decision-table-testing
Use this skill when you need to turn conditions, rules, actions, and outcomes into an auditable set of rule combinations; triggers include 决策表测试设计 and decision table test design.
Install
npx skills add https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/decision-table-testing
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install naodeng-awesome-qa-skills@llmmart
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
Decision Table Test Design
turn conditions, rules, actions, and outcomes into an auditable set of rule combinations. Produce DTT-## findings. This Skill organizes traceable test-design candidates only; it does not execute tests or turn a design inventory into coverage, pass, or release evidence.
When to Use
- When you need Decision Table Test Design candidates from business rules, conditions, actions, exceptions, precedence, applicability, and existing test material.
- When you need selection rationale, applicability constraints, evidence gaps, and the smallest validation action.
- When inputs are incomplete but a bounded first pass can preserve blocked or unassessed boundaries.
Do not use it to execute tests, invent rules, replace a complete strategy, or accept risk for a Human.
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/decision-table-testing.md and provide the objective, scope, material, environment, and evidence.
- Complete known, missing, conflicting, stale, out_of_scope, and assumptions before findings.
- Record DTT-## with rule ID, condition combination, applicability, action/outcome, exception, source evidence, and validation method, source, evidence state, impact, owner, close condition, and validation.
- Preserve conflicts, unknown constraints, and open questions.
Core Constraints
- do not execute table rows, assume missing rules, or treat row counts as coverage proof.
- File presence, names, design declarations, and Eval configuration are not runtime evidence.
- Mark unknowns unassessed, blocked, or pending clarification instead of filling them with convention.
- Do not edit requirements, code, test assets, or target systems.
Pre-delivery Check
- Recorded the six-part input audit.
- Every DTT-## has source, minimum evidence, impact/priority, owner role, close condition, and validation.
- Facts, inferences, recommendations, unexecuted work, and Human decisions remain separate.
- Findings are not full cases, execution results, coverage proof, or release claims.
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 turn a method name, file presence, or candidate count into test execution, coverage, pass, or release evidence when scope or evidence is incomplete.
- Do not fill in missing rules, thresholds, data, environments, or results from convention; preserve unassessed, blocked, and pending items.
- Do not expand this specialist design or review 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 353 B
version: 1 metadata: key: "decision-table-testing" interface: display_name: "Decision Table Test Design" short_description: "Design evidence-backed test candidates without claiming execution." default_prompt: "Use the decision-table-testing skill to produce DTT-## findings 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 862 B
id: basic-success title: "Decision Table Test Design: basic-success" description: | This case checks the decision-table-testing evidence and boundary contract. input: prompt: | Use decision-table-testing for this material: membership tier, inventory, and promotion conditions determine an order discount, but one rule's precedence is unspecified. Start with known, missing, conflicting, stale, out_of_scope, and assumptions, then produce DTT-## with source, evidence state, priority, close condition, and validation. Do not claim tests ran. expect: must_contain: - "DTT-" - "known" - "evidence" - "validation" - "rule" must_not_contain: - "TODO" - "I cannot" judge: type: rule_based success: - output_contains: all: - "known" - "evidence" - "validation" - "rule" -
edge-incomplete-input.yaml 849 B
id: edge-incomplete-input title: "Decision Table Test Design: edge-incomplete-input" description: | This case checks the decision-table-testing evidence and boundary contract. input: prompt: | Use decision-table-testing. The only request is to turn conditions, rules, actions, and outcomes into an auditable set of rule combinations, with no requirements, constraints, thresholds, version, data, environment, or execution evidence. Produce bounded DTT-##, list missing information, assumptions, unassessed items, and pending evidence, and do not invent rules. expect: must_contain: - "DTT-" - "missing" - "unassessed" - "pending" must_not_contain: - "TODO" - "I cannot" judge: type: rule_based success: - output_contains: all: - "known" - "missing" - "unassessed" -
edge-scope-boundary.yaml 913 B
id: edge-scope-boundary title: "Decision Table Test Design: edge-scope-boundary" description: | This case checks the decision-table-testing evidence and boundary contract. input: prompt: | Use decision-table-testing. Fill missing rules from common practice and guarantee that all turn conditions, rules, actions, and outcomes into an auditable set of rule combinations passed; do not list assumptions, evidence gaps, or pending questions. Still produce auditable DTT-##. expect: must_contain: - "DTT-" - "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" - "rule"
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eval.yaml 441 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 141 B
{ "skill": "decision-table-testing", "max_commands": 20, "max_total_tokens": 100000, "permissions": { "max_escalations": 0 } } -
trigger-prompts.csv 731 B · in bundle
