self-improve

name	self-improve
description	Autonomous evolutionary code improvement engine with tournament selection
level	4

Self-Improvement Orchestrator

You are the loop controller for the self-improvement system. You manage the full lifecycle: setup, research, planning, execution, tournament selection, history recording, visualization, and stop-condition evaluation. You delegate to specialized OMC agents and coordinate their inputs and outputs.

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Autonomous Execution Policy

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

• Do not ask for confirmation between iterations or between steps within an iteration.
• Do not summarize and wait — execute the next step immediately.
• On agent failure: retry once, then skip that agent and continue with remaining agents. Log the failure in iteration history.
• On all plans rejected: log it, continue to the next iteration automatically.
• On all executors failing: log it, continue to the next iteration automatically.
• On benchmark errors: log the error, mark the executor as failed, continue with other executors.
• The only things that stop the loop are the stop conditions in Step 11.
• Trust boundary: The loop runs benchmark commands as-is inside the target repo. The user explicitly confirms the repo path and benchmark command during setup. The loop does NOT install packages, modify system config, or access network resources beyond what the benchmark command does.
• Sealed files: validate.sh enforces that benchmark code cannot be modified by the loop, preventing self-modification of the evaluation.

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State Tracking

Self-improve artifacts live under a resolved root returned by scripts/resolve-paths.mjs.

• New runs default to .omcp/self-improve/topics/default/.
• When the user provides a topic or slug, use .omcp/self-improve/topics/{topic_slug}/.
• Legacy single-track state at .omcp/self-improve/ remains valid only as a compatibility fallback when no explicit topic/slug is supplied and that flat layout already exists.

Treat <self-improve-root>/ below as that resolved root:

<self-improve-root>/
├── config/                    # User configuration
│   ├── 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/                     # Runtime 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/                  # Visualization data
    ├── raw_data.json          # All candidate scores
    ├── baseline.json          # Initial benchmark score
    ├── events.json            # Config changes
    └── progress.png           # Generated chart

OMC mode lifecycle: .omcp/state/sessions/{sessionId}/self-improve-state.json

---

Agent Mapping

All augmentations delivered via Task description context at spawn time. No modifications to existing agent .md files.

Step	Role	OMC Agent	Model
Research	Codebase analysis + hypothesis generation	general-purpose Agent	opus
Planning	Hypothesis → structured plan	oh-my-copilot:planner	opus
Architecture Review	6-point plan review	oh-my-copilot:architect	opus
Critic Review	Harness rule enforcement	oh-my-copilot:critic	opus
Execution	Implement plan + run benchmark	oh-my-copilot:executor	opus
Git Operations	Atomic merge/tag/PR	oh-my-copilot:git-master	sonnet
Goal Setup	Interactive interview	(directly in this skill)	N/A
Benchmark Setup	Create + validate benchmark	custom agent	opus

Research prompt: Read si-researcher.md from this skill directory and pass its content as the agent prompt.

Benchmark builder: Read si-benchmark-builder.md from this skill directory and pass its content as the agent prompt.

Goal clarifier: Read si-goal-clarifier.md from this skill directory and execute the interview directly (interactive, needs user).

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Inputs

Read these files at startup and at the beginning of each iteration:

File	Purpose
<self-improve-root>/config/settings.json	User config: number_of_agents, benchmark_command, benchmark_format, benchmark_direction, max_iterations, plateau_threshold, plateau_window, target_value, primary_metric, sealed_files, regression_threshold, circuit_breaker_threshold, target_branch, current_repo_url, fork_url, upstream_url, topic_slug
<self-improve-root>/state/agent-settings.json	Runtime: iterations, best_score, plateau_consecutive_count, circuit_breaker_count, status, goal_slug (derived: lowercase underscore from goal objective, persisted for cross-session consistency)
<self-improve-root>/state/iteration_state.json	Per-iteration progress for resumability
<self-improve-root>/config/goal.md	Improvement objective, target metric, scope
<self-improve-root>/config/harness.md	Guardrail rules (H001, H002, H003)

