agentCLAWHUBUnverified
qa-output-validation
在最终输出前对测试用例做最后一轮防幻觉验证：事实核查（引用的需求ID是否存在）、一致性检查（用例之间是否矛盾）、可执行性验证（步骤是否能实际操作）、来源追溯（每个用例是否能追溯到具体需求）。当测试用例已经生成完毕、准备输出了，但你不确定AI有没有编造不存在的功能或需求时，应当使用此技能。这是整个工作流的最终质量守门——如果验证失败，必须返回问题清单要求修正，不得跳过。 触发场景：验证一下输出、检查有没有幻觉、这个用例对吗、确认一下质量、时。 Use when the user asks about: final anti-hallucination verification of generated test cases — fact checking, cross-case consistency, executability, and requirement traceability. Skill: qa-output-validation Owner: kokxi Summary: 在最终输出前对测试用例做最后一轮防幻觉验证：事实核查（引用的需求ID是否存在）、一致性检查（用例之间是否矛盾）、可执行性验证（步骤是否能实际操作）、来源追溯（每个用例是否能追溯到具体需求）。当测试用例已经生成完毕、准备输出了，但你不确定AI有没有编造不存在的功能或需求时，应当使用此技能。这是整个工作流的最终质量守门——如果验证失败，必须返回问题清单要求修正，不得跳过。 触发场景：验证一下输出、检查有没有幻觉、这个用例对吗、确认一下质量、时。 Use when the user asks about: final anti-hallucination verification of generated test cases — fact checking, cross-case consistency, executability,
OpenClaw
Rank
62
Safety
84
Downloads
1.1k
Updated
Oct 11, 2026
Version
1.8.0
Source
CLAWHUB
About
What it does, and when to use it.
Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.
Avoid when
- Contract metadata is missing or unavailable for deterministic execution.
Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing
Public facts
Every fact links back to the source it came from.
- Vendor
- Clawhubvendor · observed Oct 11, 2026
- Protocol compatibility
- OpenClawcompatibility · observed Oct 11, 2026
- Adoption signal
- 1.1K downloadsadoption · observed Oct 11, 2026
- Latest release
- 1.8.0release · observed Sep 29, 2026
- Handshake status
- UNKNOWNsecurity
Install and run
Setup complexity: low.
clawhub skill install s170jw3s1atcj5jwhqb4r7v7eh8912kp:qa-output-validation- Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
- Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Contract: missing
curl -s "https://www.xpersona.co/api/v1/agents/clawhub-kokxi-qa-output-validation/snapshot"
Documentation
CLAWHUB
81,926 characters of source documentation, loaded on request.
Extracted files
4 files captured from the source.
SKILL.md
---
name: qa-output-validation
description: >-
在最终输出前对测试用例做最后一轮防幻觉验证：事实核查（引用的需求ID是否存在）、一致性检查（用例之间是否矛盾）、可执行性验证（步骤是否能实际操作）、来源追溯（每个用例是否能追溯到具体需求）。当测试用例已经生成完毕、准备输出了，但你不确定AI有没有编造不存在的功能或需求时，应当使用此技能。这是整个工作流的最终质量守门——如果验证失败，必须返回问题清单要求修正，不得跳过。 触发场景：验证一下输出、检查有没有幻觉、这个用例对吗、确认一下质量、时。 Use when the user asks about: final anti-hallucination verification of generated test cases — fact checking, cross-case consistency, executability, and requirement traceability.
