zero-shot

name	zero-shot
description	Use when the user asks to "check zero-shot", "audit few-shot anchoring", "find example anchoring", or invokes /zero-shot. Read-only audit of LLM-facing prose: principle over anchoring examples.
user_invocable	true
allowed-tools	Read, Grep, Glob

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.

For each in-scope file, consider every prose passage outside formal blocks and code fences:

A Zero-Shot signal is a passage in LLM-facing prose where a principle is stated alongside few-shot examples or an enumerated example list whose primary effect is anchoring the model to specific instances rather than letting it apply the principle to novel contexts. The boundary test: "would removing this example increase the LLM's latitude in applying the principle to novel contexts, without losing the output-format or behavioral reliability that the containing instruction depends on?" If yes, the example is anchoring and a finding.

Two exemptions keep an example compliant:

• Scope-delineating examples — examples that clarify what falls inside or outside the principle's domain, rather than instantiating its application.
• Reliability-anchoring examples — examples whose primary effect is stabilizing an output format or anchoring a subtle, high-failure-rate behavior. Removing those costs adherence, not latitude.

Examples that instantiate a principle's application outside these two exemptions invite anchoring and are findings.

For each candidate finding, prefer the rewrite that strengthens the principle so application examples become unnecessary; retain a minimal high-signal exemplar when it stabilizes format or a subtle, high-failure-rate behavior. Treat ambiguous cases as severity: low and surface them for human triage.

Output

Emit a single JSON object as the final assistant message.

{
  "summary": {
    "files_audited": 0,
    "findings_total": 0,
    "by_severity": {"high": 0, "medium": 0, "low": 0}
  },
  "findings": [
    {
      "file": "<repo-relative path>",
      "line": 0,
      "severity": "high",
      "excerpt": "<verbatim text from the file — single line or short span>",
      "rationale": "<one sentence: how the example anchors application rather than delineates scope, and how a principle-only restatement would land>",
      "suggested_rewrite": "<a candidate restatement that preserves directive force without anchoring examples>"
    }
  ]
}

Severity calibration:

Severity	Surface
high	Rules sections, Phase prose, agent system prompts — places where anchoring materially narrows downstream LLM application
medium	Distinctions, Composition notes, scope-boundary descriptions in supporting sections
low	Borderline cases where the example may serve scope clarification under one reading and anchoring under another

When zero findings result, emit the JSON object with empty findings array and zero counts. The summary always emits.

Self-application

This SKILL.md is itself LLM-facing prose and so is in scope. The audit may surface findings against the prose above, emitting a suggested_rewrite that preserves directive force like any other finding; the human author decides whether it lands.

Distinction

Surface	Mechanism	Failure mode handled
Deterministic static checks	Literal pattern matching and structural validation	Structural drift between coupled artifacts; literal pattern leaks
zero-shot	Claude-judge semantic review of LLM-facing prose	Few-shot anchoring drift that survives structural validity
white-bear	Sibling semantic audit	Unnecessary competing-target mention drift

Deterministic checks run at pre-commit and CI; this semantic audit runs on-demand via its slash command. Each maintains its own confidence curve.

Confidence

An advisory, human-reviewed instrument. Findings are candidates for an author to weigh, not automatic edits; the audit illuminates the decision and leaves the judgment with the author. Promoting any recurring finding pattern into a deterministic check is a separate, evidence-gated step — it waits on a pattern proving stable across varied prose, not on a single audit run.