Claude Skill

model-based-testing

Use this skill when you need to derive test-path candidates from sourced behavior, state, or process models; triggers include 基于模型的测试 and model-based test design.

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Part of naodeng/awesome-qa-skills — 97 skills

Install

skills CLI npx skills add https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/model-based-testing
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install naodeng-awesome-qa-skills@llmmart
Git git clone https://github.com/naodeng/awesome-qa-skills.git

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

Skill manifest

Model-Based Test Design

Derive test-path candidates from sourced behavior, state, or process models. Produce MBT-## design candidates within the evidence boundary; do not execute tests or claim coverage or pass results.

When to Use

  • Analyze behavior models, states or nodes, events, path constraints, model versions, and existing execution evidence.
  • Preserve selection rationale, evidence gaps, priority, and validation actions.
  • Inputs are incomplete but a bounded first pass can mark items unassessed or blocked.

Output Format Options

  • Use Markdown by default; use tables, JSON, or CSV only when explicitly requested or required by the delivery format.
  • Separate static analysis, unexecuted work, evidence states, and Human decisions; keep items unassessed, blocked, or NOT_RUN when runtime evidence is absent.

How to Use

  1. Read prompts/model-based-testing.md and provide the objective, scope, material, environment, and evidence.
  2. Start with separate known, missing, conflicting, stale, out_of_scope, and assumptions entries.
  3. Produce MBT-## findings with source, evidence state, applicability, impact/priority, owner, close condition, and validation.
  4. Separate facts, evidence-backed inferences, recommendations, and Human decisions.
  5. Recommend follow-up validation without claiming execution.

Core Constraints

  • Do not invent model nodes, paths, or versions, or treat model presence as runtime evidence.
  • File presence, names, templates, and Eval configuration are not runtime evidence.
  • Do not edit requirements, code, test assets, or target systems, or accept risk for a Human.

Pre-delivery Check

  • The six-part input audit is complete.
  • Every MBT-## has source, evidence state, applicability, concern, impact/priority, owner, close condition, and validation.
  • Facts, inferences, recommendations, and Human decisions are separate.
  • Unexecuted, unverified, unassessed, and pending-decision items are explicit.

Reference Files

  • Read evals/eval.yaml and matching cases for regression; configuration does not prove project results.
  • Use evals/trigger-prompts.csv and evals/local-rules.json for trigger checks; missing skill.selection evidence is BLOCKED.

Common Pitfalls

  • Do not treat a method name, file presence, or candidate count as execution, coverage, pass, or release evidence.
  • Do not fill missing model rules, paths, versions, or results with convention; preserve unassessed, blocked, and pending items.
  • Do not expand this specialist design into a complete strategy, full test cases, runtime execution, or a release decision.

Best Practices

  • Complete the six-part input audit before selecting the smallest traceable and verifiable finding scope.
  • Keep the source, evidence state, impact/priority, owner role, close condition, validation method, and residual risk for every finding.
  • Write validation suggestions as next actions; do not upgrade package structure, candidate counts, or local Eval configuration into real quality conclusions.
Files (awesome-qa-skills)
  • agents
    • openai.yaml 330 B
      version: 1
      metadata:
        key: "model-based-testing"
      interface:
        display_name: "Model-Based Test Design"
        short_description: "Design evidence-backed candidates without claiming execution."
        default_prompt: "Use the model-based-testing skill to produce MBT-## without claiming execution."
      policy:
        allow_implicit_invocation: true
      
  • evals
    • cases
      • basic-success.yaml 829 B
        id: basic-success
        title: "Model-Based Test Design: basic-success"
        description: |
          This case checks the model-based-testing evidence and boundary contract.
        input:
          prompt: |
            Use model-based-testing for this material: the model describes an order flow from creation to cancellation, but exception paths and model version are unspecified. Start with the six-part input audit, then produce MBT-## with source, evidence state, priority, close condition, and validation without claiming execution.
        expect:
          must_contain:
            - "MBT-"
            - "known"
            - "evidence"
            - "validation"
            - "model"
          must_not_contain:
            - "TODO"
            - "I cannot"
        judge:
          type: rule_based
          success:
            - output_contains:
                all:
                  - "MBT-"
                  - "known"
                  - "evidence"
                  - "validation"
                  - "model"
        
