Fineuralab
Darwin Skill Optimization Guide
Use Darwin-style loops to evaluate, improve, test, keep, or roll back AI Skills.
Long-tail guide
Who this is for
Skill maintainers, agent users, and developers who want evidence-based skill improvement.
A skill should improve through evidence rather than vibes. Darwin-style optimization treats a skill as a living artifact: run tasks, observe failures, revise, test again, and keep only changes that make the result better.
Good use cases
Common tasks
- Improve an existing Skill.md.
- Compare two skill versions on the same tasks.
- Create a regression set for a workflow skill.
- Decide when to roll back a change.
Recommended workflow
- Write three to five representative test tasks.
- Run the current skill and record failures.
- Make one focused revision.
- Run the same tasks again and compare results before keeping the change.
When not to use it
- Do not edit many things at once without a test set.
- Do not keep changes just because they sound smarter.
- Do not optimize a skill on examples unrelated to real use.
Related Fineuralab pages
FAQ
What should I test?
Use tasks that represent your real workflow, including edge cases and examples that previously failed.
When should I roll back?
Roll back when a change improves one example but harms the broader task set or makes behavior less predictable.