What makes qa-input-validation notable?

1.1K downloads. Trust evidence available

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qa-input-validation

在测试工作流开始前检查用户输入是否包含有效的需求描述和足够的上下文信息。当用户的测试请求过于模糊（只说"帮我测试"却没说测什么）、缺少必要的需求文档或上下文时，应当使用此技能来验证输入完整性。如果输入验证失败，必须返回缺失信息清单要求用户补充。适用于启动任何测试设计流程的第一步。触发场景：需求不清楚、信息不够、这个需求能测吗、用户输入模糊时自动激活（第一步）。 Use when the user asks about: checking whether the user's test request contains enough context before any test design work starts. Skill: qa-input-validation Owner: kokxi Summary: 在测试工作流开始前检查用户输入是否包含有效的需求描述和足够的上下文信息。当用户的测试请求过于模糊（只说"帮我测试"却没说测什么）、缺少必要的需求文档或上下文时，应当使用此技能来验证输入完整性。如果输入验证失败，必须返回缺失信息清单要求用户补充。适用于启动任何测试设计流程的第一步。触发场景：需求不清楚、信息不够、这个需求能测吗、用户输入模糊时自动激活（第一步）。 Use when the user asks about: checking whether the user's test request contains enough context before any test design work starts. Tags: latest:1.8.0 Version history: v1.8.0 | 2026-09-29T0

OpenClaw

View on ClawHub Homepage

Rank

Safety

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-input-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-input-validation/snapshot"

Documentation

CLAWHUB

71,130 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: qa-input-validation
description: >-
  在测试工作流开始前检查用户输入是否包含有效的需求描述和足够的上下文信息。当用户的测试请求过于模糊（只说"帮我测试"却没说测什么）、缺少必要的需求文档或上下文时，应当使用此技能来验证输入完整性。如果输入验证失败，必须返回缺失信息清单要求用户补充。适用于启动任何测试设计流程的第一步。 触发场景：需求不清楚、信息不够、这个需求能测吗、用户输入模糊时自动激活（第一步）。 Use when the user asks about: checking whether the user's test request contains enough context before any test design work starts.
license: MIT
allowed-tools: Read Grep Glob WebFetch
metadata:
  display-name: "Input Validation"
  version: "1.8.0"
  when-to-use: "用户说\"需求不清楚\"、\"信息不够\"、\"这个需求能测吗\"、用户输入模糊时自动激活（第一步）"
  related-skills: "{\"upstream\":[],\"downstream\":[\"qa-requirement-review\"]}"
  references: "[\"references/output-formats.md\"]"
  input-format: "{\"required\":[{\"name\":\"用户输入\",\"type\":\"string\",\"description\":\"用户的需求描述或问题\"}],\"optional\":[{\"name\":\"附件\",\"type\":\"file\",\"description\":\"上传的需求文档\"},{\"name\":\"URL\",\"type\":\"string\",\"description\":\"需求文档链接\"}]}"
  output-format: "{\"traceability\":[\"本技能验证输入，不产出唯一ID\"],\"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/need_more_info\"},{\"input_quality_score\":\"输入质量评分（1-10）\"},{\"missing_info\":\"缺失信息清单\"},{\"clarification_questions\":\"需要追问的问题\"}]}"
  error-recovery-guidance: "{\"on_failure\":\"返回缺失信息清单和追问问题，要求用户补充\",\"retry_behavior\":\"用户补充后重新执行输入验证\"}"
  categories: "[\"Development\",\"Testing\",\"AI\"]"
  depth-requirement: "{\"reference_value\":\"根据输入模糊度调整验证深度：简单×1/中等×2/复杂×3\",\"minimum\":\"至少检查需求明确性、上下文充分性、输入类型3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订单号、支付金额、截图、身份证、手机号等敏感数据。
> 实际使用时请勿粘贴真实生产数据、客户信息或财务凭证；测试前应脱敏/掩码处理。
> 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。

# 输入验证

## 核心原则

垃圾进，垃圾出——输入质量决定输出质量。
 本技能是整个QA Test Skills工作流的第一步，在需求评审之前执行，验证输入是否满足生成高质量测试用例的基本要求。

## 验证维度

### 维度1：需求明确性

```text
检查点：
├─ 是否有明确的功能描述？
├─ 是否有业务目标？
├─ 是否有用户角色？
└─ 是否有成功标准？

评分标准：
- 10分：需求完整清晰，包含所有必要信息
- 7分：需求基本清晰，缺少少量信息
- 4分：需求模糊，缺少关键信息
- 1分：需求不明，无法理解
```

