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    Autonomous Stack Researcher — Free Research Framework

    Instead of explaining your tools to an AI, let it go learn them. This framework directs any AI to research your tech stack, visit the actual product sites and documentation, and write working knowledge notes into a memory file — so future sessions already know how your tools function.

    Step 1: Run this prompt in your current AI

    Stack Research Framework — Paste into any AI
    You are going to research my tech stack and write what you learn into a memory file called platform_knowledge.md. I will give you my tech_stack.md file. Your job is to go research each platform yourself — visiting official sites, documentation, and feature pages — and produce working knowledge notes written specifically in the context of how I use each tool.
    
    This requires web search or browser access. If you do not have it, tell me and stop.
    
    Phase 1 — Read my stack: Read my tech_stack.md file. For each tool note: the tool name, what I use it for specifically, any non-standard use or workaround I described, and how critical it is. Build a research queue: daily drivers first, then supporting tools, then occasional or legacy.
    
    Phase 2 — Research each tool: Work through the queue one tool at a time. For each tool, start with the official product site or documentation. Look for: what the platform does at a feature level, how it structures data, what its native automations or integrations look like, and terminology specific to this platform. Then search for how people actually use it — common workflows, known limitations, workarounds. Then cross-reference with how I specifically use it. If I use a tool for something it was not designed for, research it from that angle.
    
    For each tool write a platform note with these sections: what it is (one sentence), how I use it (pulled from my tech_stack.md), key concepts and terminology (5 to 10 terms with plain-language definitions), how it works at a functional level, common workflows relevant to my use case, known friction points and workarounds, how it connects to my other tools, and what you still do not know.
    
    Phase 3 — Write to memory: Compile all notes into platform_knowledge.md. Start with a short summary paragraph naming every platform you researched and noting this is written in the context of how I use each one. Then include each platform note in priority order (daily drivers first).
    
    Phase 4 — Report back: Tell me what you researched, what you found, and what you could not figure out on your own. Flag specific things you want to verify — terminology you are not sure you understood correctly, features you inferred but could not confirm, and any proprietary or undocumented tools that need a direct briefing from me.
    
    Rules: No em dashes. Write platform notes like a colleague who spent an hour getting up to speed — not like a product brochure. Do not hallucinate features. If something is uncertain, say so in the "what I still do not know" section. Daily drivers need all 8 sections fully populated. Supporting tools need at least 5. Occasional use needs 3. Legacy tools get a brief note on what they are and why they are being phased out.

    What to expect

    Paste this into Claude or any AI with web search access, then share your tech_stack.md file. The AI will research your tools, then write platform_knowledge.md — a memory file that loads automatically in your Claude workspace so future sessions already know how your tools work without you having to explain them.

    Get the Full Autonomous Stack Researcher Free on Abra AI

    The full skill adds a research depth guide with minimum section requirements per tool tier, a quality checklist that runs before the memory file is saved, and a depth test that verifies the notes are specific enough to actually use when working in each platform on your behalf.