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/dorodango

Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.

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claude-night-market
337200 skills59 agents162 commands1 MCP
Install
$ npx -y skills add athola/claude-night-market --skill dorodango --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/dorodango

Context preview

The summary Claude sees to decide when to auto-load this skill.

Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.

SKILL.md

dorodango.SKILL.md
name: dorodango
description: Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.
alwaysApply: false
category: workflow
tags:
  - polishing
  - iterative-refinement
  - code-quality
  - convergence
progressive_loading: true
dependencies:
  hub: []
  modules:
    - modules/pass-definitions.md
complexity: intermediate
model_hint: standard
estimated_tokens: 400

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 pass that finds `issues_found: 0` marks 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

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.

  • [ ] 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.

Read more
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