### 维度2：上下文充分性

```text
检查点：
├─ 是否有业务背景？
├─ 是否有技术架构？
├─ 是否有历史缺陷？
├─ 是否有约束条件？
└─ 是否有参考文档？

评分标准：
- 10分：上下文完整，可直接生成
- 7分：上下文基本充分，可补充少量信息
- 4分：上下文不足，需要补充
- 1分：上下文缺失，无法生成
```

### 维度3：输入类型识别

```text
输入类型：
├─ 直接描述：文字描述需求
├─ 上传文件：附件/文件路径
├─ URL链接：http/https开头
└─ 混合输入：多种类型组合

验证规则：
- 直接描述：检查是否包含功能关键词
- 上传文件：检查文件是否可读取
- URL链接：检查URL是否可访问
- 混合输入：检查各部分是否完整
```

## 验证流程

### 步骤1：解析用户输入

```text
解析内容：
├─ 提取需求描述
├─ 识别输入类型
├─ 检查是否有附件/URL
└─ 提取关键词
```

### 步骤2：评估输入质量

```text
评估维度：
├─ 需求明确性（0-10分）
├─ 上下文充分性（0-10分）
├─ 信息完整性（0-10分）
└─ 可测试性（0-10分）

综合评分 = (需求明确性 + 上下文充分性 + 信息完整性 + 可测试性) / 4
```

### 步骤3：生成验证结果

```text
结果类型：
├─ pass（通过）：综合评分≥7分
├─ need_more_info（需要更多信息）：综合评分4-6分
└─ fail（失败）：综合评分<4分
```

## 加载时机

| 什么时候读 | 读哪个 |
|-----------|--------|
| 校验输入完整性或需要输出格式时 | [`references/output-formats.md`](references/output-formats.md) |

> `输出格式`的完整内容已下沉至 `references/output-formats.md`，避免每次触发都占用上下文。

## 输入类型速查表

| 输入类型 | 示例 | 验证重点 | 典型评分区间 |
|---------|------|---------|------------|
| 功能名称 |

_meta.json

{
  "ownerId": "kn71y9b23csfx0ykgm55d5m9x5891zt8",
  "slug": "qa-input-validation",
  "version": "1.8.0",
  "publishedAt": 1790656013494
}

references/output-formats.md

# 需求输入校验与输出格式详解

> 本文是 `qa-input-validation` 的**需求输入校验与输出格式详解**。校验输入完整性或需要输出格式时读本文；
其余部分留在 SKILL.md，不必读本文。

---


### 通过（pass）

```json
{
  "validation_result": "pass",
  "input_quality_score": 8,
  "missing_info": [],
  "recommendation": "输入质量良好，可以继续执行"
}
```

### 需要更多信息（need_more_info）

```json
{
  "validation_result": "need_more_info",
  "input_quality_score": 5,
  "missing_info": [
    "缺少业务背景描述",
    "缺少用户角色说明",
    "缺少约束条件"
  ],
  "clarification_questions": [
    "这个功能的业务目标是什么？",
    "主要用户有哪些角色？",
    "有什么技术约束或业务规则？"
  ],
  "recommendation": "请补充以上信息后再生成"
}
```

### 失败（fail）

```json
{
  "validation_result": "fail",
  "input_quality_score": 2,
  "missing_info": [
    "缺少功能描述",
    "缺少业务背景",
    "缺少所有必要信息"
  ],
  "clarification_questions": [
    "请描述需要测试的功能是什么",
    "这个功能的业务背景是什么",
    "主要用户是谁，核心流程是什么"
  ],
  "recommendation": "输入信息严重不足，无法生成有效测试用例"
}
```

skill-card.md

## Description:

Checks whether a testing request includes clear requirements and enough context before test design begins, and asks for missing information when needed.

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 before test design to check whether a request has enough detail to test. It scores input quality and identifies missing requirements and follow-up questions.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Testing requests may contain customer, payment, identity, or production data.

Mitigation: Mask sensitive information before providing requests, attachments, or links.

Risk: The optional related-skill installation suggestion uses an unpinned npx command.

Mitigation: Use a pinned version or a trusted source if installing the broader skill set.

## Reference(s):

- [Input validation output formats](references/output-formats.md)
- [ClawHub skill page](https://clawhub.ai/kokxi/skills/qa-input-validation)

## Skill Output:

**Output Type(s):** [JSON, Guidance]

**Output Format:** [Structured JSON validation result with a quality score, missing information, and clarification questions]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Result is pass, need_more_info, or fail; input quality is scored from 1 to 10.]

## Skill Version(s):

1.8.0 (source: 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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