Xpersona
agentCLAWHUBUnverified

qa-quality-metrics

当管理层问"质量到底怎么样"、需要量化质量数据来做决策、或者想建立质量看板来跟踪趋势时使用此技能。从过程质量（需求评审通过率/用例覆盖度）、结果质量（Bug 密度/线上事故数）、效率（测试周期/回归耗时）和健康度（自动化通过率/环境稳定性）四个维度设计度量指标。⚠️ 度量的目的不是打分，是发现问题趋势——如果只报喜不报忧，度量就没用了。 触发场景：质量度量、质量指标、怎么量化质量、质量看板、质量数据、趋势分析、向管理层展示质量数据时。 Use when the user asks about: test quality metrics and dashboards — process, result, efficiency, and health indicators for management reporting. Skill: qa-quality-metrics Owner: kokxi Summary: 当管理层问"质量到底怎么样"、需要量化质量数据来做决策、或者想建立质量看板来跟踪趋势时使用此技能。从过程质量（需求评审通过率/用例覆盖度）、结果质量（Bug 密度/线上事故数）、效率（测试周期/回归耗时）和健康度（自动化通过率/环境稳定性）四个维度设计度量指标。⚠️ 度量的目的不是打分，是发现问题趋势——如果只报喜不报忧，度量就没用了。 触发场景：质量度量、质量指标、怎么量化质量、质量看板、质量数据、趋势分析、向管理层展示质量数据时。 Use when the user asks about: test quality metrics and dashboards — process, result, efficiency, and health indicators for management reporting. Tags: l

OpenClaw
View on ClawHubHomepage

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-quality-metrics
  1. 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.
  2. 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-quality-metrics/snapshot"

Documentation

CLAWHUB

60,990 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: qa-quality-metrics
description: >-
  当管理层问"质量到底怎么样"、需要量化质量数据来做决策、或者想建立质量看板来跟踪趋势时使用此技能。从过程质量（需求评审通过率/用例覆盖度）、结果质量（Bug 密度/线上事故数）、效率（测试周期/回归耗时）和健康度（自动化通过率/环境稳定性）四个维度设计度量指标。⚠️ 度量的目的不是打分，是发现问题趋势——如果只报喜不报忧，度量就没用了。 触发场景：质量度量、质量指标、怎么量化质量、质量看板、质量数据、趋势分析、向管理层展示质量数据时。 Use when the user asks about: test quality metrics and dashboards — process, result, efficiency, and health indicators for management reporting.
license: MIT
allowed-tools: Read Grep Glob
metadata:
  display-name: "Quality Metrics"
  version: "1.8.0"
  when-to-use: "用户说\"质量度量\"、\"质量指标\"、\"怎么量化质量\"、\"质量看板\"、\"质量数据\"、\"趋势分析\"、需要建立度量体系、向管理层展示质量数据时"
  related-skills: "{\"upstream\":[\"qa-release-risk-governance\",\"qa-bug-lifecycle\"],\"downstream\":[\"qa-retrospective\",\"qa-testability-advocacy\",\"qa-stakeholder-communication\",\"qa-tech-debt-management\",\"qa-test-reporting\"]}"
  references: "[\"references/metrics-dimensions.md\"]"
  input-format: "{\"required\":[{\"name\":\"测试数据\",\"type\":\"object\",\"description\":\"测试执行数据和结果\"},{\"name\":\"缺陷数据\",\"type\":\"object\",\"description\":\"缺陷统计和分析数据\"}],\"optional\":[{\"name\":\"历史基线\",\"type\":\"object\",\"description\":\"历史质量基线数据\"}]}"
  output-format: "{\"traceability\":[\"每份度量报告带唯一ID（METRIC-XXXX）\"],\"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\\\"\",{\"quality_dashboard\":\"质量仪表盘\"},{\"defect_density\":\"缺陷密度分析\"},{\"test_coverage\":\"测试覆盖率\"},{\"pass_fail_rate\":\"通过/失败率\"},{\"trend_analysis\":\"质量趋势分析\"},\"覆盖率：涉及覆盖率的结论必须标注口径（基于现有需求/输入文档），禁止\\\"全覆盖/100%\\\"绝对化表述\"]}"
  error-recovery-guidance: "{\"on_failure\":\"度量数据缺失时回退到测试执行和缺陷数据收集\",\"retry_behavior\":\"补齐数据后重新计算指标\"}"
  categories: "[\"Development\",\"Testing\",\"DevOps\"]"
  depth-requirement: "{\"reference_value\":\"根据度量维度调整指标深度：简单×1/中等×2/复杂×3\",\"minimum\":\"至少覆盖过程质量、结果质量、效率、健康度4个维度\"}"
---
> ⚠️ 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

# 质量度量体系

## 核心原则

质量不是感觉，是可以量化的。

## 加载时机

| 什么时候读 | 读哪个 |
|-----------|--------|
| 选指标、算指标或查数据来源时，取四类详表 | [`references/metrics-dimensions.md`](references/metrics-dimensions.md) |

> `四类度量指标`的完整内容已下沉至 `references/metrics-dimensions.md`，避免每次触发都占用上下文。

## 度量报告模板

```markdown
# 质量度量报告

## 1. 过程度量
- 用例执行率：[X]% (目标≥95%)
- 用例通过率：[X]% (目标≥90%)
- 需求覆盖率：[X]% (目标100%)
- 自动化覆盖率：[X]%

## 2. 结果度量
- 缺陷密度：[X]/功能点
- 缺陷修复率：[X]% (目标≥95%)
- 漏测率：[X]% (目标≤5%)
- 逃逸率：[X]% (目标≤10%)

## 3. 效率度量
- 测试周期：[X]天
- 用例执行效率：[X]条/人天
- 缺陷发现效率：[X]个/人天
- 缺陷修复周期：[X]天

## 4. 健康度量
- 缺陷趋势：[上升/稳定/下降]
- 缺陷收敛：[收敛/发散]
- 质量趋势：[改善/稳定/恶化]

## 5. 风险分析
- 高风险模块：[模块列表]
- 主要问题：[问题描述]
- 改进建议：[建议列表]
```

## 输出示例

**需要度量当前迭代的质量**
→ 四类度量：
  - 过程度量：用例评审通过率、缺陷发现率
  - 结果度量：遗留缺陷密度、线上故障数
  - 效率度量：测试执行效率、缺陷平均修复时间
  - 健康度量：缺陷趋势图、质量评分变化
→ 输出：质量度量报告，含目标值对比和改进方向

**用户说"质量到底怎么样"**
→ 自动生成质量度量报告，用数据展示质量趋势

## 检查清单

质量度量完成后检查：
- [ ] 度量指标是否定义？
- [ ] 数据收集是否可行？
- [ ] 目标值是否合理？
- [ ] 报告格式是否清晰？
- [ ] 改进建议是否可行？

_meta.json

{
  "ownerId": "kn71y9b23csfx0ykgm55d5m9x5891zt8",
  "slug": "qa-quality-metrics",
  "version": "1.8.0",
  "publishedAt": 1790656033833
}

references/metrics-dimensions.md

# 四类质量度量指标详解

> 本文是 `qa-quality-metrics` 的**四类度量指标详解**。选指标、算指标或找埋点来源时读本文；
只需要度量框架与看板设计原则时不必读。

---


### 度量1：过程度量（测试过程质量）

```text
核心指标：
├─ 用例执行率 = 已执行用例数 / 总用例数 × 100%
│   ├─ 目标：≥ 95%
│   └─ 意义：测试覆盖完整性
│
├─ 用例通过率 = 通过用例数 / 已执行用例数 × 100%
│   ├─ 目标：≥ 90%
│   └─ 意义：测试执行质量
│
├─ 需求覆盖率 = 已覆盖需求数 / 总需求数 × 100%
│   ├─ 目标：100%
│   └─ 意义：需求覆盖完整性
│
├─ 自动化覆盖率 = 自动化用例数 / 总用例数 × 100%
│   ├─ 目标：根据项目定义
│   └─ 意义：自动化程度
│
└─ 用例有效率 = 有效用例数 / 总用例数 × 100%
    ├─ 目标：≥ 80%
    └─ 意义：用例设计质量
```

### 度量2：结果度量（产品质量）

```text
核心指标：
├─ 缺陷密度 = 缺陷数 / 功能点数（或代码行数）
│   ├─ 目标：根据项目定义
│   └─ 意义：代码质量
│
├─ 缺陷修复率 = 已修复缺陷数 / 总缺陷数 × 100%
│   ├─ 目标：≥ 95%
│   └─ 意义：缺陷处理效率
│
├─ 漏测率 = 线上缺陷数 / 总缺陷数 × 100%
│   ├─ 目标：≤ 5%
│   └─ 意义：测试有效性
│
├─ 逃逸率 = 线上缺陷数 / (线上缺陷数 + 测试缺陷数) × 100%
│   ├─ 目标：≤ 10%
│   └─ 意义：测试拦截能力
│
└─ 严重缺陷占比 = 严重缺陷数 / 总缺陷数 × 100%
    ├─ 目标：≤ 10%
    └─ 意义：缺陷严重程度分布
```

### 度量3：效率度量（测试效率）

```text
核心指标：
├─ 测试周期 = 测试结束日期 - 测试开始日期
│   ├─ 目标：根据项目定义
│   └─ 意义：测试效率
│
├─ 用例执行效率 = 执行用例数 / 测试工时
│   ├─ 目标：根据项目定义
│   └─ 意义：执行效率
│
├─ 缺陷发现效率 = 发现缺陷数 / 测试工时
│   ├─ 目标：根据项目定义
│   └─ 意义：缺陷发现能力
│
├─ 缺陷修复周期 = 缺陷修复日期 - 缺陷提交日期
│   ├─ 目标：≤ 3天（严重）
│   └─ 意义：缺陷处理效率
│
└─ 回归测试效率 = 回归用例数 / 回归工时
    ├─ 目标：根据项目定义
    └─ 意义：回归测试效率
```

### 度量4：健康度量（质量趋势）

```text
核心指标：
├─ 缺陷趋势：新增缺陷数/天
│   ├─ 正常：逐渐减少
│   └─ 异常：突然增加
│
├─ 缺陷收敛：累计缺陷曲线
│   ├─ 正常：趋于平缓
│   └─ 异常：持续上升
│
├─ 质量趋势：缺陷密度/版本
│   ├─ 正常：逐渐降低
│   └─ 异常：突然升高
│
└─ 风险热力图：模块×缺陷密度
    ├─ 正常：低密度模块为主
    └─ 异常：高密度模块集中
```

skill-card.md

## Description:

Helps QA teams define and report process, product, efficiency, and health metrics to track quality trends and inform decisions.

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 engineers and managers use this skill to build quality dashboards and reports from test execution and defect data, compare trends and targets, and identify areas for improvement.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The optional companion-install command fetches an unpinned external package.

Mitigation: Run it only after trusting the publisher and verifying or pinning the exact version.

## Reference(s):

- [Quality metrics dimensions](references/metrics-dimensions.md)
- [ClawHub skill listing](https://clawhub.ai/kokxi/skills/qa-quality-metrics)

## Skill Output:

**Output Type(s):** [Markdown, Analysis, Guidance]

**Output Format:** [Markdown quality metrics report with dashboard summaries and recommendations]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Reports use METRIC-XXXX IDs, state coverage denominators and missing areas, and avoid unsupported claims of complete coverage.]

## Skill Version(s):

1.8.0 (source: ClawHub release metadata and skill frontmatter)

## 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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Record generated Oct 11, 2026.

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