Agent skill

Zero Shot

by jongwony in jongwony/epistemic-protocols

A skill your agent uses when the user asks to "check zero-shot", "audit few-shot anchoring", "find example anchoring", or invokes /zero-shot.

MITAuto-check passedAI & LLM Engineering

Install Zero Shot

skills CLI
$ npx skills add jongwony/epistemic-protocols --skill zero-shot -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jongwony/epistemic-protocols zero-shot --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jongwony/epistemic-protocols.git skills-src && mkdir -p .claude/skills && cp -r skills-src/epistemic-cooperative/skills/zero-shot .claude/skills/zero-shot && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
zero-shot
GitHub stars
173
Token cost
~1.7k tokens
SKILL.md length
768 words
Files
1
Skills in repo
29
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the user asks to "check zero-shot", "audit few-shot anchoring", "find example anchoring", or invokes /zero-shot.

  • The user asks to check zero-shot
  • SKILL.md covers Purpose, Inputs, Scope and What to evaluate, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Audit few-shot anchoring

What it does

Zero Shot is an agent skill from jongwony/epistemic-protocols. 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.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: Epistemic protocols for Claude Code — structure human-AI interaction quality at every decision point - https://epistemic-protocols.com. The licence is MIT.

When your agent uses it

  • The user asks to check zero-shot
  • Audit few-shot anchoring
  • Find example anchoring
  • Invokes /zero-shot

Example prompts

  • “check zero-shot”
  • “audit few-shot anchoring”
  • “find example anchoring”
  • “/zero-shot”

Requirements

  • Pre-approved tools (allowed-tools): Read, Grep, Glob

What it can do on your machine

Read from SKILL.md and the folder at commit af5aa79. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Zero Shot loads about 1.7k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 768 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jongwony/epistemic-protocols at commit af5aa79, republished under its MIT licence (© jongwony). 768 words, ~1,673 tokens.

Download SKILL.mdSave it as .claude/skills/zero-shot/SKILL.md (or your agent's skills folder).
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.
allowed-tools
Read, Grep, Glob
user_invocable
true

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.

Show full SKILL.md (240 more words)Show less

Output

Emit a single JSON object as the final assistant message.

json
{
  "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:

SeveritySurface
highRules sections, Phase prose, agent system prompts — places where anchoring materially narrows downstream LLM application
mediumDistinctions, Composition notes, scope-boundary descriptions in supporting sections
lowBorderline 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

SurfaceMechanismFailure mode handled
Deterministic static checksLiteral pattern matching and structural validationStructural drift between coupled artifacts; literal pattern leaks
zero-shotClaude-judge semantic review of LLM-facing proseFew-shot anchoring drift that survives structural validity
white-bearSibling semantic auditUnnecessary 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.

© jongwony, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in epistemic-cooperative/skills/zero-shot of jongwony/epistemic-protocols.

Open the folder on GitHubat commit af5aa79

Compare with similar skills

Zero Shot next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Zero Shot this skilljongwony/epistemic-protocols173—~1.7kAutomated safety check: PassMIT
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Prompt Engineering Patternsynulihao/AgentSkillOS61715 repos~1.7kAutomated safety check: PassNone
Patch CreationPiebald-AI/tweakcc2.5k—~1.6kAutomated safety check: PassMIT
LLM Application DevMoizIbnYousaf/ai-agent-skills1.1k2 repos~1.3kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2604 repos~1.4kAutomated safety check: PassCustom licence

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Questions about Zero Shot

What does Zero Shot do?

A skill your agent uses when the user asks to "check zero-shot", "audit few-shot anchoring", "find example anchoring", or invokes /zero-shot. Zero Shot is an agent skill from jongwony/epistemic-protocols. Use when the user asks to "check zero-shot", "audit few-shot anchoring", "find example anchoring", or invokes /zero-shot.

When should I use Zero Shot?

Zero Shot fits situations like: the user asks to check zero-shot; audit few-shot anchoring; find example anchoring; invokes /zero-shot.

How do I install Zero Shot in Claude Code?

Run `npx skills add jongwony/epistemic-protocols --skill zero-shot -a claude-code`. Or copy the skill folder (epistemic-cooperative/skills/zero-shot in jongwony/epistemic-protocols) into .claude/skills/zero-shot in your project. Claude Code loads it when a task matches its description.

How do I install Zero Shot in Codex?

Run `npx skills add jongwony/epistemic-protocols --skill zero-shot -a codex`. Or copy the skill folder (epistemic-cooperative/skills/zero-shot in jongwony/epistemic-protocols) into .agents/skills/zero-shot in your project. Codex loads it when a task matches its description.

Can I use Zero Shot in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jongwony/epistemic-protocols --skill zero-shot -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/zero-shot, .gemini/skills/zero-shot, .github/skills/zero-shot and .opencode/skills/zero-shot in your project.

What does Zero Shot need to run?

SKILL.md names no scripts, command-line tools or credentials: Zero Shot is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Grep, Glob.

Does Zero Shot access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Zero Shot safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Zero Shot use?

Zero Shot is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Zero Shot use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Zero Shot?

Skills that share tags, products or a category with Zero Shot: Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars), Prompt Engineering Patterns (ynulihao/AgentSkillOS, 617 stars), Patch Creation (Piebald-AI/tweakcc, 2.5k stars) and LLM Application Dev (MoizIbnYousaf/ai-agent-skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Zero Shot?

jongwony (a GitHub user) maintains it in jongwony/epistemic-protocols, which has 173 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 8, 2026.

Source: jongwony/epistemic-protocols on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.