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
Critical regression discovered during audit. Separate reconciliation found 26 issues had gone untracked despite a prior 90% coverage estimate. Audio container duration mismatches resolved in 2 of 40 clips.
Critical gap: 30s silence produced fluent hallucinated dictation with all 11 vocab hints and ok:true. Guard updated to reject the edge case. 38 audit issues processed into 22 registry rows and 8 owner cards.
Minor maintenance commit: baseline loader fixed and Fable model runner registered.
Anchor-based paraphrase matching added for better review alignment. Publication-review pipeline now gates on holdout set. Security findings from external review resolved.
Multi-agent training loop unstable, likely due to memory constraints. 3 of 5 monitoring tasks completed; primary job did not finish. Root cause unresolved.
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.
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.
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.