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Found 5,773 Skills
Guide for setting up AI configuration in your application. Helps you choose between agent vs completion mode, select the right approach for your stack, and create AI Configs that make sense for your use case.
Execute workflow agents iteratively for refinement and progressive improvement until quality criteria are met. Use when tasks require repetitive refinement, multi-iteration improvements, progressive optimization, or feedback loops until convergence.
Set up Symphony (OpenAI's Codex orchestrator) for a user's repo. Use when the user mentions Symphony setup, configuring Symphony, getting Symphony running, or wants to connect their repo to Linear for autonomous Codex agents. Also use when the user says "set up symphony", "configure symphony for my repo", or references WORKFLOW.md configuration.
Create custom multi-agent workflows for Atomic CLI using the defineWorkflow() session-based API with programmatic SDK code. Use this skill whenever the user wants to create a workflow, build an agent pipeline, define a multi-stage automation, set up a review loop, or connect multiple coding agents together. Also trigger when they mention workflow files, .atomic/workflows/, defineWorkflow, or ask how to automate a sequence of agent tasks — even if they don't use the word "workflow" explicitly.
Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.
Build voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots, interactive voice characters, or any real-time voice conversation experience.
Apply optimization techniques to extend effective context capacity. Use when context limits constrain agent performance, when optimizing for cost or latency, or when implementing long-running agent systems.
Dynamic tool selection, composition, and error handling patterns for AI agents. Use when you need to efficiently leverage available tools and handle failures gracefully.
Working memory management, context prioritization, and knowledge retention patterns for AI agents. Use when you need to maintain relevant context and avoid information loss during long tasks.
AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
Dispatch and coordinate parallel agent execution. Manages concurrent task processing with result aggregation and error handling.
Spawn a Team Leader agent that manages multiple sub-agents working toward a common goal. Team Leader reads requirements, decomposes work, assigns personalities and tasks, manages communication between team members, tracks progress, and reports results following ogt-docs task workflow. Integrates fully with docs-first system via task signals and status tracking.