zero-shot

Zero-Shot Audit

A semantic audit of LLM-facing prose for the Zero-Shot Instruction Preference: state principles, not anchoring examples. Read-only — it emits structured findings and writes no fixes. The human author decides which to rewrite, mark as scope-clarifying, or dismiss.

Purpose

Surface few-shot patches that anchor the model to specific instances rather than letting it apply the principle to novel contexts. Anchoring drift survives deterministic structural checks — it is a meaning-level pattern, so a semantic reviewer catches what literal pattern matching cannot.

Inputs

Manual invocation only (interactive /zero-shot):

• The caller passes target file paths or a glob; with no argument, the skill enumerates the in-scope set under the working tree HEAD.
• Files are read at their working-tree state — the post-edit, pre-commit content the author is about to ship.

Scope

In scope (LLM-facing prose where this principle applies):

• Skill instruction files (*/skills/*/SKILL.md), considered outside formal/definition blocks
• Agent system-prompt files (*/agents/*.md)
• Output-style files

Out of scope (the principle does not apply or examples serve a different purpose):

• Formal-definition blocks within instruction files — regions delimited by ── <NAME> ── headers (FLOW, MORPHISM, TYPES, PHASE TRANSITIONS, and peers). Notation patterns are the content there.
• Fenced code blocks (``` ... ```) — code is content, and example code attached to a definition is part of that definition.
• Human-facing documentation (README files, design notes, reference material) — examples serve human comprehension there.
• Rule-tier and principle-tier prose authored for contributors — where examples may delineate scope rather than instantiate application.
• Session and context substrates outside this audit's surface.

What to evaluate

The principle (stands alone). LLM-facing instructions state principles, not examples. When a rendering rule, behavioral guideline, or structural constraint can be expressed as a principle, it does not need few-shot examples or category-level mapping lists appended to it. Few-shot examples create a soft-table effect — anchoring the model to specific instances rather than letting it apply the principle to novel contexts. A principle that needs examples to be understood is underspecified; the fix is to sharpen the principle, not to patch it with examples.