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
The v2 patch carries sha256 prefix 3006c12e2f665cdf. A gate-enact leg was dispatched to apply the v2 pre-push identity patch and run eight publish-path suites.
No outcomes from the successor leg were visible.
The 51 dead matches were Azure B2C tokens 527 days past expiry. The triage ran across two sessions.
Orchestrator confused reminder queue entries with open decision cards. Owner issued halt at 21:53Z; 12 adjudications from 21:30Z vetoed and preserved as restoration verification evidence.
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.