Instruction Layer Auditor

Debug Your AI's Logic Stack

When an AI agent behaves inconsistently, it’s rarely a single prompt issue—it's usually a layer conflict. Modern AI development involves stacking system prompts, developer instructions, repository-level rules (like AGENTS.md), skill-specific logic, and persistent memory. These layers often collide, leading to "behavior drift" where the agent becomes over-cautious, ignores instructions, or hallucinates constraints.

What it does

The Instruction Layer Auditor acts as a debugger for your agent’s "operating system." It maps out every instruction layer, extracts operational rules, and identifies precise points of failure. Rather than just adding more instructions to fix a bug, this skill helps you prune and normalize your prompt stack for maximum reliability.

• Layer Mapping: Identifies conflicts between system prompts and local repo rules.
• Conflict Detection: Flags direct contradictions and hidden logic loops.
• Normalization: Provides a strategic rewrite plan to move rules to their high-precedence layers.
• Risk Assessment: Predicts when a stack will cause over-verbosity or tool-use failures.

Why use this skill?

Manually auditing thousands of lines of stacked prompts is error-prone. This skill uses a structured framework to find "misplaced" rules—like a global formatting constraint buried in a local memory file—and tells you exactly where they should live to avoid logic bloat.