6.8K skills · sorted by relevance
Plan and build AI agents through selected stages of design, implementation, tool integration, memory, and evaluation. The workflow points to specialized skills for single agents, coordinated agent teams, and stateful workflows using CrewAI or LangGraph.
Design AI agents that use tools, plan tasks, retain relevant context, and recover from failures. Choose execution patterns, permission boundaries, memory handling, and runtime limits, and assess whether coordinating multiple agents is justified for the task.
Create, improve, or audit CLAUDE.md files that explain a project's purpose, structure, and development workflow to AI agents. Organize essential instructions, remove redundant content, and link to detailed documents where needed.
Create, improve, or audit CLAUDE.md files that introduce AI agents to a project. Review project structure and development commands, keep shared instructions concise, and move detailed guidance into referenced documents, with drafts reviewed before changes are applied.
Create, improve, or audit CLAUDE.md files that explain a project's purpose, structure, and development workflow to AI agents. Organize essential instructions, remove redundant content, and link to detailed documents where needed.
Organize feature development across multiple agents by dividing work, assigning one owner per file, and agreeing on interfaces before implementation. Provides strategies for shared files, branches, and integration so agents can build separate parts of the same system.
Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
Consult another agent for specialist advice, reviews, or workspace context through QwenPaw commands. Keep follow-up conversations connected using session identifiers, and submit longer tasks in the background while continuing other work and checking results later.
Manage multiple AI agents through the AI Maestro command-line tool. Create agents, inspect their status, pause and resume work, rename them, export or import configurations, and manage plugins from one interface.
Connect agentmemory to coding agents such as Claude Code and Codex while preserving existing server settings. It guides configuration, host restarts, and checks that memory tools are available and can save and retrieve information.
Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.
Plan and build AI agents on Google Cloud, from gathering requirements to choosing a single-agent or multi-agent design. Produce architecture diagrams, deployment code and instructions, and validation plans tailored to the workload.
Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.
Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.
Build teams of AI agents with CrewAI, assigning roles such as researcher, writer, and analyst. Guidance covers passing results between tasks, running work in sequence or under a manager, adding memory, and creating workflows with conditional branches.
Inspect imports and agent patterns in existing Python code to identify its framework and recommend an Omnigent executor, the component that runs the agent. Explain unsupported frameworks and suggest available next steps.
Manage agents on a host by listing their capabilities, reading source code, and downloading or deploying agents in the smith namespace. Check available language model connections and their roles without exposing API keys.
Helps older OpenRig instructions that reference software-for-agents lead to its maintained successor, agent-operated-software. This temporary compatibility redirect identifies where to find the current architecture and operating guidance.
Guides the design and review of agent startup context, including role files and links to authoritative records. It helps identify stale guidance, missing updates, overloaded startup files, and instructions that lose the purpose of the work.
Turn reusable workflows and project conventions into concise instructions for AI agents. Structure SKILL.md with required metadata, move supporting material into local files, and register project skills in AGENTS.md when applicable.
Create specialized assistants for Cursor that work with their own context, separate from the main conversation. Define responsibilities, instructions, output formats, model choices, and execution settings for reusable workflows such as verification, debugging, and documentation.
Build servers and clients for A2A, an open protocol that lets AI agents communicate across frameworks. Publish agent capabilities, exchange tasks and results, stream progress, and add notifications for workflows involving multiple agents.
Build shared settlement sessions where multiple agents commit funds, update allocations off-chain, and co-sign a final split. Covers Yellow SDK connections, participant voting weights, signature thresholds, deposits, withdrawals, closure, and the system's trust limits.
Use the OpenServ Ideaboard API to find service ideas, register work in progress, and report completed implementations. It also covers proposing ideas, coordinating through comments, and sharing service URLs that accept payments through x402.