Test Effectiveness Analysis
Use this skill when you need evidence-bounded analysis, design, or validation preparation for the relationship between test signals and defects or risks, missed-detection limits, false signals, and validation plans.
StatusStable
TypeAtomic Skill
DomainSoftware testing
SDLCTest design
Good forQA
LanguageChinese / English
EvalsEvals ✓
Synced2026-09-15
Why this Skill
It turns this Skill's method into a quality input that can be executed, reviewed, and reused.
- Keep the analysis focused on the relationship between test signals and defects or risks, missed-detection limits, false signals, and validation plans; do not replace business owners or Human risk acceptance, exception approval, or release decisions.
- Never invent system behavior, fields, metrics, thresholds, data, root causes, execution records, or pass claims.
- Static design, plans, file presence, or a dry run retain their evidence state and cannot become proof of real execution.
- Listing checks without triggers, expected concerns, owner roles, close conditions, and evidence.
When to use
Use this Skill
- Use this skill when you need evidence-bounded analysis, design, or validation preparation for the relationship between test signals and defects or risks, missed-detection limits, false signals, and validation plans.
- Use it to review a plan, result, or metric and turn the review into executable improvements.
- Use it when input is incomplete but a bounded first pass with assumptions, gaps, and human-decision boundaries is still useful.
Common pitfalls
- Listing checks without triggers, expected concerns, owner roles, close conditions, and evidence.
- Treating adjacent metrics or tool names as a complete test effectiveness judgment.
- Using unexplained numbers for false precision or writing correlation as causation.
- Refusing incomplete input, or pretending that incomplete evidence is conclusive.
Input
Minimum Input
- The current task scope, objective, and subject under review.
Recommended Input
- Project goal
- Test scope
- Constraints
Optional Context
- Relevant code or configuration
- Historical results
- Logs and metrics
Output
The output follows this Skill's method and makes facts, assumptions, risks, and next steps explicit.
These are the Skill's expected output requirements, not evidence that it ran in a real project.
- 01Read and follow prompts/test-effectiveness-analysis.md, including its input audit, domain coverage, and output order.
- 02Extract scope, environment, version, time window, constraints, success criteria, and available evidence, with attention to test signal, risk coverage, missed-detection limit, false signal, validation plan.
- 03Separate confirmed facts, evidence-backed inferences, candidate recommendations, and Human decisions before ranking by risk and evidence strength.
- 04Turn high-risk items into preconditions, steps, expected behavior or decision criteria, required evidence, and a validation method.
View full output structure
- 05When input is incomplete, deliver a bounded first pass, state unsupported conclusions, and never present static material as execution evidence.
How It Works
- 01Read and follow prompts/test-effectiveness-analysis.md, including its input audit, domain coverage, and output order.
- 02Extract scope, environment, version, time window, constraints, success criteria, and available evidence, with attention to test signal, risk coverage, missed-detection limit, false signal, validation plan.
- 03Separate confirmed facts, evidence-backed inferences, candidate recommendations, and Human decisions before ranking by risk and evidence strength.
- 04Turn high-risk items into preconditions, steps, expected behavior or decision criteria, required evidence, and a validation method.
- 05When input is incomplete, deliver a bounded first pass, state unsupported conclusions, and never present static material as execution evidence.
Install & Quick Start
Install command / SHELL
npx skills add \
https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/test-effectiveness-analysis
-gtest-effectiveness-analysis.prompt
@skill test-effectiveness-analysis
Using the current project context, produce an actionable result following this Skill.
Additional context:
[Paste project context or requirement]---
name: test-effectiveness-analysis
description: Use this skill when you need evidence-bounded the relationship between test signals and defects or risks, missed-detection limits, false signals, and validation plans; triggers include 测试有效性 and test effectiveness.
---
# Test Effectiveness Analysis
## When to Use
- Use this skill when you need evidence-bounded analysis, design, or validation preparation for the relationship between test signals and defects or risks, missed-detection limits, false signals, and validation plans.
- Use it to review a plan, result, or metric and turn the review into executable improvements.
- Use it when input is incomplete but a bounded first pass with assumptions, gaps, and human-decision boundaries is still useful.
## Output Format Options
- Default to Markdown organized by domain risk, evidence state, priority, and boundary.
- When the user requests tables, CSV, JSON, or ticket fields, preserve the same finding fields, evidence, and decision boundaries.
- Before machine consumption, confirm the schema, enums, required fields, and evidence sources.
## How to Use
1. Read and follow `prompts/test-effectiveness-analysis.md`, including its input audit, domain coverage, and output order.
2. Extract scope, environment, version, time window, constraints, success criteria, and available evidence, with attention to test signal, risk coverage, missed-detection limit, false signal, validation plan.
3. Separate confirmed facts, evidence-backed inferences, candidate recommendations, and Human decisions before ranking by risk and evidence strength.
4. Turn high-risk items into preconditions, steps, expected behavior or decision criteria, required evidence, and a validation method.
5. When input is incomplete, deliver a bounded first pass, state unsupported conclusions, and never present static material as execution evidence.
## Reference Files
- Always read `prompts/test-effectiveness-analysis.md`; it is the complete execution specification for this skill.
- For evaluation, read `evals/eval.yaml` and the matching cases under `evals/cases/`.
- Load `references/`, `examples/`, `scripts/`, or `output-formats.md` only when those directories exist and the task needs them.
## Core Constraints
- Keep the analysis focused on the relationship between test signals and defects or risks, missed-detection limits, false signals, and validation plans; do not replace business owners or Human risk acceptance, exception approval, or release decisions.
- Never invent system behavior, fields, metrics, thresholds, data, root causes, execution records, or pass claims.
- Static design, plans, file presence, or a dry run retain their evidence state and cannot become proof of real execution.
- When evidence is insufficient, use pending confirmation, blocked, unassessed, or NOT_SCORED and give the smallest validation method.
- For production, privacy, or security work, use least privilege, masked data, mocks, dry runs, or isolation.
## Delivery Checklist
- [ ] Covered test signal, risk coverage, missed-detection limit, false signal, validation plan, with source, evidence state, and validation method for each.
- [ ] Separated facts, inferences, candidate recommendations, gaps, and Human decisions.
- [ ] Gave high-risk items P0/P1/P2/P3 or an equivalent priority, owner role, and close condition.
- [ ] Did not turn plans, static checks, or dry runs into test execution, all-passed, or release-approved claims.
- [ ] Stated residual risk, stop/escalation conditions, and next actions.
## Common Pitfalls
- Listing checks without triggers, expected concerns, owner roles, close conditions, and evidence.
- Treating adjacent metrics or tool names as a complete test effectiveness judgment.
- Using unexplained numbers for false precision or writing correlation as causation.
- Refusing incomplete input, or pretending that incomplete evidence is conclusive.
## Best Practices
- Start with paths most likely to cause business loss, quality regression, or decision blockage.
- Use the smallest verifiable experiment to reduce uncertainty and record conditions, versions, sources, and evidence.
- Make the Skill independently installable, executable, and reviewable by another engineer.