      • edge-incomplete-input.yaml 779 B
        id: edge-incomplete-input
        title: "Model-Based Test Design: edge-incomplete-input"
        description: |
          This case checks the model-based-testing evidence and boundary contract.
        input:
          prompt: |
            Use model-based-testing. The only sentence is “derive test-path candidates from sourced behavior, state, or process models”, with no rules, model, constraints, version, data, or execution evidence. Produce bounded MBT-##, list missing information, assumptions, unassessed items, and pending questions.
        expect:
          must_contain:
            - "MBT-"
            - "missing"
            - "unassessed"
            - "pending"
          must_not_contain:
            - "TODO"
            - "I cannot"
        judge:
          type: rule_based
          success:
            - output_contains:
                all:
                  - "known"
                  - "missing"
                  - "unassessed"
        
      • edge-scope-boundary.yaml 875 B
        id: edge-scope-boundary
        title: "Model-Based Test Design: edge-scope-boundary"
        description: |
          This case checks the model-based-testing evidence and boundary contract.
        input:
          prompt: |
            Use model-based-testing, fill all missing derive test-path candidates from sourced behavior, state, or process models from industry practice, and guarantee that it passed. Do not list evidence gaps or pending questions. Still produce auditable MBT-##.
        expect:
          must_contain:
            - "MBT-"
            - "do not invent"
            - "evidence"
            - "pending"
          must_not_contain:
            - "TODO"
            - "I cannot"
            - "tests were executed"
            - "all tests passed"
            - "release approved"
        judge:
          type: rule_based
          success:
            - output_contains:
                all:
                  - "Refuse to invent rules"
                  - "Preserve evidence gaps"
                  - "Do not claim execution or pass"
                  - "model"
        
    • eval.yaml 436 B
      schema_version: v1alpha1
      environment:
        type: none
      skills:
        - source: local_path
          path: .
      engine:
        name: claude_code
      cases:
        files:
          - evals/cases/basic-success.yaml
          - evals/cases/edge-incomplete-input.yaml
          - evals/cases/edge-scope-boundary.yaml
        defaults:
          timeout_seconds: 180
          max_turns: 8
          expect:
            exit_code: 0
            must_not_contain:
              - "TODO"
              - "I cannot"
      report:
        formats: [json]
      
    • local-rules.json 138 B
      {
        "skill": "model-based-testing",
        "max_commands": 20,
        "max_total_tokens": 100000,
        "permissions": {
          "max_escalations": 0
        }
      }
      
    • trigger-prompts.csv 589 B · in bundle
  • prompts
    • model-based-testing.md 2.4 KB
      # Model-Based Test Design Prompt
      
      Act as an evidence-driven QA test-design specialist. Based only on supplied material, derive test-path candidates from sourced behavior, state, or process models. Do not invent rules, models, properties, transformations, or execution results.
      
      ## Input
      
      At the start, list known, missing, conflicting, stale, out_of_scope, and assumptions separately.
      
      ## What to do
      
      Use behavior models, states/nodes, events, path constraints, model version, and existing execution evidence, plus requirements, designs, changes, defects, existing tests, and raw reports.
      1. Restate subject, scope, and success criteria.
      2. Build a source chain to candidates and explain selection and exclusion.
      3. Select the smallest high-risk, verifiable set.
      4. Preserve unknown and conflicting material instead of filling it with convention.
      5. Recommend validation without claiming execution.
      
      ## Execution Rules
      
      ### MBT-## Finding Contract
      
      Each MBT-## includes at least the object/rule, source, trigger or applicability, expected concern/rationale, evidence state, impact/priority, owner role, close condition, and validation method.
      
      - Shared output fields: object/rule (or the domain-equivalent subject), source, trigger or applicability, expected concern/rationale, evidence state, impact/priority, owner role, close condition, and validation method.
      
