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Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.
Passed 14 of 18 test cases with the skill, and 4 of 18 without it.
and 9 more in the full report
12 fixed5 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
The file `.attune/dorodango-state.json` shows `converged_dimensions: ["correctness", "clarity"]`. Which quality pass should be executed next?
| Checked | Without skill | With skill |
|---|---|---|
| Selects consistency as the next pass to run | Missed | Passed |
The instructions your model reads when the skill loads.
Named after the Japanese art of polishing a ball of dirt into a high-gloss sphere. Applied to code: take the initial implementation (the "mud ball") and refine it through successive quality passes until it shines.
Four quality dimensions, each a self-contained pass:
See modules/pass-definitions.md for detailed scope
of each pass type.
issues_found: 0 marks that
dimension as convergedtest cases passed, vs 4 / 18 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.