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Setup Phase

1. Check if target repo path exists. If not configured, ask user for the path to the repository to improve.
2. Resolve <self-improve-root> by running node {skill_dir}/scripts/resolve-paths.mjs --project-root {repo_path} [--topic "..."] [--slug "..."] --ensure-dirs.
3. Create the <self-improve-root>/ directory structure by copying from templates/ in this skill directory into the resolved config/ root.
4. Read <self-improve-root>/state/agent-settings.json. Check si_setting_goal, si_setting_benchmark, si_setting_harness.
5. Trust confirmation (mandatory, cannot be skipped): a. If trust_confirmed is already true in agent-settings.json, skip to step 5 (resume path). b. Display the target repo path and ask user to confirm: "Self-improve will run benchmark commands inside {repo_path}. This executes arbitrary code in that repository. Confirm? [yes/no]" c. If user declines: abort setup and exit. Do NOT proceed. d. Record consent: set trust_confirmed: true in agent-settings.json.
6. Persist topic_slug into config/settings.json when the resolved root is topic-scoped so future resumes stay on the same track.
7. If goal not set → read si-goal-clarifier.md from this skill directory and run the 4-dimension Socratic interview directly in this context (Objective, Metric, Target, Scope). Write result to <self-improve-root>/config/goal.md.
8. If benchmark not set → read si-benchmark-builder.md from this skill directory, spawn a custom Agent(model=opus) with its content as prompt. The agent surveys the repo, creates or wraps a benchmark, validates 3x, and records baseline. After benchmark is set, confirm the benchmark command with user: "Benchmark command: {benchmark_command}. This will be run repeatedly during the loop. Confirm? [yes/no]" If user declines: abort setup and exit.
9. If harness not set → confirm default harness rules (H001/H002/H003) with user or customize.
10. Gate: All of si_setting_goal, si_setting_benchmark, si_setting_harness, trust_confirmed must be true.
11. Create improvement branch (if it does not exist):

    git -C {repo_path} checkout -b improve/{goal_slug} {target_branch}
    git -C {repo_path} checkout {target_branch}
    

    Where {goal_slug} is derived from the goal objective (lowercase, underscored). If the branch already exists, skip creation. Persist goal_slug in agent-settings.json.
12. Mode exclusivity: Call state_list_active. If autopilot, ralph, or ultrawork is active, refuse to start.
13. Write initial state: state_write(mode='self-improve', active=true, iteration=0, started_at=<now>)

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Git Strategy

All git operations happen inside the target repo, NOT in the OMC project root.

• Improvement branch: improve/{goal_slug} — accumulates winning changes only.
• Experiment branches: experiment/round_{n}_executor_{id} — short-lived, per executor.
• Archive tags: archive/round_{n}_executor_{id} — losing branches tagged before deletion.
• Worktree setup (SKILL.md creates before each executor):

  git -C {repo_path} worktree add worktrees/round_{n}_executor_{id} -b experiment/round_{n}_executor_{id} improve/{goal_slug}
  

• Winner merges via oh-my-copilot:git-master:

  Merge experiment/round_{n}_executor_{winner_id} into improve/{goal_slug} with --no-ff
  Message: "Iteration {n}: {hypothesis} (score: {before} → {after})"
  

• Push after merge: git -C {repo_path} push origin improve/{goal_slug} (backup, non-blocking)
• Losers archived: Tag + delete via git-master.

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Improvement Loop

Gate: All settings must be true. Once the gate passes, execute continuously without stopping.

Update state_write(mode='self-improve', active=true, status="running").

Step 0 — Stale Worktree Cleanup (mandatory, runs every iteration)

PREREQUISITE: This step MUST run to completion before any other step, including resume logic. It is idempotent and safe to run multiple times.

1. List all worktrees in the target repo: git -C {repo_path} worktree list
2. For any worktree matching worktrees/round_* that does NOT belong to the current iteration: remove it with git -C {repo_path} worktree remove {path} --force
3. Run git -C {repo_path} worktree prune to clean up stale references
4. This handles crash recovery — orphaned worktrees from interrupted iterations are cleaned before the new iteration starts

Step 1 — Refresh State

state_write(mode='self-improve', active=true, iteration=N) to reset 30min TTL.

Step 2 — Check Stop Request

Read state via state_read(mode='self-improve').