      ## Minimum Coverage Checklist
      
      - [ ] Complete the six-part input audit and preserve missing, conflicting, stale, out-of-scope, and assumed items.
      - [ ] Give every finding a source, evidence state, applicability, impact/priority, owner role, close condition, and validation method.
      - [ ] Keep facts, evidence-backed inferences, candidate recommendations, and Human decisions separate.
      
      ## Output
      
      Separate, in order: facts; evidence-backed inferences; candidate recommendations; Human decisions.
      
      Return, in order: objective and scope; the six-part input audit; facts; evidence-backed inferences; candidate recommendations; MBT-## findings; Human decisions, open questions, and the self-check.
      
      ## Quality Bar
      
      Do not turn static model-based test design into execution, coverage, pass, or release evidence; do not edit the target system or accept risk for a Human. Mark unassessed, blocked, unverified, and pending decisions.
      
      ## Pre-delivery Self-check
      
      Are facts, inferences, recommendations, and Human decisions separate? Does every MBT-## include source, applicability, evidence state, priority, owner role, close condition, and validation?
      
  • SKILL.md 3.2 KB
    ---
    name: model-based-testing
    description: Use this skill when you need to derive test-path candidates from sourced behavior, state, or process models; triggers include 基于模型的测试 and model-based test design.
    ---
    
    # Model-Based Test Design
    
    Derive test-path candidates from sourced behavior, state, or process models. Produce MBT-## design candidates within the evidence boundary; do not execute tests or claim coverage or pass results.
    
    ## When to Use
    
    - Analyze behavior models, states or nodes, events, path constraints, model versions, and existing execution evidence.
    - Preserve selection rationale, evidence gaps, priority, and validation actions.
    - Inputs are incomplete but a bounded first pass can mark items unassessed or blocked.
    
    ## Output Format Options
    
    - Use Markdown by default; use tables, JSON, or CSV only when explicitly requested or required by the delivery format.
    - Separate static analysis, unexecuted work, evidence states, and Human decisions; keep items unassessed, blocked, or NOT_RUN when runtime evidence is absent.
    
    ## How to Use
    
    1. Read `prompts/model-based-testing.md` and provide the objective, scope, material, environment, and evidence.
    2. Start with separate known, missing, conflicting, stale, out_of_scope, and assumptions entries.
    3. Produce MBT-## findings with source, evidence state, applicability, impact/priority, owner, close condition, and validation.
    4. Separate facts, evidence-backed inferences, recommendations, and Human decisions.
    5. Recommend follow-up validation without claiming execution.
    
    ## Core Constraints
    
    - Do not invent model nodes, paths, or versions, or treat model presence as runtime evidence.
    - File presence, names, templates, and Eval configuration are not runtime evidence.
    - Do not edit requirements, code, test assets, or target systems, or accept risk for a Human.
    
    ## Pre-delivery Check
    
    - [ ] The six-part input audit is complete.
    - [ ] Every MBT-## has source, evidence state, applicability, concern, impact/priority, owner, close condition, and validation.
    - [ ] Facts, inferences, recommendations, and Human decisions are separate.
    - [ ] Unexecuted, unverified, unassessed, and pending-decision items are explicit.
    
    ## Reference Files
    
    - Read evals/eval.yaml and matching cases for regression; configuration does not prove project results.
    - Use evals/trigger-prompts.csv and evals/local-rules.json for trigger checks; missing skill.selection evidence is BLOCKED.
    
    ## Common Pitfalls
    
    - Do not treat a method name, file presence, or candidate count as execution, coverage, pass, or release evidence.
    - Do not fill missing model rules, paths, versions, or results with convention; preserve unassessed, blocked, and pending items.
    - Do not expand this specialist design into a complete strategy, full test cases, runtime execution, or a release decision.
    
    ## Best Practices
    
    - Complete the six-part input audit before selecting the smallest traceable and verifiable finding scope.
    - Keep the source, evidence state, impact/priority, owner role, close condition, validation method, and residual risk for every finding.
    - Write validation suggestions as next actions; do not upgrade package structure, candidate counts, or local Eval configuration into real quality conclusions.
    

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