Self-Improvement Orchestrator

Autonomous loop controller for evolutionary code improvement. Manages the full lifecycle: setup, research, planning, execution, tournament selection, history recording, and stop-condition evaluation.

When to Use

• You want to iteratively improve a codebase toward a measurable benchmark goal
• Optimization tasks: performance, bundle size, test coverage, accuracy
• Code quality improvement with measurable metrics

When NOT to Use

• No measurable benchmark available
• One-shot fix or feature request → use /omg-autopilot
• Manual, interactive coding → use /ralph

Autonomous Execution Policy

NEVER stop or pause to ask the user during the improvement loop. Once the gate check passes and the loop begins, run fully autonomously until a stop condition is met.

• Do not ask for confirmation between iterations
• On agent failure: retry once, then skip and continue
• On all plans rejected: log it, continue to next iteration
• The only things that stop the loop are the stop conditions in Step 11

State Tracking

All state lives under .omc/self-improve/:

.omc/self-improve/
├── config/
│   ├── settings.json          # agents, benchmark, thresholds, sealed_files
│   ├── goal.md                # Improvement objective + target metric
│   ├── harness.md             # Guardrail rules (H001/H002/H003)
│   └── idea.md                # User experiment ideas
├── state/
│   ├── agent-settings.json    # iterations, best_score, status, counters
│   ├── iteration_state.json   # Within-iteration progress (resumability)
│   ├── research_briefs/       # Research output per round
│   ├── iteration_history/     # Full history per round
│   ├── merge_reports/         # Tournament results
│   └── plan_archive/          # Archived plans (permanent)
├── plans/                     # Active plans (current round)
└── tracking/
    ├── raw_data.json          # All candidate scores
    ├── baseline.json          # Initial benchmark score
    └── events.json            # Config changes

Agent Mapping

Step	Role	Agent	Purpose
Research	Codebase analysis	@explore + @architect	Hypothesis generation
Planning	Hypothesis → plan	@planner	Structured plan per agent
Architecture Review	6-point review	@architect	Advisory review
Critic Review	Harness enforcement	@critic	Approve/reject plans
Execution	Implement + benchmark	@executor	Implement plan faithfully
Git Operations	Merge/tag/PR	@git-master	Atomic merge operations

Setup Phase

1. Check if target repo path exists. If not configured, ask user.
2. Create .omc/self-improve/ directory structure.
3. Read agent-settings.json. Check setup flags.
4. Trust confirmation (mandatory):
   • Display target repo path, ask user to confirm benchmark execution.
   • If declined: abort.
   • Record consent: trust_confirmed: true
5. If goal not set → Run Socratic interview (Objective, Metric, Target, Scope). Write to goal.md.
6. If benchmark not set → Survey repo, create/wrap benchmark, validate 3x, record baseline.
7. If harness not set → Confirm default harness rules (H001/H002/H003).
8. Gate: All settings + trust must be true.
9. Create improvement branch: improve/{goal_slug} from target branch.
10. Write initial state via omg_write_state.

Improvement Loop

Gate: All settings must be true. Execute continuously without stopping.

Step 0 — Stale Worktree Cleanup

Remove orphaned worktrees from prior iterations.

Step 1 — Refresh State

Update state to reset TTL.

Step 2 — Check Stop Request

If state is cleared or status is user_stopped: exit gracefully.

Step 3 — Check User Ideas

Read idea.md. If non-empty, pass to planners.

Step 4 — Research

Spawn @explore + @architect to analyze codebase and generate hypotheses based on goal, history, and prior briefs.

Step 5 — Plan

Spawn N @planner agents in parallel (N = number_of_agents). Each produces a plan with one testable hypothesis, approach_family tag, and history_reference.

Step 6 — Review

For each plan:

• 6a. Architecture Review: @architect with 6-point checklist (testability, novelty, scope, target files, implementation clarity, expected outcome). Advisory only.
• 6b. Critic Review: @critic with harness rules (H001: one hypothesis, H002: no approach_family streak ≥3, H003: intra-round diversity). Sets critic_approved: true/false.

Step 7 — Execute

For each approved plan, spawn @executor in parallel. Each executor works in a git worktree, implements the plan, runs validation, and benchmarks.

Step 8 — Tournament Selection

1. Collect results, filter to status: "success"
2. Rank by benchmark_score (respecting direction)
3. For each candidate (best first):
   • No-regression check vs best_score
   • Merge via @git-master with --no-ff
   • Re-benchmark on merged state
   • If confirmed: accept winner, break
   • If regression: revert merge, try next
   • If conflict: abort merge, try next
4. Archive non-winner branches

Step 9 — Record & Visualize

Write iteration history, update agent-settings (scores, plateau count, circuit breaker), append tracking data.

Step 10 — Cleanup

Remove worktrees, update iteration state to completed.

Step 11 — Stop Condition Check

Condition	Check
User stop	status == "user_stopped"
Target reached	best_score meets/exceeds target_value
Plateau	plateau_consecutive_count >= plateau_window
Max iterations	iterations >= max_iterations
Circuit breaker	circuit_breaker_count >= circuit_breaker_threshold

If NO stop condition: immediately go back to Step 1.

Resumability

On invocation:

1. Always run Step 0 (stale worktree cleanup)
2. Check agent-settings.json:
   • user_stopped: ask to resume
   • running: crashed — resume automatically
   • idle: fresh start
3. Check iteration_state.json: resume from last step if in-progress

Completion

1. Update final status
2. Print summary (status, iterations, best score, baseline, improvement %)
3. Run /cancel for clean state cleanup

Approach Family Taxonomy

Every plan must be tagged with exactly one:

Tag	Description
architecture	Model/component structure changes
training_config	Optimizer, LR, scheduler, batch size
data	Data loading, augmentation, preprocessing
infrastructure	Mixed precision, distributed training
optimization	Algorithmic/numerical optimizations
testing	Evaluation methodology changes
documentation	Documentation-only changes
other	Does not fit above