The Fable Workflow

How Claude Fable 5 worked, written down before it was gone. With the eval that keeps it honest.

In its final days before deprecation, Claude Fable 5 distilled its own way of approaching problems into a set of skills any model can run: 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. Then it tested that distillation against itself, adversarially, across 159 agent runs, and kept the failures in the log.

Most agent instruction files tell the model what to value ("be careful, verify your work"). This one tells it what to do, in what order, with thresholds, so a mid-tier model can follow it literally. Three skills, one philosophy: think (fable-method), act (fable-loop), prove (fable-judge). Every rule exists because a test failed without it; every claim below links to the committed judge transcript that backs it.

Results at a glance

Eight eval rounds, 159 agent runs, blind LLM judges that verify by diffing and executing, never by reading reports. Read the evidence as stories: eval/cases/ has one case study per scenario (the exact problem, what each agent actually did, who passed); start with the surprise trap. Full log: eval/RESULTS.md · raw judge outputs: eval/results/

That last row is deliberate: the method's value concentrates at traps (authority conflicts, false completion claims, weak executors, unattended runs), not everywhere. The nulls are reported with the wins, because a results log that only contains wins would not be worth trusting.