Disaster-Recovery Testing - QA Skill Library

Why this Skill

It turns this Skill's method into a quality input that can be executed, reviewed, and reused.

Use this Skill when the work needs evidence-bounded analysis of disaster scenarios, recovery priority, backup evidence, runbooks, and restore validation.
Analyze only disaster scenarios, recovery priority, backup evidence, runbooks, and restore validation; do not inject faults, access real dependencies, or call production systems.
Do not invent thresholds, availability, recovery times, vulnerability states, or completed test runs.
Mark unsupported claims as pending, blocked, or unassessed and provide a validation method.
Treating adjacent performance, incident, or API analysis as a complete substitute for Disaster-Recovery Testing.

When to use

Use this Skill

Use this Skill when the work needs evidence-bounded analysis of disaster scenarios, recovery priority, backup evidence, runbooks, and restore validation.
Use it when the input is incomplete but a reviewable first draft with assumptions and gaps is still useful.
Use it when static design evidence must remain separate from planned validation and completed execution.

Common pitfalls

Treating adjacent performance, incident, or API analysis as a complete substitute for Disaster-Recovery Testing.
Listing steps without triggers, expected results, owner roles, or close conditions.
Refusing incomplete input, or filling critical facts with template assumptions.

Input

Minimum Input

Read prompts/disaster-recovery-testing.md and follow its input audit, coverage checklist, and output order.

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.

Judge the output value before installing

01Read prompts/disaster-recovery-testing.md and follow its input audit, coverage checklist, and output order.
02Extract scope, environment, version, dependencies, constraints, success criteria, and available evidence.
03Model disaster scenarios, recovery priority, backup evidence, runbooks, and restore validation with scenarios and decision criteria, prioritizing high-impact or hard-to-detect items.
04Separate facts, evidence-backed inferences, candidate recommendations, and Human decisions.

View full output structure

05When information is missing, deliver a bounded draft and the smallest evidence-gathering actions; do not write recommendations as execution results.

How It Works

01Read prompts/disaster-recovery-testing.md and follow its input audit, coverage checklist, and output order.
02Extract scope, environment, version, dependencies, constraints, success criteria, and available evidence.
03Model disaster scenarios, recovery priority, backup evidence, runbooks, and restore validation with scenarios and decision criteria, prioritizing high-impact or hard-to-detect items.
04Separate facts, evidence-backed inferences, candidate recommendations, and Human decisions.
05When information is missing, deliver a bounded draft and the smallest evidence-gathering actions; do not write recommendations as execution results.

Install & Quick Start

Install command / SHELL

npx skills add \
  https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/disaster-recovery-testing
  -g

disaster-recovery-testing.prompt

@skill disaster-recovery-testing

Using the current project context, produce an actionable result following this Skill.

Additional context:
[Paste project context or requirement]

---
name: disaster-recovery-testing
description: Use this skill when you need evidence-bounded disaster-recovery-testing analysis and validation preparation; triggers include 灾备测试 and disaster-recovery-testing.
---

# Disaster-Recovery Testing

## When to Use

- Use this Skill when the work needs evidence-bounded analysis of disaster scenarios, recovery priority, backup evidence, runbooks, and restore validation.
- Use it when the input is incomplete but a reviewable first draft with assumptions and gaps is still useful.
- Use it when static design evidence must remain separate from planned validation and completed execution.

## Output Format Options

- Default to Markdown organized by risk, evidence, and priority.
- If the user asks for a table, CSV, JSON, or ticket format, preserve the same finding fields and evidence states.
- Confirm the schema, enum values, and required fields before feeding the output to automation.

## How to Use

1. Read prompts/disaster-recovery-testing.md and follow its input audit, coverage checklist, and output order.
2. Extract scope, environment, version, dependencies, constraints, success criteria, and available evidence.
3. Model disaster scenarios, recovery priority, backup evidence, runbooks, and restore validation with scenarios and decision criteria, prioritizing high-impact or hard-to-detect items.
4. Separate facts, evidence-backed inferences, candidate recommendations, and Human decisions.
5. When information is missing, deliver a bounded draft and the smallest evidence-gathering actions; do not write recommendations as execution results.

## Reference Files

- Read prompts/disaster-recovery-testing.md for every invocation; it is the complete execution contract.
- Read evals/eval.yaml and the matching evals/cases/ when evaluating the Skill.
- Read references/, examples/, scripts/, or output-formats.md only when the directory exists and the task needs it.

## Core Constraints

- Analyze only disaster scenarios, recovery priority, backup evidence, runbooks, and restore validation; do not inject faults, access real dependencies, or call production systems.
- Do not invent thresholds, availability, recovery times, vulnerability states, or completed test runs.
- Mark unsupported claims as pending, blocked, or unassessed and provide a validation method.
- Leave risk acceptance, release approval, and Human takeover to a Human.

## Delivery Self-Check

- [ ] Complete the six-part input audit and mark evidence freshness.
- [ ] Cover the disaster-recovery objective, failure modes, expected concerns, and validation method.
- [ ] Separate facts, inferences, recommendations, gaps, and Human decisions.
- [ ] Do not turn static design or a dry-run into a claim of execution, passing, or release.

## Common Pitfalls

- Treating adjacent performance, incident, or API analysis as a complete substitute for Disaster-Recovery Testing.
- Listing steps without triggers, expected results, owner roles, or close conditions.
- Refusing incomplete input, or filling critical facts with template assumptions.

## Best Practices

- Start with the paths most likely to cause business loss or recovery failure.
- Use the smallest isolated and reversible validation suggestion, with explicit stop conditions.
- Make every conclusion reviewable by another engineer from its evidence and boundary.