# prompt-deep-optimizer Iteratively optimizes prompts that live in code and run repeatedly — system prompts, agent instruction blocks, tool templates — via a 16-pass audit in parallel bundles, looping to convergence. For prompts that are *infrastructure*: system prompts, agent instruction blocks, tool-call templates, workflow scaffolds — text that runs thousands of times and whose defects compound. Runs a 16-pass audit in 5 parallel bundles, applies every Medium+ fix, and loops to convergence under the shared family contract. Beyond the rewrite, it picks an **algorithm recommendation** for training-data-driven optimization — APE, OPRO, MIPROv2, GEPA, PromptBreeder, ProTeGi, TextGrad, or EvoPrompt — or reports "structural-only" when no training data exists to drive one. **Use it for:** "optimize this system prompt", "run pdo", a production prompt that produces inconsistent output, an agent instruction block that needs hardening before it ships. **Not for:** a one-off conversational prompt (prompt-helper) · a SKILL.md ([skill-optimizer](/skills/skill-optimizer/)) · prose documents (document-critique) · an llms file ([llms-deep-optimizer](/skills/llms-deep-optimizer/)).