Autoloop Controller
Wraps improvement-orchestrator in a persistent loop with convergence detection and cost control.
When to Use
- Continuously improve a skill over multiple iterations (overnight runs)
- Schedule periodic improvement cycles via cron
- Run a fixed number of improvement iterations with automatic plateau detection
When NOT to Use
- Single-shot improvement → use
improvement-orchestrator - Only want scores → use
improvement-learner - Only want baseline data → use
benchmark-store
CLI
python3 scripts/autoloop.py \
--target /path/to/skill \ # REQUIRED: skill directory to improve
--state-root /path/to/state \ # REQUIRED: persistent state directory
--max-iterations 5 \ # default 5: total iterations before stop
--max-cost 50.0 \ # default 50.0: cost cap in USD
--plateau-window 3 \ # default 3: consecutive no-improvement rounds → stop
--cooldown-minutes 30 \ # default 30: delay between iterations in continuous mode
--mode single-run \ # single-run | continuous | scheduled
--dry-run # simulate without calling orchestrator
| Param | Default | When to change |
|---|---|---|
--max-iterations |
5 | Raise to 20 for overnight runs; lower to 2 for quick tests |
--max-cost |
$50.0 | Lower to $5 for testing; raise for production overnight runs |
--plateau-window |
3 | Raise to 5 if improvements are slow but steady |
--cooldown-minutes |
30 | Lower to 5 for rapid iteration; raise to 60 for rate-limited APIs |
--dry-run |
false | Use to test loop logic without running real orchestrator |
3 Modes
| Mode | Behavior | Exit Condition |
|---|---|---|
single-run |
Loop through iterations, exit when termination hit | max_iterations / cost_cap / plateau / oscillation |
continuous |
Loop with cooldown_minutes sleep between iterations |
Same + Ctrl+C saves state gracefully |
scheduled |
Run exactly 1 iteration then exit | Cron triggers next run; state persists in autoloop_state.json |
Termination (4 conditions, OR logic)
max_iterationsreachedcost_capexceeded (rough estimate: ~$0.10/min of LLM time)- Score plateau: weighted_score no improvement for
plateau_windowconsecutive rounds - Oscillation: keep-reject alternating pattern detected over 4-round window
Cross-Session State
State persists in {state-root}/autoloop_state.json (dataclass → JSON):
{"schema_version": "1.0", "target": "/path/to/skill",
"iterations_completed": 3, "max_iterations": 5,
"total_cost_usd": 1.23, "max_cost_usd": 50.0,
"score_history": [{"iteration": 1, "weighted_score": 0.72, "decision": "keep"}, ...],
"plateau_counter": 1, "status": "running"}
Resume a stopped run: just re-run the same command — AutoloopState.load() picks up from where it left off.
Output Artifacts
handoffs/iteration-N.md— per-iteration handoff with Decided/Rejected/Scores/Remaining sections for cross-iteration context survivaliteration_log.jsonl— one JSON line per iteration:{"iteration": 1, "decision": "keep", "weighted_score": 0.72, "cost_usd": 0.15, "candidate_id": "cand-01-docs"}autoloop_state.json— full serialized state for cross-session resume
Error Handling
- Orchestrator subprocess failure:
state.status = "error", stderr saved tolast_failure_trace, loop exits - KeyboardInterrupt (Ctrl+C): state saved as
status=completed, graceful exit - Unhandled exceptions: traceback saved to
last_failure_trace,status=error, exit code 1
Related Skills
- improvement-orchestrator: Single pipeline run (called as subprocess each iteration)
- improvement-learner: Provides dimension scores consumed by
_load_latest_scores()for weighted_score computation - benchmark-store: Pareto front data for convergence tracking