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fable-method

Claude Code plugin with four skills for a classify, verify, report workflow

Stars
2.3k
License
MIT
Last commit
Oct 2026
Last release
Jul 2026
Language
Python
Stars, last 15 weeks+2,302
7 Jul 2026: 0 stars14 Jul 2026: 756 stars21 Jul 2026: 1,748 stars28 Jul 2026: 1,935 stars4 Aug 2026: 2,103 stars11 Aug 2026: 2,176 stars18 Aug 2026: 2,204 stars25 Aug 2026: 2,223 stars1 Sept 2026: 2,248 stars8 Sept 2026: 2,255 stars15 Sept 2026: 2,268 stars22 Sept 2026: 2,277 stars29 Sept 2026: 2,287 stars6 Oct 2026: 2,291 stars13 Oct 2026: 2,296 stars20 Oct 2026: 2,302 stars

0 on 7 Jul 20262,302 on 20 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

Packages a stepwise agent workflow as four skills: fable-method (classify the ask, define done, gather evidence, decide, act, verify, report), fable-loop (orchestrated subagent run), fable-judge (adversarial check of finished work) and fable-domain (generates domain adapters). Installs as a Claude Code plugin or Codex skills, with AGENTS.md for other harnesses. The repo includes eval logs from fifteen rounds and 260+ runs, self-described as smoke-test grade.

Who it is for: Claude Code and Codex users who want agents to verify their own work

Strengths

  • Every rule is tied to a committed eval transcript and per-scenario case study
  • fable-judge re-runs claimed checks and diffs changes to catch false completion reports
  • Installs as a Claude Code plugin, Codex skills, or AGENTS.md for other agents
  • Eight domain adapters load lazily; eval nulls and failures are logged beside the wins

Weaknesses

  • Evals are 1-4 runs per cell with LLM judges; the README calls them smoke-test grade
  • No measured lift on ordinary small tasks with capable models
  • Haiku surfaced the skipped-deploy decision in only 1 of 12 runs
  • Seven of the eight domain adapters lack sourced references; medical has none

What it needs

  • no GPU
  • Needs Claude Code or Codex
  • Models: Claude Haiku, Claude Sonnet, Claude Opus

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