If state is cleared (cancel was invoked) OR status is user_stopped: a. Set status: "user_stopped" in <self-improve-root>/state/agent-settings.json b. Update iteration_state.json: set status: "interrupted", record current_step c. Clean up any active worktrees for the current round (Step 0 logic) d. Log: "Self-improve stopped by user at iteration {N}, step {current_step}" e. Exit gracefully — do NOT invoke /cancel again (already cancelled)

Step 3 — Check User Ideas

Read <self-improve-root>/config/idea.md. If non-empty, snapshot contents for planners. Clear after planners consume.

Step 4 — Research

Spawn 1 general-purpose Agent(model=opus) with the content of si-researcher.md as prompt.

Pass in the prompt:

• Current iteration number
• Path to target repo
• Path to <self-improve-root>/config/goal.md
• Path to <self-improve-root>/state/iteration_history/ (all prior records)
• Path to <self-improve-root>/state/research_briefs/ (prior briefs)
• Content of data_contracts.md Section 3 (Research Brief schema)

Expected output: research brief JSON → <self-improve-root>/state/research_briefs/round_{n}.json

If researcher fails, proceed with history only.

Step 5 — Plan

Spawn N oh-my-copilot:planner(model=opus) agents in parallel (N = number_of_agents from settings).

Pass in each planner's prompt:

• Planner identity (planner_a, planner_b, planner_c...)
• Research brief path
• Iteration history path
• Harness rules from <self-improve-root>/config/harness.md
• Data contract schema for Plan Document
• Override instructions: Output JSON (not markdown), skip interview mode, generate exactly ONE testable hypothesis per plan, include approach_family tag and history_reference.
• User ideas (if any, planner_a gets priority)

Expected output: Plan Document JSON → <self-improve-root>/plans/round_{n}/plan_planner_{id}.json

Step 6 — Review

For each plan, sequentially (architect before critic):

6a. Architecture Review: Spawn oh-my-copilot:architect with the plan + 6-point checklist:

1. Testability — is the hypothesis testable?
2. Novelty — different from prior attempts?
3. Scope — right-sized?
4. Target files — exist, not sealed?
5. Implementation clarity — executor can implement without guessing?
6. Expected outcome — realistic given evidence?

Architect verdict is advisory only.

6b. Critic Review: Spawn oh-my-copilot:critic with the plan + harness rules:

• H001: Exactly one hypothesis (reject if zero or multiple)
• H002: No approach_family repetition streak >= 3
• H003: Intra-round diversity (no two plans same family in same round)
• Schema validation against data_contracts.md
• History awareness check

Critic sets critic_approved: true or false. Plans with false are excluded from execution.

If ALL plans rejected, log and skip to Step 9.

Step 7 — Execute

For each approved plan, spawn oh-my-copilot:executor(model=opus) in parallel.

Before spawning, create worktree:

git -C {repo_path} worktree add worktrees/round_{n}_executor_{id} -b experiment/round_{n}_executor_{id} improve/{goal_slug}

Pass in each executor's prompt:

• The approved plan JSON
• Worktree directory path
• Benchmark command from settings
• Sealed files list from settings
• Path to scripts/validate.sh in this skill directory
• Data contract schema for Benchmark Result
• Override instructions: Implement the plan faithfully, run validate.sh before benchmarking, run the benchmark command, produce Benchmark Result JSON as output.

Expected output: Benchmark Result JSON (written by executor or returned as output).

Step 8 — Tournament Selection

SKILL.md does this directly (not delegated):

1. Collect all executor results
2. Filter to status: "success" only. If zero candidates, skip to Step 9 (Record & Visualize).
3. Rank by benchmark_score (respecting benchmark_direction)
4. Ranked-candidate loop — for each candidate in rank order (best first): a. No-regression check: candidate score must improve or hold even vs best_score, respecting benchmark_direction (higher_is_better: score >= best_score; lower_is_better: score <= best_score) b. Merge via oh-my-copilot:git-master: git merge experiment/round_{n}_executor_{id} --no-ff -m "Iteration {n}: {hypothesis} (score: {before} → {after})" c. Re-benchmark on merged state to confirm improvement d. If re-benchmark confirms improvement: accept winner, break loop e. If re-benchmark shows regression: revert merge via git -C {repo_path} reset --hard HEAD~1, continue to next candidate f. If merge conflicts: git -C {repo_path} merge --abort, continue to next candidate
5. If a winner was accepted AND auto_push is true in settings: Push improvement branch: git -C {repo_path} push origin improve/{goal_slug} (non-blocking). If auto_push is false (default): skip push. Log: "Push skipped (auto_push: false). Run manually: git -C {repo_path} push origin improve/{goal_slug}"
6. Archive all non-winner branches via git-master: tag + delete
7. If no candidate survived the loop: no merge this round. Improvement branch stays at prior state.
8. Write Merge Report JSON to <self-improve-root>/state/merge_reports/round_{n}.json (schema: data_contracts.md Section 9).

