Claude Skill

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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Part of athola/claude-night-market — 46 skills

Install

skills CLI npx skills add https://github.com/athola/claude-night-market/tree/master/plugins/attune/skills/dorodango
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install athola-claude-night-market@llmmart
Git git clone https://github.com/athola/claude-night-market.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole athola/claude-night-market collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

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.
Files (claude-night-market)
  • modules
    • pass-definitions.md 2.3 KB
      ---
      name: pass-definitions
      description: Definitions and scope for each quality
        pass type in the dorodango polishing workflow.
      parent_skill: attune:dorodango
      category: workflow
      estimated_tokens: 250
      ---
      
      # Pass Type Definitions
      
      ## Pass 1: Correctness
      
      **Goal**: Code works as intended.
      
      **Scope**:
      - Run test suite, identify failures
      - Fix failing tests
      - Check for runtime errors
      - Verify edge cases specified in requirements
      
      **Agent prompt focus**: "Run all tests. For each
      failure, identify the root cause and fix it. Do not
      refactor; focus only on making tests pass."
      
      **Tools**: Bash (pytest/test runner), Edit
      
      **Convergence**: 0 test failures
      
      ## Pass 2: Clarity
      
      **Goal**: Code is readable and well-structured.
      
      **Scope**:
      - Variable and function naming
      - Function length and complexity
      - Comment quality (helpful vs. noise)
      - Dead code removal
      - Single-responsibility adherence
      
      **Agent prompt focus**: "Review for readability.
      Rename unclear variables, break up long functions,
      remove dead code. Do not change behavior."
      
      **Tools**: Read, Edit, pensive:code-refinement
      
      **Convergence**: 0 clarity issues found by reviewer
      
      ## Pass 3: Consistency
      
      **Goal**: Code follows project conventions.
      
      **Scope**:
      - Naming conventions (snake_case, camelCase, etc.)
      - Import ordering
      - Error handling patterns
      - Logging patterns
      - File organization
      
      **Agent prompt focus**: "Compare against codebase
      conventions. Flag deviations from established patterns.
      Use style-gene-transfer if exemplar available."
      
      **Tools**: Read, Grep, Edit
      
      **Convergence**: 0 convention deviations found
      
      ## Pass 4: Polish
      
      **Goal**: Code is production-ready.
      
      **Scope**:
      - Error messages are user-friendly
      - Documentation is accurate and complete
      - Edge cases have graceful handling
      - Configuration is well-documented
      
      **Agent prompt focus**: "Review error messages, docs,
      and edge cases. Ensure each error message helps the
      user understand what went wrong and how to fix it."
      
      **Tools**: Read, Edit
      
      **Convergence**: 0 polish issues found
      
      ## Pass Ordering
      
      Passes run in order: correctness, clarity, consistency,
      polish. This ordering is intentional:
      
      1. Fix bugs before cleaning up code
      2. Clean up code before checking conventions
      3. Check conventions before polishing edges
      
      If a pass in a later dimension discovers a bug
      (correctness regression), surface to human rather
      than re-running the converged correctness pass.
      
  • SKILL.md 4.2 KB
    ---
    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`:
    
    ```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.
    

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