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Dorodango

  • 74 installs
  • 325 repo stars
  • Updated August 2, 2026
  • athola/claude-night-market

Dorodango is an agent skill that defines correctness, clarity, and consistency passes for the dorodango code-polishing workflow.

About

Dorodango (pass-definitions) documents each quality pass in the attune dorodango polishing workflow for solo builders who want agents to improve code in layers instead of one messy refactor. Pass 1 targets correctness with the test suite and edge cases; Pass 2 targets readability without behavior changes; Pass 3 targets project conventions and patterns, optionally using style-gene-transfer when exemplars exist. Each pass states goals, scope, agent prompt focus, tools, and a clear convergence metric. Install it when you orchestrate multi-agent or multi-step polish after features land—especially if you already use the parent dorodango skill and need precise pass boundaries so agents do not scope-creep across correctness and style in one shot.

  • Pass 1 Correctness: pytest/runner, fix failures only—convergence 0 test failures
  • Pass 2 Clarity: naming, function length, dead code—convergence 0 clarity issues
  • Pass 3 Consistency: naming conventions, imports, error/logging patterns vs codebase
  • Per-pass agent prompt focus and tool hints (Bash, Edit, Read, style-gene-transfer)
  • Child definition skill for attune:dorodango parent workflow

Dorodango by the numbers

  • 74 all-time installs (skills.sh)
  • Ranked #509 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/athola/claude-night-market --skill dorodango

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Listed on Skillselion
Installs74
repo stars325
Security audit3 / 3 scanners passed
Last updatedAugust 2, 2026
Repositoryathola/claude-night-market

What it does

Run the dorodango multi-pass polish loop—correctness, clarity, consistency—with defined agent focus and convergence criteria per pass.

Who is it for?

Best when you use pytest-backed repos and want a repeatable polish ritual after implementation, with parent skill attune:dorodango.

Skip if: Greenfield scaffolding with no tests yet, or projects that only need a one-off lint fix without a multi-pass workflow.

When should I use this skill?

Executing or scoping a pass within the parent attune:dorodango polishing workflow.

What you get

You run ordered passes with explicit convergence (0 failures, 0 clarity issues, convention alignment) and scoped agent instructions per pass.

  • Pass-scoped polish with stated convergence metric
  • Convention-aligned codebase segment after Pass 3

By the numbers

  • 3 defined pass types: Correctness, Clarity, Consistency

Files

SKILL.mdMarkdownGitHub ↗

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

Related skills

How it compares

Defines pass boundaries for a polishing stack, not a single-shot “fix all code review comments” prompt.

FAQ

Who is dorodango for?

Developers running the attune dorodango workflow who need crisp pass definitions so agents fix tests first, then clarity, then consistency.

When should I use dorodango?

After build backend work when tests exist, during ship review before merge, and during ship testing when you want correctness pass before style passes.

Is dorodango safe to install?

Passes assume Bash test runs and file edits; review the Security Audits panel on this Prism page and sandbox test commands appropriately.

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