feynman/prompts/autoresearch.md

description, args, section, topLevelCli

description	args	section	topLevelCli
Autonomous experiment loop — try ideas, measure results, keep what works, discard what doesn't, repeat.	<idea>	Research Workflows	true

Start an autoresearch optimization loop for: $@

This command uses pi-autoresearch.

Step 1: Gather

If autoresearch.md and autoresearch.jsonl already exist, ask the user if they want to resume or start fresh. If CHANGELOG.md exists, read the most recent relevant entries before resuming.

Otherwise, collect the following from the user before doing anything else:

• What to optimize (test speed, bundle size, training loss, build time, etc.)
• The benchmark command to run
• The metric name, unit, and direction (lower/higher is better)
• Files in scope for changes
• Maximum number of iterations (default: 20)

Step 2: Environment

Ask the user where to run:

• Local — run in the current working directory
• New git branch — create a branch so main stays clean
• Virtual environment — create an isolated venv/conda env first
• Docker — run experiment code inside an isolated Docker container

Do not proceed without a clear answer.

Step 3: Confirm

Present the full plan to the user before starting:

Optimization target: [metric] ([direction])
Benchmark command:   [command]
Files in scope:      [files]
Environment:         [chosen environment]
Max iterations:      [N]

Ask the user to confirm. Do not start the loop without explicit approval.

Step 4: Run

Initialize the session: create autoresearch.md, autoresearch.sh, run the baseline, and start looping.

Each iteration: edit → commit → run_experiment → log_experiment → keep or revert → repeat. Do not stop unless interrupted or maxIterations is reached. After the baseline and after meaningful iteration milestones, append a concise entry to CHANGELOG.md summarizing what changed, what metric result was observed, what failed, and the next step.

Key tools

• init_experiment — one-time session config (name, metric, unit, direction)
• run_experiment — run the benchmark command, capture output and wall-clock time
• log_experiment — record result, auto-commit, update dashboard

Subcommands

• /autoresearch <text> — start or resume the loop
• /autoresearch off — stop the loop, keep data
• /autoresearch clear — delete all state and start fresh