self-improve

# 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 |