autoloop

/autoloop — Autonomous Recursive Improvement

Step 0: Preamble

Before executing, run the shared ProductionOS preamble (templates/PREAMBLE.md).

You are running the /autoloop command. This is an autonomous recursive improvement loop that takes a target and iteratively improves it until convergence.

Input

The user provides:

• Target: A file path, directory, or description of what to improve
• Goal: What "good" looks like (optional -- defaults to "maximize quality score")

Execution Protocol

Step 1: Understand the Target

1. If target is a file path: Read it and assess current state
2. If target is a directory: Scan for key files and assess overall quality
3. If target is a description: Identify what needs to be created or improved

Step 2: Gap Analysis

1. Score current state using the ProductionOS rubric and convergence heuristics already present in this repo
2. Scan ~/repos/ for reference implementations (per CLAUDE.md Auto-Enrichment Protocol)
3. Check ~/.productionos/recursive/reference-corpus/ for similar high-quality outputs
4. Identify specific gaps between current state and goal

Step 3: Initialize Recursion

1. Create session state at ~/.productionos/recursive/recursion-state.json:

   {
     "session_id": "<generated>",
     "target": "<target>",
     "goal": "<goal>",
     "layer": "L17",
     "current_iteration": 0,
     "max_iterations": 10,
     "best_iteration": 0,
     "best_score": 0.0,
     "scores": [],
     "convergence_verdict": "CONTINUE",
     "status": "running"
   }
   

2. Select the appropriate layer:
   • Complex decomposable task -> L16 RecDecomp
   • Quality improvement (default) -> L17 SelfRefine
   • Context too large -> L18 RecSumm
   • Security/factual claims -> L19 RecVerify
   • Plan execution -> L20 PEER

Step 4: Iteration Loop (max 10)

For each iteration:

1. Score: Run confidence scorer on current output
2. Record: Add score to convergence monitor
3. Check Convergence: Run all 5 algorithms from convergence.py:
   • Score delta tracking (stalled if < 0.1 for 2+ iterations)
   • Spectral contraction (converged if cosine > 0.95)
   • Diminishing returns (stalled if DR ratio < 0.15)
   • Oscillation detection (oscillating if sign changes > 60%)
   • EMA velocity (plateau if |EMA delta| < 0.05)
4. If STOP: Return best iteration output
5. If CONTINUE: Apply refinement via the selected layer
6. Quality Gate: Check for monotonic improvement and stop if the loop regresses materially
7. Log: Write metrics to ~/.productionos/recursive/metrics/