license: MIT
allowed-tools: Read Grep Glob
metadata:
display-name: "Output Validation"
version: "1.8.0"
when-to-use: "AI生成测试用例后、最终输出前自动激活；用户说\"验证一下输出\"、\"检查有没有幻觉\"、\"这个用例对吗\"、\"确认一下质量\"时"
related-skills: "{\"upstream\":[\"qa-ai-output-critique\",\"qa-ai-blindspot-compensation\"],\"downstream\":[\"qa-test-reporting\"]}"
references: "[\"references/validation-dimensions.md\"]"
input-format: "{\"required\":[{\"name\":\"测试用例\",\"type\":\"array\",\"description\":\"AI生成的测试用例列表\"},{\"name\":\"需求解构表\",\"type\":\"object\",\"description\":\"原始需求解构结果\"}],\"optional\":[{\"name\":\"评审报告\",\"type\":\"object\",\"description\":\"评审结果\"}]}"
output-format: "{\"traceability\":[\"本技能验证输出，不新增唯一ID；问题清单关联到原用例ID（TC_{模块缩写}_{功能缩写}_{序号}，如 TC_API_LOGIN_001）\"],\"structure\":[\"覆盖率：标注口径（基于现有需求/输入文档），禁止\\\"全覆盖/100%\\\"绝对化表述；缺失模块标注\\\"未覆盖+原因\\\"\",{\"validation_result\":\"pass/fail\"},{\"fact_check\":\"事实核查结果\"},{\"consistency_check\":\"一致性检查结果\"},{\"executability_check\":\"可执行性验证结果\"},{\"issues\":\"问题清单\"},{\"traceability\":\"来源追溯\"}]}"
error-recovery-guidance: "{\"on_failure\":\"返回问题清单和具体失败原因，要求修正后重新生成\",\"retry_behavior\":\"修正后重新执行AI生成步骤\"}"
categories: "[\"Development\",\"Testing\",\"AI\"]"
depth-requirement: "{\"reference_value\":\"根据用例数量调整验证深度：简单×1/中等×2/复杂×3\",\"minimum\":\"至少完成事实核查、一致性检查、可执行性验证、来源追溯4项\"}"
---
> ⚠️ 本技能单独使用效果有限，建议配合完整技能集（12 步工作流）使用。安装：npx skills add Kokxi/qa-test-skills
> **⚠️ 安全警告**：本技能的示例可能涉及对虚无功能的删除或标记建议。
> 实际使用时请勿直接删除测试用例或功能项，先确认其来源并备份原数据。
> 本技能仅在 workspace/ 输出评估文件，不持久化、不外传、不跨会话复用。
# 输出验证
## 核心原则
AI可能编造不存在的内容——必须验证每个输出的依据。
## 加载时机
| 什么时候读 | 读哪个 |
|-----------|--------|
| 逐维做防幻觉校验时 | [`references/validation-dimensions.md`](references/validation-dimensions.md) |
> `验证维度`的完整内容已下沉至 `references/validation-dimensions.md`，避免每次触发都占用上下文。
## 验证流程
### 步骤1：事实核查
```text
执行内容：
1. 对比用例中的需求ID与需求解构表
2. 检查风险ID是否基于实际分析
3. 验证边界条件是否真实存在
4. 检查引用的行业知识是否准确
输出：
├─ 核查通过项：[列表]
├─ 核查失败项：[列表]
└─ 幻觉风险项：[列表]
```
### 步骤2：一致性检查
```text
执行内容：
1. 验证需求ID与用例的对应关系
2. 验证风险ID与用例的匹配关系
3. 验证场景与边界的覆盖关系
4. 验证评审结果与实际输出的一致性
输出：
├─ 一致项：[列表]
├─ 不一致项：[列表]
└─ 矛盾项：[列表]
```
### 步骤3：可执行性验证
```text
执行内容：
1. 检查测试步骤的具体性
2. 检查预期结果的可验证性
3. 检查测试数据的可构造性
4. 检查测试环境的可搭建性
输出：
├─ 可执行项：[列表]
├─ 部分可执行项：[列表]
└─ 不可执行项：[列表]
```
### 步骤4：生成验证报告
```markdown
## 输出验证报告
### 验证摘要
- 验证日期：YYYY-MM-DD
- 用例总数：XX条
- 验证结果：通过/不通过
### 事实核查
| 检查项 | 结果 | 说明 |
|--------|------|------|
| 需求真实性 | 通过/失败 | [说明] |
| 风险合理性 | 通过/失败 | [说明] |
| 边界可验证性 | 通过/失败 | [说明] |
| 行业标准准确性 | 通过/失败 | [说明] |
### 一致性检查
| 检查项 | 结果 | 说明 |
|--------|------|------|
| 需求ID匹配 | 通过/失败 | [_meta.json
{
"ownerId": "kn71y9b23csfx0ykgm55d5m9x5891zt8",
"slug": "qa-output-validation",
"version": "1.8.0",
"publishedAt": 1790656027439
}references/validation-dimensions.md
# 验证维度详解
> 本文是 `qa-output-validation` 的**验证维度详解**。逐维做防幻觉校验时读本文；
其余部分留在 SKILL.md，不必读本文。
---
### 维度1：事实核查
**目标**：验证AI输出是否基于真实信息，而非编造
```text
检查点：
├─ 需求是否真实存在？
│ └─ 用例中的需求ID是否在需求解构表中？
├─ 风险是否合理推断？
│ └─ 风险ID是否基于实际风险分析？
├─ 边界是否可验证？
