Autonomous optimization loop. 3 parallel agents per round, mechanical metrics only.
From godmodenpx claudepluginhub arbazkhan971/godmodeThis skill uses the workspace's default tool permissions.
references/autonomous-loop-protocol.mdreferences/core-principles.mdreferences/results-logging.mdDesigns and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
/godmode:optimize, "make faster", "improve", "optimize"Ask: Goal, Metric (cmd -> single number),
Direction (up/down), Scope (file globs).
# Run metric_cmd 3 times, take median
metric_cmd; metric_cmd; metric_cmd
IF variance >5%: 10 runs, trim outliers, median of 8. If metric variance > 5% across 3 runs: consider Docker isolation for deterministic measurement. Log variance alongside metric in results.tsv. Commit baseline as iteration 0.
METRIC: shell cmd -> single number (target)
GUARD: test_cmd && lint_cmd && build_cmd (must pass)
Change must BOTH improve metric AND pass guard.
Guard failure -> DISCARD (terminal, counts against
2-rework cap).
On start: check .godmode/session-state.json. If resuming (stop_reason is null), restore baseline/round/approach_history and skip to saved round.
After each iteration: atomically save state (round, baseline, current_best, last_kept_commit, consecutive_discards, approach_history, failure_classes) to .godmode/session-state.json.
On completion: set stop_reason in the state file.
Before IDEATE: read .godmode/lessons.md for optimization-specific insights.
After session: append lessons (e.g., "Metric X is I/O-bound, not CPU-bound" or "Table Y too small for index benefit").
WHILE current_round < max_rounds:
1. REVIEW: in-scope files + results.tsv + git log
Read last 10 rows of optimize-failures.tsv before proposing next change. Avoid repeating the most common failure class.
Profile first: identify hotspot before changing.
IF bounded AND remaining < 3: exploit only.
2. HYPOTHESIZE: 3 independent untested changes
(algorithmic > caching > structural)
IF >5 consecutive discards: STUCK RECOVERY
3. DISPATCH 3 AGENTS (parallel, worktrees)
Each: ONE change -> commit -> guard -> 3x verify
Timeout: 5 min per agent. Exceeded -> kill+discard.
4. PICK WINNER: largest improvement
improved + guard pass -> cherry-pick, update baseline
improved + guard fail -> rework (max 2), else discard
no improvement -> discard all
5. LOG to .godmode/optimize-results.tsv
6. STATUS every 5 rounds.
Last 3 keeps <1% -> radical -> compound -> STOP
STOP: target | max rounds | diminishing | guard broken
When stuck or when the search space is wide, test multiple approaches simultaneously:
Trigger: after 2 consecutive single-agent discards, switch to parallel mode. Return to single-agent mode after a successful parallel keep.
Log to results.tsv: round | agent_1_change | agent_1_delta | agent_2_change | agent_2_delta | agent_3_change | agent_3_delta | winner | status
IF >5 consecutive discards:
0. DIAGNOSE: Re-read ALL in-scope files (stale model)
On 3+ consecutive discards: PAUSE. Read the last 3 diffs and test outputs.
Write a 2-sentence diagnosis explaining the shared failure pattern.
Use the diagnosis to pick a fundamentally different approach.
Log the diagnosis to optimize-failures.tsv in the reason column.
1. Try OPPOSITE approach (informed by diagnosis)
2. If opposite fails -> radical rewrite (informed by diagnosis)
3. If radical fails -> accept defeat, log, stop
Discard if: +5 lines AND improvement < 0.5%
Discard if: complexity up AND improvement < 1%
Keep if: lines removed with equal/better metric
Tie-break: fewer lines wins
Append .godmode/optimize-results.tsv:
round agent change metric_before metric_after delta_pct status failure_class
On DISCARD: also append to .godmode/optimize-failures.tsv with failure_class and reason.
KEEP if: metric improved AND guard passed.
DISCARD if: metric worsened OR guard failed.
On discard: git reset --hard HEAD~1.
After DISCARD: read optimize-failures.tsv. Count by failure_class.
If >3 in same class: announce "Approach category exhausted: {class}. Switching strategy."
Before next IDEATE: skip any change similar to the top failure class.
Before KEEP: run metric_cmd 3x. If stdev > |improvement|: DISCARD as noise. "Would this optimization help if the specific bottleneck moved?" If NO → DISCARD.
STOP when FIRST of:
- target_reached: metric meets target
- budget_exhausted: max_rounds reached
- diminishing: 3 consecutive keeps each < 1%
- stuck: >5 consecutive discards after recovery
On failure: git reset --hard HEAD~1. Never pause.
| Failure | Action |
|---|---|
| Metric non-numeric | Pipe through tail -1 or awk |
| Noisy metric (>5%) | 10 runs, trim outliers, median 8 |
| All 3 agents regress | Stuck recovery, opposite approach |
| Agent timeout (>5m) | Kill worktree, discard, next round |
# Profile and benchmark
npm run build -- --profile
npx lighthouse http://localhost:3000 --output=json
pytest --benchmark-only