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autoresearch-dspy implements a generalized AutoResearch ratchet loop using DSPy 3.1.3 and dspy.RLM. It applies Karpathy’s autonomous experiment pattern — propose, execute, evaluate, keep or revert — generalized beyond ML to any optimizable system. Includes confidence-aware classification scoring via the GEPA ConfidenceAdapter.

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When to use

  • Autonomous optimization loops
  • Iterative experiments that keep only measured improvements
  • RLM recursive context exploration
  • Overnight system optimization
  • The Karpathy Loop pattern
  • Classification prompt optimization with confidence scoring
Trigger phrases: autoresearch, ratchet loop, experiment loop, optimize overnight, autonomous research, Karpathy loop, iterative optimization, self-improving code, experiment automation, confidence adapter, classification optimization.

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Core concept

The fundamental ratchet loop:
  1. Propose a change.
  2. Execute the experiment.
  3. Evaluate against the chosen metric.
  4. Keep if it measurably improves; otherwise revert.
Only measured improvements survive, so progress is monotonic by construction.

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Install

gh skill install Qredence/qredence-plugins autoresearch-dspy
The plugin ships under plugins/autoresearch-dspy/ with a single skill at skills/autoresearch-dspy/SKILL.md plus supporting scripts.