Claude Skill

jidoka

Debug software by making failures visible, reproducing them, finding contributing causes, and adding durable regression protection. Use for bugs, failing tests, incidents, regressions, flaky behavior, and unexplained production errors.

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Download kuya-egg-monozukuri-jidoka-b4243fe.zip · 2 KB
Part of kuya-egg/monozukuri — 13 skills

Install

skills CLI npx skills add https://github.com/kuya-egg/Monozukuri/tree/main/jidoka
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install kuya-egg-monozukuri@llmmart
Git git clone https://github.com/kuya-egg/Monozukuri.git

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

Skill manifest

Jidoka

Jidoka is the stop-the-line discipline: when a meaningful defect appears, make it visible and understand it before allowing more work or releases to build on it. It is a Japanese-inspired engineering metaphor for quality at the source, not a promise that every minor imperfection stops all work forever.

Use this skill when

  • a test, build, type check, migration, or invariant fails;
  • behavior is incorrect, flaky, slow, or unexplained;
  • a regression or production incident needs diagnosis;
  • an existing workaround hides a recurring problem; or
  • a fix is tempting but the cause is not yet understood.

Outcome

Produce a verified root-cause fix with:

  • a tight reproduction or a clear explanation of why reproduction is blocked;
  • observations separated from hypotheses;
  • experiments that distinguish plausible causes;
  • the contributing cause and why the system allowed it;
  • the narrowest safe fix;
  • regression protection or durable diagnostic evidence; and
  • explicit results and residual risk.

Workflow

1. Stop and frame

State the failure, impact, affected boundary, and current known-good or known-bad evidence. Do not start with a broad refactor or a preferred theory.

2. Reproduce

Create one reliable command, test, fixture, request, or observation that fails for the actual problem. If the failure is intermittent, capture the smallest useful signal and improve the feedback loop before guessing.

3. Observe

Read the error, inputs, state transitions, logs, traces, timing, and related code. Instrument only what distinguishes hypotheses. Treat symptoms as clues, not causes.

4. Test hypotheses

List plausible causes and run the cheapest experiment that can eliminate one. Change one relevant variable at a time. Stop using a hypothesis when evidence contradicts it.

5. Find the contributing cause

Ask why the failure occurred and why existing tests, types, constraints, observability, or review did not catch it. Use Five Whys when it exposes a missing guardrail; do not use it to invent blame or a single simplistic cause.

6. Fix and protect

Make the smallest fix at the right boundary. Add a regression test, invariant, diagnostic, or process guardrail that would fail if the problem returns.

7. Verify the stop is over

Rerun the reproduction, targeted checks, and broader checks appropriate to the risk. Confirm that the fix does not mask an adjacent failure.

Autonomous correction boundary

During the edit phase, fix compile errors, test failures, and minor defects introduced by your own change when the intended behavior is clear. Stop for human input only when requirements or architecture are ambiguous, a new authorization is required, a destructive action is involved, or the evidence reveals a conflict that cannot be resolved safely.

Evidence standard

Do not claim root cause from a plausible code reading alone. Show the failing case before the fix, the result after the fix, and the checks that cover the changed boundary. Every verification claim must be traceable to an executed command, observed result, or authoritative evidence.

Boundaries

  • Do not suppress a failing check to restore green status.
  • Do not patch only the symptom when the cause is observable.
  • Do not perform unrelated cleanup while the feedback loop is broken.
  • Do not declare “cannot reproduce” without recording what was tried and what evidence is missing.
  • Do not leave a known high-impact defect invisible because it was inherited.

Handoff

Report the failure, cause, fix, regression protection, verification, and any remaining uncertainty. Use poka-yoke for stronger prevention, andon for diagnostics, or hansei when the failure reveals a process or system gap.

If a referenced skill is not installed, apply its named lens inline instead of trying to invoke it.

