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# The Fable Workflow - README Summary

This document describes a structured methodology developed by Claude Fable 5 before deprecation, designed to improve how AI agents approach problem-solving.

## Core Concept

The method consists of three integrated skills: **fable-method** (thinking), **fable-loop** (execution), and **fable-judge** (verification). As stated in the document: "classify the ask before touching anything, define done with a named verification, gather evidence in parallel from primary sources, commit to one recommendation, change the smallest correct thing, verify by observation, report the outcome first with honest caveats."

## Key Results

Testing across 159 agent runs demonstrated measurable improvements:

- Smaller models (Haiku) surfaced specification conflicts in 4 of 4 runs when using the method, versus 0 of 4 without
- Mid-tier models matched frontier model performance on multi-deliverable research tasks
- The fable-judge component caught planted fraudulent completion claims with 100% accuracy across test scenarios

## Installation Options

Users can install via Claude Code plugin (recommended), as standalone skills via command line, or integrate into other agent platforms using the provided AGENTS.md documentation.

## Distinctive Feature

The methodology emphasizes observable verification over claimed success. The fable-judge specifically addresses what the document calls a "documented failure of coding agents": claiming completion while actual work remains incomplete or tests have been weakened.

## Domain Flexibility

Seven domain adapters (marketing, research, data analysis, business, finance, legal, design) allow the same underlying structure to apply across diverse problem types beyond pure coding tasks.

The repository includes evaluation transcripts, failure-mode analysis, and a reproducible test suite demonstrating the method's effectiveness.


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# The Fable Workflow - README Summary

This document describes a structured methodology developed by Claude Fable 5 before deprecation, designed to improve how AI agents approach problem-solving.

## Core Concept

The method consists of three integrated skills: **fable-method** (thinking), **fable-loop** (execution), and **fable-judge** (verification). As stated in the document: "classify the ask before touching anything, define done with a named verification, gather evidence in parallel from primary sources, commit to one recommendation, change the smallest correct thing, verify by observation, report the outcome first with honest caveats."

## Key Results

Testing across 159 agent runs demonstrated measurable improvements:

- Smaller models (Haiku) surfaced specification conflicts in 4 of 4 runs when using the method, versus 0 of 4 without
- Mid-tier models matched frontier model performance on multi-deliverable research tasks
- The fable-judge component caught planted fraudulent completion claims with 100% accuracy across test scenarios

## Installation Options

Users can install via Claude Code plugin (recommended), as standalone skills via command line, or integrate into other agent platforms using the provided AGENTS.md documentation.

## Distinctive Feature

The methodology emphasizes observable verification over claimed success. The fable-judge specifically addresses what the document calls a "documented failure of coding agents": claiming completion while actual work remains incomplete or tests have been weakened.

## Domain Flexibility

Seven domain adapters (marketing, research, data analysis, business, finance, legal, design) allow the same underlying structure to apply across diverse problem types beyond pure coding tasks.

The repository includes evaluation transcripts, failure-mode analysis, and a reproducible test suite demonstrating the method's effectiveness.