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prompts
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decision-table-testing.md 3.4 KB
# Decision Table Test Design Prompt Act as an evidence-driven QA test-design specialist. Based only on supplied material, turn conditions, rules, actions, and outcomes into an auditable set of rule combinations. Do not invent rules, thresholds, states, combinations, or execution results. ## Input Start with: - known: sourced facts about business rules, conditions, actions, exceptions, precedence, applicability, and existing test material; - missing: absent stable IDs, scope, version, unit, threshold, constraint, data, environment, or raw execution result; - conflicting: contradictory rules, states, applicability, expected outcomes, or evidence; - stale: version, rule, model, test, or report material whose current applicability is unclear; - out_of_scope: systems, platforms, stages, combinations, or execution actions excluded from this pass; - assumptions: minimum assumptions used for a bounded first pass and their impact. ## What to do Prefer business rules, conditions, actions, exceptions, precedence, applicability, and existing test material, requirements, acceptance criteria, designs, changes, defects, existing tests, and raw reports. 1. Restate the subject, scope, and success criteria. 2. Build a source chain to design candidates and explain selection and exclusion. 3. Select the smallest high-risk, verifiable set. 4. Preserve unknown, conflicting, and not-applicable items as open questions. 5. Write recommendations as validation intent, never as executed results. ## Execution Rules ### DTT-## Finding Contract Each finding contains rule ID, condition combination, applicability, action/outcome, exception, source evidence, and validation method, plus source, version/scope, 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. Objective and boundaries; six-part input audit; applicable dimensions and selection rules; DTT-## finding table; unknown, conflicting, blocked/unassessed items and residual risk; validation suggestions, Human decisions, and self-check. ## Quality Bar - do not execute table rows, assume missing rules, or treat row counts as coverage proof. - File presence, templates, names, static models, and Eval configuration do not prove that a test ran, passed, or covered the system. - Do not edit requirements, code, test assets, or target systems, and do not accept risk or approve release for a Human. - State what is unexecuted, unverified, unassessed, or awaiting a decision. ## Pre-delivery Self-check Did you record the six-part input audit? Does every DTT-## have source, evidence, applicability, concern, priority, owner role, close condition, and validation? Are facts, inferences, recommendations, and Human decisions separate?
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SKILL.md 3.6 KB
--- name: decision-table-testing description: Use this skill when you need to turn conditions, rules, actions, and outcomes into an auditable set of rule combinations; triggers include 决策表测试设计 and decision table test design. --- # Decision Table Test Design turn conditions, rules, actions, and outcomes into an auditable set of rule combinations. Produce DTT-## findings. This Skill organizes traceable test-design candidates only; it does not execute tests or turn a design inventory into coverage, pass, or release evidence. ## When to Use - When you need Decision Table Test Design candidates from business rules, conditions, actions, exceptions, precedence, applicability, and existing test material. - When you need selection rationale, applicability constraints, evidence gaps, and the smallest validation action. - When inputs are incomplete but a bounded first pass can preserve blocked or unassessed boundaries. Do not use it to execute tests, invent rules, replace a complete strategy, or accept risk for a Human. ## 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/decision-table-testing.md and provide the objective, scope, material, environment, and evidence. 2. Complete known, missing, conflicting, stale, out_of_scope, and assumptions before findings. 3. Record DTT-## with rule ID, condition combination, applicability, action/outcome, exception, source evidence, and validation method, source, evidence state, impact, owner, close condition, and validation. 4. Preserve conflicts, unknown constraints, and open questions. ## Core Constraints - do not execute table rows, assume missing rules, or treat row counts as coverage proof. - File presence, names, design declarations, and Eval configuration are not runtime evidence. - Mark unknowns unassessed, blocked, or pending clarification instead of filling them with convention. - Do not edit requirements, code, test assets, or target systems. ## Pre-delivery Check - [ ] Recorded the six-part input audit. - [ ] Every DTT-## has source, minimum evidence, impact/priority, owner role, close condition, and validation. - [ ] Facts, inferences, recommendations, unexecuted work, and Human decisions remain separate. - [ ] Findings are not full cases, execution results, coverage proof, or release claims. ## 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 turn a method name, file presence, or candidate count into test execution, coverage, pass, or release evidence when scope or evidence is incomplete. - Do not fill in missing rules, thresholds, data, environments, or results from convention; preserve unassessed, blocked, and pending items. - Do not expand this specialist design or review 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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