What problem does it solve?
Prompts often fail because of structural issues rather than wording: a node does multiple jobs, input organization primes the wrong reasoning pattern, the schema places verdicts before reasoning, or prompts accumulate conflicting rules over time. These structural failures produce inconsistent, unstable, or incorrect LLM outputs and increase debugging cost in production agent pipelines.
Core Features & Use Cases
- Define a node's single primary job before writing any prompt and list explicit "must not" responsibilities to avoid mixed responsibilities.
- Design input structure, system/user split, and output schema (reasoning-before-verdict, scores-derived-booleans, enum routing) to improve determinism and routing robustness.
- Diagnose existing prompts with a stepwise audit (boundary check, input structure, system/user split, schema, rules, token budget, prompt decay) and produce concrete fixes and regression risk notes.
- Use cases include designing critic/judge nodes, retrieval sufficiency checks, prompt-change management, and preventing prompt decay in long-lived agents.
Quick Start
Ask the assistant to audit the prompt and return a redesigned prompt that defines one clear job, flattens evidence for holistic reasoning, places reasoning before verdict, constrains routing fields to enums, and lists no more than five critical rules.
