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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Download
naodeng-awesome-qa-skills-skills_en_testing-types_model-based-testing-c44b892.zip · 5 KB
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
- Read
prompts/model-based-testing.mdand provide the objective, scope, material, environment, and evidence. - Start with separate known, missing, conflicting, stale, out_of_scope, and assumptions entries.
- Produce MBT-## findings with source, evidence state, applicability, impact/priority, owner, close condition, and validation.
- Separate facts, evidence-backed inferences, recommendations, and Human decisions.
- 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)
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agents
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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
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evals
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cases
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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"
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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
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prompts
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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?
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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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