Files (monozukuri)
  • agents
    • openai.yaml 301 B
      interface:
        display_name: "Jidoka — Stop the Line"
        short_description: "Debug defects to root cause"
        brand_color: "#1F2937"
        default_prompt: "Use $jidoka to reproduce this failure, find its contributing cause, and add durable regression protection."
      
      policy:
        allow_implicit_invocation: false
      
  • SKILL.md 4 KB
    ---
    name: jidoka
    description: "Debug software by making failures visible, reproducing them, finding contributing causes, and adding durable regression protection. Use for bugs, failing tests, incidents, regressions, flaky behavior, and unexplained production errors."
    ---
    
    # Jidoka
    
    Jidoka is the stop-the-line discipline: when a meaningful defect appears, make
    it visible and understand it before allowing more work or releases to build on
    it. It is a Japanese-inspired engineering metaphor for quality at the source,
    not a promise that every minor imperfection stops all work forever.
    
    ## Use this skill when
    
    - a test, build, type check, migration, or invariant fails;
    - behavior is incorrect, flaky, slow, or unexplained;
    - a regression or production incident needs diagnosis;
    - an existing workaround hides a recurring problem; or
    - a fix is tempting but the cause is not yet understood.
    
    ## Outcome
    
    Produce a verified root-cause fix with:
    
    - a tight reproduction or a clear explanation of why reproduction is blocked;
    - observations separated from hypotheses;
    - experiments that distinguish plausible causes;
    - the contributing cause and why the system allowed it;
    - the narrowest safe fix;
    - regression protection or durable diagnostic evidence; and
    - explicit results and residual risk.
    
    ## Workflow
    
    ### 1. Stop and frame
    
    State the failure, impact, affected boundary, and current known-good or
    known-bad evidence. Do not start with a broad refactor or a preferred theory.
    
    ### 2. Reproduce
    
    Create one reliable command, test, fixture, request, or observation that fails
    for the actual problem. If the failure is intermittent, capture the smallest
    useful signal and improve the feedback loop before guessing.
    
    ### 3. Observe
    
    Read the error, inputs, state transitions, logs, traces, timing, and related
    code. Instrument only what distinguishes hypotheses. Treat symptoms as clues,
    not causes.
    
    ### 4. Test hypotheses
    
    List plausible causes and run the cheapest experiment that can eliminate one.
    Change one relevant variable at a time. Stop using a hypothesis when evidence
    contradicts it.
    
    ### 5. Find the contributing cause
    
    Ask why the failure occurred and why existing tests, types, constraints,
    observability, or review did not catch it. Use Five Whys when it exposes a
    missing guardrail; do not use it to invent blame or a single simplistic cause.
    
    ### 6. Fix and protect
    
    Make the smallest fix at the right boundary. Add a regression test, invariant,
    diagnostic, or process guardrail that would fail if the problem returns.
    
    ### 7. Verify the stop is over
    
    Rerun the reproduction, targeted checks, and broader checks appropriate to the
    risk. Confirm that the fix does not mask an adjacent failure.
    
    ## Autonomous correction boundary
    
    During the edit phase, fix compile errors, test failures, and minor defects
    introduced by your own change when the intended behavior is clear. Stop for
    human input only when requirements or architecture are ambiguous, a new
    authorization is required, a destructive action is involved, or the evidence
    reveals a conflict that cannot be resolved safely.
    
    ## Evidence standard
    
    Do not claim root cause from a plausible code reading alone. Show the failing
    case before the fix, the result after the fix, and the checks that cover the
    changed boundary. Every verification claim must be traceable to an executed
    command, observed result, or authoritative evidence.
    
    ## Boundaries
    
    - Do not suppress a failing check to restore green status.
    - Do not patch only the symptom when the cause is observable.
    - Do not perform unrelated cleanup while the feedback loop is broken.
    - Do not declare “cannot reproduce” without recording what was tried and what
      evidence is missing.
    - Do not leave a known high-impact defect invisible because it was inherited.
    
    ## Handoff
    
    Report the failure, cause, fix, regression protection, verification, and any
    remaining uncertainty. Use `poka-yoke` for stronger prevention, `andon` for
    diagnostics, or `hansei` when the failure reveals a process or system gap.
    
    If a referenced skill is not installed, apply its named lens inline instead of
    trying to invoke it.
    

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