dorodango

Dorodango Polishing Workflow

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:

1. Correctness - run tests, fix failures
2. Clarity - code readability and structure
3. Consistency - naming, patterns, style alignment
4. 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 dimension converges on a record, never on the passing subagent's own count alone: correctness on the test command's exit code, clarity and consistency on the linter and formatter exit codes plus a reviewer count of 0, polish on a reviewer count of 0 from a fresh subagent that did not make the edits
• A pass that reports issues_found: 0 without the matching exit code recorded in the state file has not converged
• Convergence is reversible: a later pass that finds a regression in a converged dimension un-converges it, and that dimension is re-run before the run completes
• 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

1. Initialize state file (or load existing)
2. Determine next unconverged dimension
3. Dispatch subagent for that dimension
4. Record results in state file
5. If dimension converged (0 issues), mark it
6. If all dimensions converged or 10 passes reached, stop
7. Otherwise, proceed to next dimension

Cross-References

• pensive:code-refinement - used in clarity pass
• conserve:code-quality-principles - KISS/YAGNI/SOLID
• imbue:latent-space-engineering - frame pass prompts with emotional framing for better results

Exit Criteria

• .attune/dorodango-state.json exists with "converged": true and all four dimensions (correctness, clarity, consistency, polish) listed under converged_dimensions.
• Total pass_count in 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.
• Clarity and consistency converge only with the linter and formatter exit codes recorded as 0 in the state file beside the reviewer's count; a 0 issues claim with no exit code does not converge a dimension.
• A regression found in a later pass un-converges its dimension in the state file (converged_dimensions shrinks), and the dimension is re-run before converged: true.
• 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.