Autonomous experiment loop — iteratively improve any measurable metric by modifying code, evaluating results, and keeping improvements. Use when the user says "autoresearch", "start experiments", "optimize this", "run the loop", or wants autonomous iteration on any measurable goal. Reads autoresearch.toml for config. Run `autoresearch init` first. --- ## Autoresearch — Autonomous Experiment Loop You are an autonomous research agent. Your mission: iteratively improve a measurable metric by modifying code, running experiments, and keeping what works. You will run hundreds of experiments. Most will fail. That's expected. The wins compound. --- ### Phase 1: Pre-Flight Before touching any code, validate the environment: ```bash autoresearch doctor ```
Run a subagent-first structured improve-verify loop in OpenCode. Activate with /autoresearch or specialized modes like /autoresearch:plan, /autoresearch:debug, /autoresearch:fix, /autoresearch:learn, /autoresearch:predict, /autoresearch:scenario, /autoresearch:security, /autoresearch:ship. Supports recursive self-improvement loops.
- 📁 references/
- 📁 templates/
- 📄 SKILL.md
Apply Karpathy's autoresearch loop (goal + mechanical fitness + mutable surface + keep-or-revert iteration) to ANY measurable workflow - code, content, sales, research, design, operations, not just ML or software. Use when the user asks to set up an overnight improvement loop, a keep-or-revert experiment workflow, iterative optimization, or asks "can I autoresearch this?". Includes a pre-loop triage that refuses fat-tailed, reflexive, or slow-feedback problems without adapting the mode.
- 📁 test_cases/
- 📄 base.py
- 📄 dashboard.py
- 📄 SKILL.md
Self-improving optimization via Karpathy autoresearch pattern. Generates → evaluates → scores → mutates prompts/descriptions in a loop. Targets — tool-selection, system-prompt, skill, decision-parser. Use when "optimize tools", "autoresearch", "improve skill X", "self-improve prompts", "optimize tool descriptions".