│ └─ 边界条件是否真实存在？
├─ 行业标准是否准确？
│ └─ 引用的行业知识是否正确？
└─ 历史缺陷是否真实？
└─ 引用的历史缺陷是否有依据？
防幻觉检查：
├─ 检查是否有"凭空捏造"的内容
├─ 检查是否有"过度推断"的内容
├─ 检查是否有"错误引用"的内容
└─ 检查是否有"逻辑矛盾"的内容
```
### 维度2：一致性检查
**目标**：验证输出各部分是否一致
```text
检查点：
├─ 需求ID与用例是否一一对应？
│ └─ 每条用例的需求ID是否在需求列表中？
├─ 风险ID与用例是否匹配？
│ └─ 风险等级是否与用例内容一致？
├─ 场景与边界是否对应？
│ └─ 边界是否覆盖了相关场景？
├─ 评审结果与实际输出是否一致？
│ └─ 评审指出的问题是否已修正？
└─ 不同技能输出是否矛盾？
└─ 需求解构、场景树、边界分析是否一致？
一致性矩阵：
| 维度1 | 维度2 | 检查项 |
|-------|-------|--------|
| 需求 | 用例 | 需求ID是否匹配 |
| 风险 | 用例 | 风险ID是否匹配 |
| 场景 | 边界 | 边界是否覆盖场景 |
| 评审 | 输出 | 问题是否已修正 |
```
### 维度3：可执行性验证
**目标**：验证测试用例是否可实际执行
```text
检查点：
├─ 测试步骤是否具体可操作？
│ └─ 步骤是否清晰到可以由任何人执行？
├─ 预期结果是否可验证？
│ └─ 预期结果是否客观可测量？
├─ 测试数据是否可构造？
│ └─ 需要的测试数据能否准备？
├─ 测试环境是否可搭建？
│ └─ 需要的环境能否搭建？
└─ 测试工具是否可用？
└─ 需要的工具是否可获取？
可执行性评分：
- 10分：完全可执行，无任何障碍
- 7分：基本可执行，少量障碍可克服
- 4分：部分可执行，有明显障碍
- 1分：无法执行，需要重新设计
```
### 维度4：来源追溯
**目标**：标注每个输出的来源和依据
```text
追溯内容：
├─ 需求来源：来自用户输入/需求文档
├─ 风险来源：来自风险分析/行业经验
├─ 边界来源：来自边界分析/最佳实践
├─ 用例来源：来自哪个技能生成
└─ 评审来源：来自哪个评审维度
追溯格式：
每条用例标注：
- 需求来源：REQ-XXX（来自需求解构）
- 风险来源：RISK-XXX（来自风险分析）
- 生成来源：qa-ai-prompt-strategy
- 评审状态：已评审/未评审
```skill-card.md
## Description: Validates AI-generated test cases against requirements for factual accuracy, consistency, executability, and traceability before final delivery. This skill is ready for commercial/non-commercial use. ## Publisher: [kokxi](https://clawhub.ai/user/kokxi) ### License/Terms of Use: MIT-0 ## Use Case: QA practitioners and developers use this skill to check generated test cases against source requirements before delivery, flagging unsupported claims, contradictions, unworkable steps, and missing traceability. ### Deployment Geography for Use: Global ## Known Risks and Mitigations: Risk: Incorrectly flagged cases could lead to deletion of valid tests or requirements. Mitigation: Confirm findings against source requirements and back up originals before deleting or changing cases. Risk: The companion installation command may execute publisher-supplied code. Mitigation: Review the package or pinned source and trust the publisher before running the command. ## Reference(s): - [Validation dimensions](references/validation-dimensions.md) - [ClawHub skill listing](https://clawhub.ai/kokxi/skills/qa-output-validation) ## Skill Output: **Output Type(s):** [Text, Markdown, Guidance] **Output Format:** [Markdown validation report with pass/fail results, findings, and source traceability] **Output Parameters:** [1D] **Other Properties Related to Output:** [Findings refer to existing test case IDs; coverage statements identify their source scope and gaps.] ## Skill Version(s): 1.8.0 (source: skill frontmatter and ClawHub release) ## Ethical Considerations: Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
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Machine-readable data
The same record, as JSON, for agents and crawlers.
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