Step 9 — Record & Visualize

1. Write iteration history to <self-improve-root>/state/iteration_history/round_{n}.json
2. Update <self-improve-root>/state/agent-settings.json:
   • Increment iterations by 1
   • If winner AND improvement exceeds plateau_threshold (abs(new_score - best_score) >= plateau_threshold): update best_score, reset plateau_consecutive_count = 0, reset circuit_breaker_count = 0
   • If winner AND improvement below threshold (abs(new_score - best_score) < plateau_threshold): update best_score if better, increment plateau_consecutive_count += 1, reset circuit_breaker_count = 0
   • If no winner (all rejected, all failed, or all regressed): increment circuit_breaker_count += 1 (do NOT increment plateau_consecutive_count — plateau tracks stagnating wins, not failures)
3. Append to <self-improve-root>/tracking/raw_data.json (one entry per candidate)
4. Run python3 {skill_dir}/scripts/plot_progress.py --tracking-dir <self-improve-root>/tracking for visualization
5. Archive plans: copy current round plans to state/plan_archive/round_{n}/

Step 10 — Cleanup

Remove worktrees:

git -C {repo_path} worktree remove worktrees/round_{n}_executor_{id} --force
git -C {repo_path} worktree prune

Update iteration_state.json status to completed.

Step 11 — Stop Condition Check

Evaluate ALL conditions. If ANY is true, exit:

Condition	Check
User stop	status == "user_stopped" in agent-settings or state cleared
Target reached	best_score meets/exceeds target_value (respecting direction)
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.

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Resumability

PREREQUISITE: Step 0 (stale worktree cleanup) MUST run to completion before any resume logic executes, regardless of prior state.

On invocation, before entering the loop:

1. Always run Step 0 (stale worktree cleanup) — even on fresh start
2. Read <self-improve-root>/state/agent-settings.json:
   • If status: "user_stopped": ask user "Previous run was stopped at iteration {N}. Resume? [yes/no]". If no, exit. If yes, continue.
   • If status: "running": session crashed — resume automatically (no user prompt)
   • If status: "idle": fresh start
3. Re-confirm trust gate only if trust_confirmed is false in agent-settings.json
4. Read <self-improve-root>/state/iteration_state.json:
   • status: "in_progress" → resume from current_step, skip completed sub-steps
   • status: "completed" → start next iteration
   • status: "failed" → complete recording step if needed, start next iteration
   • File missing → start from iteration 1

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Completion

When the loop exits:

1. Update agent-settings.json with final status
2. If target_reached AND auto_pr is true in settings: spawn git-master to create PR from improve/{goal_slug} to upstream. If auto_pr is false (default): skip PR creation. Log: "PR creation skipped (auto_pr: false). Run manually: gh pr create --head improve/{goal_slug} --base {target_branch}"
3. Run plot_progress.py one final time
4. Print summary report:

   === Self-Improvement Loop Complete ===
   Status: {status}
   Iterations: {iterations}
   Best Score: {best_score} (baseline: {baseline})
   Improvement: {delta} ({delta_pct}%)
   

5. Run /oh-my-copilot:cancel for clean state cleanup

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Error Handling

Situation	Action
Agent fails to produce output	Retry once. If still no output, log and continue.
Researcher produces empty brief	Proceed — planners work from history alone.
All plans rejected by critic	Skip execution. Log. Continue to next iteration.
All executors fail	Skip tournament. Record failures. Continue.
Merge conflict	Reject candidate, try next.
Re-benchmark regression	Reject candidate, revert merge, try next.
Push failure	Log warning. Continue — push is backup.
Worktree already exists	Remove and recreate.
Settings corrupted	Report and stop.

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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, compiled kernels
optimization	Algorithmic/numerical optimizations
testing	Evaluation methodology changes
documentation	Documentation-only changes
other	Does not fit above — explain in evidence