Automatic Stateful Prompt Improver

This intercepts your prompts before Claude responds and automatically optimizes them using the prompt-learning MCP server, which maintains an embedding-indexed history to learn what works over time. It triggers on complex tasks, technical outputs, or anything precision-critical, running 3-20 iterations of refinement using APE, OPRO, and DSPy patterns. The feedback loop records success rates to improve future optimizations. It's overkill for simple questions but genuinely useful when you're building reusable prompts or need structured output. The anti-patterns section is honest about where this goes wrong: over-optimization makes prompts brittle, and you can waste tokens chasing perfection. Requires the prompt-learning MCP server to function.