Dorodango Polishing Workflow
SkillAI & modelsPolishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Dorodango Polishing Workflow skill
What this skill tells your AI
The instructions your AI receives, as published by athola/claude-night-market in plugins/attune/skills/dorodango/SKILL.md and read by ahel’s review.
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.
When To Use
- After initial implementation is complete and tests pass
- Code works but needs refinement across multiple quality dimensions
- Preparing code for review or release
- Resuming a previous polishing session
When NOT To Use
- Code does not compile or pass basic tests (fix first)
- Single-dimension improvement needed (use the specific skill directly: pensive:code-refinement, etc.)
- Greenfield design phase (use brainstorming instead)
Pass Sequence
Four quality dimensions, each a self-contained pass:
- Correctness - run tests, fix failures
- Clarity - code readability and structure
- Consistency - naming, patterns, style alignment
- Polish - documentation, error messages, edges
See modules/pass-definitions.md for detailed scope
of each pass type.
Convergence Model
- Each pass targets one dimension
- A pass that finds
issues_found: 0marks that dimension as converged - Convergence is irreversible per run; a converged dimension is not re-run
- When all 4 dimensions converge, polishing is complete
- Maximum 10 total passes (hard limit)
- If not converged after 10 passes, surface state to human with recommendation to split into smaller units
State Persistence
State tracked in .attune/dorodango-state.json:
{
"target": "plugins/foo",
"started_at": "2026-03-18T12:00:00Z",
"pass_count": 3,
"passes": [
{
"type": "correctness",
"issues_found": 2,
"issues_fixed": 2
},
{
"type": "clarity",
"issues_found": 5,
"issues_fixed": 5
},
{
"type": "consistency",
"issues_found": 0
}
],
"converged_dimensions": ["consistency"],
"converged": false
}
This file enables resume across sessions. On resume, skip converged dimensions and continue from the next unconverged dimension.
Subagent Isolation
Each pass dispatches a self-contained subagent to prevent context accumulation. The subagent receives:
- Target directory/files
- Pass type and scope (from pass-definitions module)
- Previous pass results (summary only, not full context)
Subagent dispatch is optional for targets under 100 lines of code; in-session review is sufficient for small files.
Workflow
- Initialize state file (or load existing)
- Determine next unconverged dimension
- Dispatch subagent for that dimension
- Record results in state file
- If dimension converged (0 issues), mark it
- If all dimensions converged or 10 passes reached, stop
- Otherwise, proceed to next dimension
Cross-References
pensive:code-refinement- used in clarity passconserve:code-quality-principles- KISS/YAGNI/SOLIDimbue:latent-space-engineering- frame pass prompts with emotional framing for better results
Exit Criteria
-
.attune/dorodango-state.jsonexists with"converged": trueand all four dimensions (correctness,clarity,consistency,polish) listed underconverged_dimensions. - Total
pass_countin the state file is <= 10; if 10 passes complete without full convergence, the skill surfaces the unconverged dimensions to the user with a recommendation to split the target into smaller units. - The correctness dimension converges only after all tests pass (exit code 0); a correctness pass that finds failing tests never marks the dimension as converged.
- Each pass is dispatched as a separate subagent for targets over 100 lines, confirmed by the state file recording individual pass results rather than a single bulk entry.
Signals
- GitHub stars
- 337
- Forks
- 34
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
dorodango- Source
- github.com/athola/claude-night-market