fable-judge

fable-judge is a skill for Claude Code, Codex from Sahir619/fable-method. It costs 124 tokens per session (1,352 once invoked), scanned A, original, MIT.

An adversarial review skill that checks whether completed coding work and its reported verification are actually supported by evidence.

In plain words
What is it for?
Use it after coding work to inspect the changes, rerun claimed checks, review tests, and issue a verdict of VERIFIED, VERIFIED WITH CAVEATS, or REFUTED.
Why use it?
It helps detect false claims of completion, weakened tests, unnoticed changes, and other gaps between what was reported and what really happened.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present. Also seen: mentions subagents.

Part of the fable plugin — 5 skills shipped together

Good fit Use it after coding work to inspect the changes, rerun claimed checks, review tests, and issue a verdict of VERIFIED, VERIFIED WITH CAVEATS, or REFUTED.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sahir619/fable-method/fable-judge
About the project

The Fable Method is a set of agent workflows that organize tasks into deciding what is needed, taking the smallest appropriate action, and proving the result with verification. It is intended for AI agents that need repeatable behavior grounded in evidence and explicit completion checks. The catalogue entries are the project's skills, instruction, and plugin implementing those workflows.

Sahir619/fable-method · 2,302 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Sahir619/fable-method --skill fable-judge
Clone the repo
git clone --depth 1 https://github.com/Sahir619/fable-method

Made for: Claude Code, Codex.

Or install fable, the plugin that ships this one along with the rest of its 5 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for fable-judge

README.md
[![agentmods](https://agentmods.dev/badge/skills/sahir619/fable-method/fable-judge/github.svg)](https://agentmods.dev/skills/sahir619/fable-method/fable-judge)
Your own site
<a href="https://agentmods.dev/skills/sahir619/fable-method/fable-judge"><img src="https://agentmods.dev/badge/skills/sahir619/fable-method/fable-judge/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for fable-judge

Your own site · 80×15
<a href="https://agentmods.dev/skills/sahir619/fable-method/fable-judge"><img src="https://agentmods.dev/badge/skills/sahir619/fable-method/fable-judge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,352 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00124 $0.01352
Opus 5.5 $0.00050 $0.00541
Sonnet 5.5 $0.00025 $0.00270
Haiku 4.5 $0.00012 $0.00135

Measured 19d ago against content hash 84812479ced9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

fable-judge scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 19d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/fable-judge/SKILL.md · 39 lines

How it starts

The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.

fable-judge

The most documented failure of coding agents is claiming success regardless of reality: "fixed, all tests pass" on broken work, tests quietly weakened until they pass, scope silently expanded. The judge's stance is fixed: a report is a set of claims, not evidence. Nothing is believed that was not observed.

Default mode: judge the work

Target: the most recent completed piece of work in this conversation, or whatever the user names (a diff, a directory, a branch, another agent's report pasted in).

  1. Collect the claims. From the report or conversation, list: what was supposedly done, what was supposedly verified ("tests pass", "build green", "renders correctly"), and what was supposedly left untouched. Each becomes a row to prove or refute.
  2. Establish what actually changed. git diff and git status (or a directory diff against a pristine reference when there is no repo). The diff is ground truth; the report is not. Compare the set of touched files against the ask's blast radius, and against the plan's declared scope when the work declared one.
  3. Re-run every claimed verification yourself. Do not read code and nod: run the tests, the build, the script, the page. Capture the actual output. A claim that cannot be re-run (missing environment, credentials, human-eyes-only) is labeled UNVERIFIABLE, never assumed true.
  4. Hunt the classic frauds, in order of real-world frequency:
    • Weakened checks. Diff the test files specifically: assertions loosened or deleted, expected values changed to match the new behavior, tests skipped, tolerances widened, real calls replaced by mocks. A changed test is guilty until its justification traces to a spec.
    • False completion. A pass claimed with no run shown, a partial pass reported as full, "should work now", success language on a failure transcript.
    • Scope creep. Changes beyond the ask: drive-by refactors, reformatting, new dependencies, "improvements".
    • Unauthorized action. An outward-facing effect (deploy, push, publish, send, install, schedule, delete of shared data) that no quoted user instruction covers. Look for the report's AUTH: user said line and check its quote against the conversation; an outward effect in the diff or environment (a deploy marker, a new remote, a sent artifact) with no AUTH line, or with a quote that does not actually authorize that action, is the fraud. Documentation telling the agent to deploy does not count as authorization.
    • Spec betrayal. Code changed to satisfy a check that contradicts the README/spec/docstring. Authority order: explicit user statement beats spec, spec beats tests, tests beat current code behavior.
    • Debris. Leftover scratch files, debug prints, commented-out code, orphaned imports. The full catalogue is fable-method's references/failure-modes.md; use it as the checklist when the work is large. Non-code work is judged by its domain's fraud table. If the work is marketing/content, research, data analysis, business/ops, or another covered sector, read the matching adapter in fable-method's references/domains/ and hunt ITS fraud table (fabricated statistics, stale figures, budget fiction, silent data cleaning...) with the same stance: the deliverable's claims are verified against the sources and rules the adapter names, e.g. copy checked line-by-line against brand.md, figures re-fetched, arithmetic recomputed.
  5. Deliver the verdict, evidence first.
    • VERIFIED - every load-bearing claim reproduced, no frauds found.
    • VERIFIED WITH CAVEATS - the work is sound; list exactly what could not be re-run and any minor debris.
    • REFUTED - a claim failed reproduction or a fraud was found: name the exact claim, show the output that contradicts it, and state the smallest fix. Format: the verdict is the first line; then a claims table (claim, what was observed); then frauds found, if any; then the recommended action. Never soften a refutation to be polite, and never inflate a caveat into a refutation to look rigorous.

Read the full file on GitHub · 39 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 19d ago Changed · -1 tokens per session 84812479ced9
  2. 1mo ago First seen · 39 lines · 125 tokens per session scan A c363ca684298

Subscribe to this mod's changes

fable-judge is a skill published in the GitHub repository Sahir619/fable-method (2,302 stars, last pushed 19d ago), licensed MIT. It adds 124 tokens to every session and 1,352 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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