution), Vertex AI deployment, agent evaluation, human-in-the-loop flows. Actions: build, create, deploy, evaluate, orchestrate AI agents. Keywords: Google ADK, Agent Development Kit, AI agent, multi-agent system, LlmAgent, SequentialAgent, ParallelAgent, LoopAgent, tool integration, Google Search, Code Execution, Vertex AI, Cloud Run, agent evaluation, human-in-the-loop, agent orchestration, workflow agent, hierarchical coordination. Use when: building AI agents, creating multi-agent systems, implementing workflow pipelines, integrating LLM agents with tools, deploying to Vertex AI, evaluating agent performance, implementing approval flows.
Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.
Use when performing ralph wiggum style long-running development loops with pacing control.
OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent workflows, tool integrations, or streaming applications with the openai-agents package.
Coding patterns extracted from OpenAI Codex Rust codebase - a production CLI/agent system with strict error handling, async patterns, and workspace organization
Build AI agents with Cloudflare Agents SDK on Workers + Durable Objects. Includes critical guidance on choosing between Agents SDK (infrastructure/state) vs AI SDK (simpler flows). Use when: deciding SDK choice, building WebSocket agents with state, RAG with Vectorize, MCP servers, multi-agent orchestration, or troubleshooting "Agent class must extend", "new_sqlite_classes", binding errors.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
Build AI agents on Cloudflare Workers with MCP integration, tool use, and LLM providers.
Enforce disciplined agent development workflows with plan-first development, small-slice execution, specialized self-review roles, quality gates, and project setup. Use when starting a new project, setting up development conventions, wanting structured planning, or needing the agent to follow best practices for code quality, review, and validation.
Drive development using delegated agent workflows. Coordinates multi-agent task execution with proper supervision and result integration.