fishbone-diagram

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Create comprehensive Fishbone (Ishikawa/Cause-and-Effect) diagrams for structured root cause brainstorming. Guides teams through problem definition, category selection (6Ms, 8Ps, 4Ss, or custom), cause identification, sub-cause drilling, prioritization via multi-voting, and 5 Whys integration. Generates visual SVG diagrams and professional HTML reports. Use when brainstorming potential causes, conducting root cause analysis, facilitating quality improvement sessions, analyzing defects or failures, structuring team problem-solving, or when user mentions "fishbone", "Ishikawa", "cause and effect diagram", "6Ms", "cause analysis", or "brainstorming causes".

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Sourceddunnock/claude-plugins
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npx skill4agent add ddunnock/claude-plugins fishbone-diagram

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fishbone-diagramroot-cause-analysisquality-improvementbrainstorming-toolrcca-integration

SKILL.md Content

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Fishbone Diagram (Ishikawa) Analysis

Create structured cause-and-effect diagrams to systematically identify potential root causes of problems. This skill guides collaborative brainstorming, ensures comprehensive category coverage, and produces visual outputs.

Integration with Other RCCA Tools

The Fishbone Diagram provides breadth (identifying all possible causes across categories), while 5 Whys provides depth (drilling into specific causes). Typical workflow:
  1. Use Fishbone to brainstorm and categorize all potential causes
  2. Prioritize top 2-3 causes via multi-voting
  3. Apply 5 Whys to each prioritized cause to find root causes
Also integrates with: Pareto Analysis (prioritize by frequency/impact), FMEA (risk assessment), 8D (Problem Definition phase).

Workflow Overview

6 Phases (Q&A-driven):
  1. Problem Definition → Clear, specific effect statement
  2. Category Selection → Choose framework (6Ms/8Ps/4Ss/custom)
  3. Cause Brainstorming → Identify causes under each category
  4. Sub-cause Drilling → Add 2-3 levels of detail
  5. Prioritization → Multi-voting to identify top causes
  6. Documentation → Generate diagram and report

Phase 1: Problem Definition

Goal: Establish a clear, specific, measurable problem statement.
Ask the user:
What specific problem or effect are you trying to analyze?
A good problem statement is:
  • Specific: "Machine 4 overheated at 2 PM" not "Machine broke"
  • Measurable: Include quantities, frequencies, or timeframes when possible
  • Observable: Describes what happened, not why
  • Non-blaming: Focus on the situation, not individuals
Quality Gate: Problem statement must:
  • Describe observable effect (not assumed cause)
  • Be specific enough to guide focused analysis
  • Avoid embedding solutions or blame
If vague, ask: "Can you be more specific about [what/when/where/how much]?"

Phase 2: Category Selection

Goal: Select appropriate cause categories for the analysis context.
Present options:
Which category framework fits your analysis context?
6Ms (Manufacturing/Operations):
  • Man (People), Machine, Method, Material, Measurement, Mother Nature (Environment)
8Ps (Service/Marketing):
  • Product, Price, Place, Promotion, People, Process, Physical Evidence, Policies
4Ss (Service Operations):
  • Surroundings, Suppliers, Systems, Skills
Custom: Define your own categories based on your specific domain
Or describe your context and I'll recommend an appropriate framework.
For detailed category definitions and prompting questions, see: 
references/category-frameworks.md

Phase 3: Cause Brainstorming

Goal: Generate comprehensive list of potential causes under each category.
For each category, ask:
Under [Category], what factors might contribute to "[Problem]"?
Think about:
  • What could go wrong in this area?
  • What variations or inconsistencies exist?
  • What has changed recently?
Facilitation techniques (see 
references/facilitation-guide.md
):
  • Round-robin: Each participant contributes one cause, rotate until exhausted
  • Brainwriting: Silent individual brainstorming on sticky notes before discussion
  • Affinity grouping: Cluster related causes together
  • "Why does this happen?": Probe each cause for deeper understanding
Quality indicators:
  • Minimum 2-3 causes per category (empty categories may indicate blind spots)
  • Mix of obvious and non-obvious causes
  • Causes should be distinct (not restating the problem)

Phase 4: Sub-cause Drilling

Goal: Add depth to major causes with 2-3 levels of sub-causes.
For significant causes, ask:
For the cause "[Cause]", what specific factors contribute to it?
Ask "Why might this happen?" to uncover sub-causes.
Depth guidance:
  • Level 1: Direct causes (e.g., "Equipment malfunction")
  • Level 2: Contributing factors (e.g., "Lack of maintenance")
  • Level 3: Root-level factors (e.g., "No maintenance schedule defined")
Typically 2-3 levels is sufficient. If more depth needed, transition to 5 Whys analysis.

Phase 5: Prioritization

Goal: Identify most likely/impactful causes for focused investigation.
Present prioritization options:
How would you like to prioritize the identified causes?
Multi-voting (Recommended): Each participant gets 3 votes to place on causes they believe are most significant
Impact-Effort Matrix: Rate each cause by impact (if addressed) and effort (to investigate/fix)
Data-driven: Use existing data to identify most frequent/costly causes (Pareto)
Consensus: Team discussion to agree on top 3-5 causes
After prioritization:
The top prioritized causes are:
  1. [Cause 1] - [votes/score]
  2. [Cause 2] - [votes/score]
  3. [Cause 3] - [votes/score]
Would you like to apply 5 Whys analysis to drill deeper into any of these?

Phase 6: Documentation

Goal: Generate visual diagram and comprehensive report.
Ask:
Ready to generate documentation. Options:
  1. SVG Diagram - Visual fishbone diagram
  2. HTML Report - Complete analysis with diagram, findings, and recommendations
  3. Both - Full documentation package
  4. JSON Export - Structured data for integration with other tools
Scripts:
  • scripts/generate_diagram.py
    - Creates SVG fishbone visualization
  • scripts/generate_report.py
    - Creates HTML report with embedded diagram
  • scripts/export_data.py
    - Exports analysis data as JSON

Common Pitfalls

See 
references/common-pitfalls.md
for detailed pitfall descriptions and redirection strategies.
Quick reference:
  1. Vague problem statement → Ask for specifics (what/when/where/how much)
  2. Stopping at symptoms → Probe with "Why might this happen?"
  3. Empty categories → Use category-specific prompting questions
  4. Person-blame → Redirect to "What process/system allowed this?"
  5. Groupthink → Use brainwriting before group discussion
  6. Confirmation bias → Challenge assumptions, seek contrary evidence
  7. Too shallow → Add sub-cause levels
  8. Too complex → Consider splitting into multiple diagrams

Quality Assessment

Rate the analysis on these dimensions (see 
references/quality-rubric.md
):
DimensionWeightDescription
Problem Clarity15%Specific, measurable, non-blaming
Category Coverage20%All relevant categories explored
Cause Depth25%2-3 levels of sub-causes
Cause Quality20%Distinct, actionable, evidence-based
Prioritization10%Clear method, justified rankings
Documentation10%Complete, visual, shareable
Scoring: Use 
scripts/score_analysis.py
to calculate quality score.

Examples

See 
references/examples.md
for worked examples:
  1. Manufacturing defect analysis (6Ms)
  2. Customer service complaint (8Ps)
  3. Healthcare incident (4Ss)
  4. Software deployment failure (Custom)