version: "1.0.0"

Experiment Loop (Metric-Driven Optimization)

Autonomous benchmark-driven experiment loop: propose a change, measure the metric, keep what improves it, revert what doesn't, repeat. Git is the ledger — every kept improvement is a commit. Every discarded attempt is a clean revert.

Setup

Before iterating, call init_experiment to configure the session:

• metric_name: the scalar to optimize (e.g. test_duration_ms, accuracy_pct, bundle_size_kb)
• metric_direction: "minimize" or "maximize"
• objective: plain-English goal (e.g. "Reduce test suite wall-clock time below 10s")
• benchmark_command: shell command that must print METRIC <name>=<number> to stdout
• checks_command (optional): correctness guard (e.g. npm test or python -m pytest) — a failed check blocks a keep even if the metric improved (Goodhart's Law prevention)

Loop

1. Propose: read autoresearch.ideas.md for queued ideas; pick the most promising and describe the change
2. Implement: make the code change using write_file or bash_exec
3. Measure: call run_experiment with a description of what was tried
4. Decide: call log_experiment with:

• decision="keep" if metric improved AND checks passed → git commit
• decision="discard" if metric regressed or was flat → git revert
• decision="checks_failed" if metric improved but correctness check failed → revert
• decision="crash" if the benchmark itself crashed → revert

1. Record ideas: append promising-but-deferred ideas to autoresearch.ideas.md
2. Repeat

Benchmark Contract

The benchmark command MUST print at least one METRIC name=number line to stdout:

METRIC test_duration_ms=4231
METRIC memory_mb=128

Secondary metrics are captured automatically. The primary metric is whatever metric_name was set to in init_experiment.

Segment-Aware Baselines

Call init_experiment again mid-session to start a new baseline segment — useful when pivoting to a different optimization goal. Old results are preserved in autoresearch.jsonl with their original segment index.

Auto-Resume

When context resets, re-read autoresearch.md (current state) and autoresearch.ideas.md (deferred ideas) to resume exactly where you left off. Never lose progress.

Files

Integration with Research Pipeline

Combine with the Autonomous Researcher skill:

1. Use Autonomous Researcher to identify the problem, review literature, and form a hypothesis
2. Use Experiment Loop to validate the hypothesis empirically (implement → measure → keep/discard)
3. Use council to evaluate competing hypothesis directions before committing to a loop direction

Example

{ "tool": "init_experiment", "metric_name": "test_duration_ms", "metric_unit": "ms", "metric_direction": "minimize", "objective": "Cut test suite time below 8000ms", "benchmark_command": "npm test 2>&1 | tail -1 | awk '{print \"METRIC test_duration_ms=\" $NF}'" }
{ "tool": "run_experiment", "description": "Parallelize independent test suites with --runInBand=false" }
{ "tool": "log_experiment", "decision": "keep", "metric_value": 5400, "description": "Parallelize independent test suites" }