DSPy Prompt Optimizer
Automated prompt engineering using Stanford's DSPy framework. Optimizes Claude Code skill prompts through bootstrap, copro, and iterative algorithms with cross-validation.
- 11 of 13 optimization targets deployed
- Bootstrap, CoPro, and iterative algorithms
- Cross-validation with dropout regularization
- Background optimization with progress tracking
Activity Timeline
- Fixed fail-open baseline loader; registered Fable runner.
Minor maintenance commit: baseline loader fixed and Fable model runner registered.
- Training datasets expanded; reasoning_effort tuned; holdout gate and security hardening deployed.
Anchor-based paraphrase matching added for better review alignment. Publication-review pipeline now gates on holdout set. Security findings from external review resolved.
- Batch optimization run for 4 agents failed with exit code 144.
Multi-agent training loop unstable, likely due to memory constraints. 3 of 5 monitoring tasks completed; primary job did not finish. Root cause unresolved.
- Hostile-but-fair review framework codified; matching algorithm improved.
Five-criteria review framework with 3-tier severity triage established. Anchor entity extraction combined with char n-grams and keyword Jaccard distance added to matching algorithm. Publication-review pipeline added as optimization target.
- Hostile-but-fair document review framework designed and piloted on blog post.
Five-criteria framework built for pre-publication critique: steelman opposition, weak claims, consistency, scope, evidence gaps. Applied to the agentic coding post; full analysis output not captured.
- Phase 1.5 consistency optimization designed: 5 extraction fields, two-phase enum+COPRO approach.
Enum discovery from 24 existing extractions via Opus categorization, followed by COPRO optimization (9 calls/field). Checkpoint gates added before Phase C and Phase 2b to control compute spend.