Stand up a new AI/agent project - or add an AI subsystem to an existing repo - starting from a requirements intake and ending in a committed full stack. Use when the user says "scaffold an agent project", "set up a new AI app", "bootstrap a RAG/agent repo", "add an agent to this codebase", "add an AI/LLM feature to our existing app"; when they ask for the stack for a specific system they intend to build ("get me a stack", "which stack should we use for this", "pick the stack for this agent", "what should we build it with") - as opposed to open-ended options questions, which go to ai-engineering; when they make ANY "build me an agent that does X" request, even a trivial-sounding one, because the intake may rightly conclude a plain script suffices; and when they hand over a vague AI build idea that needs shaping first, in which case it calls interview-me and idea-refine before selecting anything. Exits with a named component per slot - harness, framework, model provider, memory, retrieval, ingestion, tools, MCP servers, eval, frontend - then generates the skeleton and hands implementation to agent-skills.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryEngineeringStars0
About this skill
Stand up a new AI/agent project - or add an AI subsystem to an existing repo - starting from a requirements intake and ending in a committed full stack. Use when the user says "scaffold an agent project", "set up a new AI app", "bootstrap a RAG/agent repo", "add an agent to this codebase", "add an AI/LLM feature to our existing app"; when they ask for the stack for a specific system they intend to build ("get me a stack", "which stack should we use for this", "pick the stack for this agent", "what should we build it with") - as opposed to open-ended options questions, which go to ai-engineering; when they make ANY "build me an agent that does X" request, even a trivial-sounding one, because the intake may rightly conclude a plain script suffices; and when they hand over a vague AI build idea that needs shaping first, in which case it calls interview-me and idea-refine before selecting anything. Exits with a named component per slot - harness, framework, model provider, memory, retrieval, ingestion, tools, MCP servers, eval, frontend - then generates the skeleton and hands implementation to agent-skills.
Quick start
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Stand up a new AI/agent project - or add an AI subsystem to an existing repo - starting from a requirements intake and ending in a committed full stack. Use when the user says "scaffold an agent project", "set up a new AI app", "bootstrap a RAG/agent repo", "add an agent to this codebase", "add an AI/LLM feature to our existing app"; when they ask for the stack for a specific system they intend to build ("get me a stack", "which stack should we use for this", "pick the stack for this agent", "what should we build it with") - as opposed to open-ended options questions, which go to ai-engineering; when they make ANY "build me an agent that does X" request, even a trivial-sounding one, because the intake may rightly conclude a plain script suffices; and when they hand over a vague AI build idea that needs shaping first, in which case it calls interview-me and idea-refine before selecting anything. Exits with a named component per slot - harness, framework, model provider, memory, retrieval, ingestion, tools, MCP servers, eval, frontend - then generates the skeleton and hands implementation to agent-skills.
About this listing
Category
Engineering
Reported license
MIT
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI