ai-assist-test-audit
16-dimension test suite audit with depth control (quick/standard/deep), gap matrix, and health scoring. Covers coverage, quality, mocking, data management, CI/CD, performance, mutation testing, and modern patterns. Use when evaluating test suite quality, identifying testing gaps,
供给资产档案
研究与知识工作
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
场景
研究 Agent
I need my agent to research a topic, compare sources, and produce a concise report.
适配 Agent
Claude Code + CLI + Codex
适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。
安装
就绪
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
维护状态
新鲜
距上次推送 2 天
风险
需审查
许可证不清晰
GitHub 质量
88
61/100 质量 · 66/100 信任
覆盖标签
审查说明
许可证不清晰 · Financial research output is not financial advice; require human review before any live investment decision
Agent 采用评分卡
一眼查看信任、审计与安装准备度
这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。
质量
有潜力有用的候选项,但采用前应与替代方案比较。
信任
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
审计
需审查对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。
OpenAgentSkill 信任评分 v5
安装前需人工审查
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
88 个 GitHub Stars
仓库活跃度
88 个 Star,12 个 Fork
维护状态
距上次推送 2 天
许可证
未知
安装
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
安装安全性
标准软件包或运行时安装路径
权限范围
filesystem or document access, network or browser access
Agent 结果
暂未有 Agent 结果数据
文档
Usable metadata, review docs
风险摘要
生产前审查
- Repository license is unknown; consider adding a license file for clarity.
- Financial research output is not financial advice; require human review before any live investment decision.
- 许可证不清晰
- Quality score needs review
安装准备度
安装路径可用
- 安装路径可用
- 仓库证据可用
- 许可证不清晰
- 暂无 Agent 验证结果证据
Agent 可读元数据
这个 Skill 的机器可读决策数据。
使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。
适用任务
- 研究 Agent 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
- 检索来源
适用 Agent
安装决策
- 命令
- npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
- 策略
- 审查
- 人工审查
- 是
信任与风险
- 信任
- 58/100
- 审计
- 73/100
- 风险级别
- 需审查
结果闭环
- 端点
- /api/agent/outcome
- 事件 ID
- resolve
- 结果
- 5
不适用场景
- 需要厂商支持 SLA 的团队
- production agents without a repository review
- Repository license is unknown; consider adding a license file for clarity.
- 许可证不清晰
- Financial research output is not financial advice; require human review before any live investment decision
Agent 安全 v2
57/100 · 安装前审查
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
中
网络访问
Skill 可能访问远程页面、API、仓库或外部服务。
中
文件系统访问
Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。
- 许可证不清晰
安装目标
在你的 Agent 工作流中安装此 Skill
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install jparkerweb-ai-assist-test-auditAgent 解析计划
让 Agent 在安装前验证匹配度。
Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。
打开 JSON
/api/agent/resolve?task=Use%20ai-assist-test-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 文本
/api/agent/resolve?task=Use%20ai-assist-test-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
安装交接
/api/skills/jparkerweb-ai-assist-test-audit/install
Agent 应检查
- 从 Resolve API 检查任务匹配与替代方案。
- 检查审计评分、信任评分和安全策略警告。
- 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。
复制提示词
Task: Use ai-assist-test-audit in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-assist-test-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-test-audit/install
Install command: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent 交接
把安装路径交给 Agent,而不是再给一个目录页。
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
安装交接
/api/skills/jparkerweb-ai-assist-test-audit/install
LLM 文本格式
/api/skills/jparkerweb-ai-assist-test-audit/install?format=text
寻找替代方案
/api/skills/search?q=ai-assist-test-audit&limit=3
Agent 提示词
Use ai-assist-test-audit for this task. Review https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-test-audit/install, then install with: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-auditRegistry 元数据
用于自动选择 Skill 的 Agent 可读档案。
本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。
Agent 决策面板
Fallback candidate for Research agents
先用此 Skill 做原型验证,并保留备选方案。
栈中角色
备选候选
主要匹配
研究 Agent
信任标签
先做原型验证
安装路径
命令已就绪
适用场景
- 研究 Agent 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
证据
- 仓库近期活跃
- 已提供安装命令或 GitHub 仓库
- 61/100 质量档案
- 3 个 OpenAgentSkill 交互事件
先审查
- Repository license is unknown; consider adding a license file for clarity.
实施路径
- 1在沙盒 Agent 中安装它,并端到端完成一次研究 Agent任务。
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
信任档案
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub 采用度
检查88 个 GitHub Stars
Star/Fork 活跃度
检查88 个 Star,12 个 Fork; 当前元数据中没有议题活跃度信息
近期维护
通过距上次推送 2 天
许可证清晰度
检查未知
积极信号
- AI 审查已通过
- 安装路径可用
- 仓库证据可用
- 近期维护的仓库
- 安装命令未发现明显高风险模式
- 结果闭环已就绪,但需要首次真实 Agent 运行
安装前审查
- Repository license is unknown; consider adding a license file for clarity.
- Financial research output is not financial advice; require human review before any live investment decision.
- 许可证不清晰
- Quality score needs review
- GitHub adoption: 88 GitHub stars
- Stars/forks activity: 88 stars, 12 forks; issue activity unavailable in current metadata
- License clarity: Unknown
- 暂未有真实 Agent 结果报告
- 无人值守安装前需要人工审查
建议操作
Choose a stronger alternative or inspect the source manually before any install attempt.
质量档案
有潜力 适用于 Agent 工作流的候选
有用的候选项,但采用前应与替代方案比较。
工作流匹配
在这些场景使用此 Skill
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
工作流匹配
加入完整工作流
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
替代方案短名单
安装前对比
可能适合该任务的相近 Skill。
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概览
--- name: ai-assist-test-audit description: "16-dimension test suite audit with depth control (quick/standard/deep), gap matrix, and health scoring. Covers coverage, quality, mocking, data management, CI/CD, performance, mutation testing, and modern patterns. Use when evaluating test suite quality, identifying testing gaps, or assessing test infrastructure health." argument-hint: "[quick|standard|deep] [scope]" ---
# TEST AUDIT
**Objective:** Produce a severity-ranked test suite assessment with deterministic metrics, gap matrix, health score, and remediation plan across 16 dimensions. **When to use:** Evaluating test suite quality, identifying testing gaps, assessing test infrastructure health.
> This skill audits existing tests. To write new tests, ask your AI agent directly.
Start all responses with '🩺 [Test Audit Step X: Name]'
## Role
Test quality specialist evaluating test suites for effectiveness, completeness, and adherence to enterprise-grade standards across 16 dimensions.
## Context
**AGENTS.md check:** If `./AGENTS.md` exists, read it — follow test conventions, patterns, and architecture. Overrides defaults. If missing, warn and proceed.
**Spec awareness:** If `specs/` has active work, verify test changes don't conflict.
**Stack detection:** Detect framework, runner, coverage tool, file patterns, config. Research current best practices for detected stack version.
**Input:** `$ARGUMENTS` — optional depth (quick/standard/deep), focus areas, scope (directory/pattern/all). Default: standard, all applicable dims, entire suite. No test files: "No test suite found. Would you like me to help create tests?"
**Project type:** WEB / API / DIST / PERF / ALL (default). Determines dimension applicability.
## Rules
1. **Run tests before reviewing.** Execute suite for pass/fail, duration, coverage. Incomplete without deterministic metrics. 2. **Test behavior, not implementation.** Flag tests asserting on internal state. 3. **Flakiness is Critical severity.** Any non-deterministic test is worse than no test. 4. **Over-mocking is a code smell.** >50% mock setup lines = testing mocks not code. 5. **Coverage without assertions is theater.** Flag high-coverage with weak assertions. 6. **Adapt to depth.** Quick=3 dims (1,3,14), Standard=12-14 dims, Deep=all 16. 7. **Dimension applicability.** 14 ALL, Contract=API/DIST, Accessibility=WEB. N/A redistributes weight. 8. **Current-year standards.** Research specific framework version docs. 9. **Respect conventions.** AGENTS.md/config choices are not findings. 10. **Evidence required.** File:line, metric output, or code sample for every finding. 11. **Chat-only output.** Present ALL findings, tables, and scores in chat. Never create files without explicit user permission.
## Process
### Step 1: Context & Infrastructure
1. Read AGENTS.md, run `git status`, detect stack (framework, runner, coverage tool) 2. Parse arguments for depth, focus, scope; count test files 3. Determine project type (WEB/API/DIST/PERF/ALL) and active dimensions
> 🩺 [Test Audit Step 1] Suite: [framework] with [tool]. [X] files. Depth: [depth]. Active: [N]/16.
### Step 2: Test Execution
1. Run suite with coverage (confirm with user if side effects uncertain) 2. Record: total, passing, failing, skipped, duration, line/branch/function % 3. If tests fail: note failures, continue audit
> 🩺 [Test Audit Step 2] [X] pass, [Y] fail, [Z] skip. Coverage: [X]% lines, [Y]% branches. [X]s.
### Step 3: Dimension Audit
Read `references/dimensions.md` for the depth mapping table, dimension activation rules, and per-dimension check definitions.
1. Activate dimensions per depth: Quick (1,3,14), Standard (1-8, 11 if API/DIST, 12 if WEB, 13-16), Deep (all 16) 2. Skip N/A dimensions, redistribute weight proportionally 3. Audit each activated dimension using the check definitions 4. Score each dimension
> 🩺 [Test Audit Step 3] Auditing dimension [X/Y]: [Name]...
### Step 4: Gap Matrix
Build module-by-dimension grid showing coverage across the codebase. Columns adapt to depth level: Quick shows Cov/Qual/Edge only, Standard shows all active, Deep shows all 16.
> 🩺 [Test Audit Step 4] Gap matrix: [X] modules, [Y] gaps identified.
### Step 5: Findings & Score
Read `references/scoring.md` for health score calculation, severity definitions, and deterministic metrics thresholds.
Read `references/output-template.md` for finding format, summary table, gap matrix format, positive observations, improvement plan, and fix options.
1. Calculate health score using dimension weights and N/A redistribution 2. Populate deterministic metrics table from actual execution (Step 2) 3. Rank findings by severity (Critical > Warning > Suggestion) 4. Present: finding details, summary table, gap matrix, metrics, positive observations, health score, improvement plan, fix options
### Self-Verification
> Canonical version in `references/output-template.md`. Brief version here for quick reference.
- [ ] Tests executed (or documented why not) - [ ] Metrics from actual output, not estimates - [ ] Every finding has file:line - [ ] Over-mocking verified by reading mock setup - [ ] Gap matrix reflects actual modules - [ ] AGENTS.md conventions respected - [ ] All active dimensions audited - [ ] Weights consistent with depth/N/A redistribution - [ ] Depth mapping correct (Q=3, S=12-14, D=16)
### Session End
> 🩺 [Test Audit Complete] > > **Score:** [XX]/100. Depth: [depth]. Dimensions: [N]/16. > **Findings:** [X] critical, [Y] warnings, [Z] suggestions. > **Metrics:** [X]/[Y] passing, [Z]% coverage, [W]s duration.
**Next steps (ask user — do not auto-execute):** - Save report to `specs/audit-reports/test-audit-<date>.md`? - Implement fixes? (offer fix options by priority) - Create remediation plan? → `/1-plan` with findings as input - Deeper analysis? → `/ai-assist-security-audit`, `/ai-assist-tech-debt`
## Recovery
| Issue | Solution | |-------|----------| | Suite won't run | Audit code quality without execution; note in report | | No coverage tool | Recommend one; audit without coverage metrics | | Tests >5 minutes | --bail or scope to directory; note in report | | No test files | Critical finding; offer to help create tests | | Mutation too slow | Scope to critical modules or skip | | N/A ambiguous | Default ALL; skip Contract/Accessibility only with evidence |
## Important Reminders
**Response format:** Every response starts with `🩺 [Test Audit Step X: Name]`
**Hard rules:** Run tests first (rule 1). Flakiness is Critical (rule 3). Evidence for every finding (rule 10). Metrics from execution, not estimation.
**Process rules:** Adapt to depth (rule 6). Applicability controls activation (rule 7). Gap matrix for all depths. Summary table mandatory. Weights sum to 100 with N/A redistribution.
**Related:** `/ai-assist-security-audit` for security posture, `/ai-assist-observability-audit` for telemetry, `/ai-assist-tech-debt` for codebase health.
技术详情
- 版本
- 1.0.0
- 许可证
- Unknown
- 最近更新
- 2026年8月21日
- 发布时间
- 2026年8月21日
决策摘要
备选候选
仓库近期活跃
Agent 验证证据
Agent 验证证据
来自解析、审查、安装和一次小范围运行后的结果报告。
- 成功率
- —
- 近期失败
- —
- 结果
- 0
- 输出质量
- —
- 失败
- 0
- 不相关
- 0
- 安装次数
- 0
- 风险拦截
- 0
- 需要配置
- 0
- 生产环境
- 0
暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。
增长闭环
分享工具包
为 ai-assist-test-audit 准备的场景化草稿,可手动发布到 X。
ai-assist-test-audit: 16-dimension test suite audit with depth control (quick/standard/deep), gap matrix, and healt... 88 stars https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit?ref=x
可选:带安装命令的回复
Listing + install path for ai-assist-test-audit: https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit?ref=x Install: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-test-audit
收录来源
Registry 收录
此列表来自公开来源,维护者认领获批前不会标记为官方。
- 创作者
- jparkerweb
- 收录方
- OpenAgentSkill 社区索引
归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。
认领此 Skill所有者认领
认领此 Skill 页面
这条 Registry 收录 列表归属于 jparkerweb,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。
创作者外链工具包
将证据徽章加入你的 README
在开发者评估仓库的位置展示规范页面、当前信任与审计信号,以及真实的 Agent 验证证据。
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit/audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-test-audit)作者
jparkerweb
@jparkerweb
平台适配
健康信号
- GitHub Stars
- 88
- 质量评分
- 37/100
- 最近 GitHub 推送
- 2026年8月20日
- 框架提示
- 未知
- OpenAgentSkill 浏览量
- 3
- 复制安装命令
- 0
- 跳转点击
- 0
社区信号
告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。
信任与安全
Do not auto-install
- GitHub 采用度88 个 GitHub Stars检查
- Star/Fork 活跃度88 个 Star,12 个 Fork; 当前元数据中没有议题活跃度信息检查
- 近期维护距上次推送 2 天通过
- 许可证清晰度未知检查
- README/SKILL.md 完整度公开元数据需要更完整的 README/SKILL.md 上下文信息
- 依赖与运行时风险network or browser surface通过
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