autoresearch

Autoresearch — Autonomous Goal-directed Iteration

Inspired by Karpathy's autoresearch. Applies constraint-driven autonomous iteration to ANY work — not just ML research.

Core idea: You are an autonomous agent. Modify -> Verify -> Keep/Discard -> Repeat.

When to Activate

• User invokes /skill:autoresearch or /autoresearch
• User says "work autonomously", "iterate until done", "keep improving", "run overnight"
• Any task requiring repeated iteration cycles with measurable outcomes

Phase 1: Understand (Do This First — Before ANY Work)

Before touching any files, deeply understand the goal. Do NOT rush into iteration.

1. Read relevant files — Scan the codebase to build context around the user's goal. Understand what exists, what patterns are in use, and what's realistic.

2. Identify ambiguities — Based on the goal and codebase context, what's unclear?

   • Is the success metric obvious or ambiguous?
   • Is the scope (which files to modify) clear?
   • Are there constraints the user hasn't mentioned?
   • Are there multiple valid interpretations?

3. Ask clarifying questions — If ANY ambiguity exists, use ask_user to ask targeted questions:

   ask_user {
     question: "I have a few questions before I build the research plan:",
     mode: "questions",
     options: [
       { label: "1. What metric should define success? (e.g. test coverage %, build time ms, bundle size KB)" },
       { label: "2. Which files/directories are in scope for modification?" },
       { label: "3. Are there any approaches to avoid or constraints I should know about?" },
       { label: "4. What does 'done' look like — a specific target, or iterate until interrupted?" }
     ]
   }
   

   Tailor questions to the specific goal. Don't ask about what's already clear. Ask about genuine ambiguities.

4. Skip if crystal clear — If the goal is unambiguous (clear metric, scope, exit criteria), skip questions and proceed to Phase 2. State briefly why no questions are needed.