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Agent Skill · 研究检索

analyze-test-run(分析测试运行)

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

analyze-test-run 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 6,340。

总安装 22,512周安装 938GitHub Stars 201下载 7,504核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:analyze-test-run(分析测试运行)
来源仓库:https://github.com/microsoft/github-copilot-for-azure
仓库路径:skills/analyze-test-run
安装命令:
npx skills add https://github.com/microsoft/github-copilot-for-azure --skill analyze-test-run
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/microsoft/github-copilot-for-azure --skill analyze-test-run

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

analyze-test-run 下载 GitHub Actions 集成测试运行产物,生成技能调用报告并为失败项创建 Issue。适用于自动化测试流水线监控,追踪 skill invocation rate 与部署链完整性。

输入 Run ID 或 URL,自动关联 azure-prepare/validate/deploy 阶段日志,定位故障节点。需配置 GitHub Token 与 Actions 读取权限,确保能访问 artifacts 与 issue 创建 API。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/analyze-test-run

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/microsoft/github-copilot-for-azure --skill ana…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

analyze-test-run 怎么安装?

在终端执行 npx skills add https://github.com/microsoft/github-copilot-for-azure --skill analyze-test-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

analyze-test-run 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

analyze-test-run 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

analyze-test-run 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

analyze-test-run 用的人多吗?

公开数据是累计安装 22,512 次,近一周安装 938 次,下载 7,504 次,GitHub 201 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

analyze-test-run 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
analyze-test-run

分析测试运行 · 本页

22,512201调用外部服务未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Analyze Test Run

Downloads artifacts from a GitHub Actions integration test run, generates a summarized skill invocation report, and files GitHub issues for each test failure with root-cause analysis.

When to Use

  • Summarize results of a GitHub Actions integration test run
  • Calculate skill invocation rates for the skill under test
  • For azure-deploy tests: track the full deployment chain (azure-prepare → azure-validate → azure-deploy)
  • Compare skill invocation across two runs
  • File issues for test failures with root-cause context

Input

ParameterRequiredDescription
Run ID or URLYesGitHub Actions run ID (e.g. 22373768875) or full URL
Comparison RunNoSecond run ID/URL for side-by-side comparison

MCP Tools

All tools use owner: "microsoft" and repo: "GitHub-Copilot-for-Azure" as fixed parameters. method selects the operation within the tool.

ToolmethodKey ParameterPurpose
actions_getget_workflow_runresource_id: run IDFetch run status and metadata
actions_listlist_workflow_run_artifactsresource_id: run IDList all artifacts for a run
actions_getdownload_workflow_run_artifactresource_id: artifact IDGet a temporary download URL for an artifact ZIP
get_job_logs—run_id + failed_only: trueRetrieve job logs when artifact content is inaccessible
search_issues—query: search stringFind existing open issues before creating new ones
create_issue—title, body, labels, assigneesFile a new GitHub issue for a test failure

Workflow

Phase 1 — Download & Parse

  1. Extract the numeric run ID from the input (strip URL prefix if needed)
  2. Fetch run metadata using the MCP actions_get tool: actions_get({method: "get_workflow_run", owner: "microsoft", repo: "GitHub-Copilot-for-Azure", resource_id: "<run-id>"})
  3. List artifacts using the MCP actions_list tool, then download each relevant artifact: // List artifacts actions_list({method: "list_workflow_run_artifacts", owner: "microsoft", repo: "GitHub-Copilot-for-Azure", resource_id: "<run-id>"}) // Download individual artifacts by ID actions_get({method: "download_workflow_run_artifact", owner: "microsoft", repo: "GitHub-Copilot-for-Azure", resource_id: "<artifact-id>"}) The download returns a temporary URL. Fetch the ZIP archive from that URL and extract it locally. If the environment restricts outbound HTTP (e.g. AWF sandbox), record in the analysis report that artifact content was unavailable and fall back to job logs via the get_job_logs MCP tool.
  4. Locate these files in the downloaded artifacts: ⚠️ Note: If artifact ZIP files cannot be downloaded due to network restrictions, or if downloaded files cannot be extracted, use the get_job_logs MCP tool to identify test failures and produce a best-effort analysis from whatever data is accessible.

- junit.xml — test pass/fail/skip/error results - *-SKILL-REPORT.md — generated skill report with per-test details - agent-metadata-*.md files — raw agent session logs per test

Phase 2 — Build Summary Report

Produce a markdown report with four sections. See report-format.md for the exact template.

Section 1 — Test Results Overview

Parse junit.xml to build:

MetricValue
Total testscount from <testsuites tests=…>
Executedtotal − skipped
Skippedcount of <skipped/> elements
Passedexecuted − failures − errors
Failedcount of <failure> elements
Test Pass Ratepassed / executed as %

Include a per-test table with name, duration (from time attribute, convert seconds to Xm Ys), and Pass/Fail result.

Section 2 — Skill Invocation Rate

Read the SKILL-REPORT.md "Per-Test Case Results" sections. For each executed test determine whether the skill under test was invoked.

The skills to track depend on which integration test suite the run belongs to:

azure-deploy integration tests — track the full deployment chain:

SkillHow to detect
azure-prepareMentioned as invoked in the narrative or agent-metadata
azure-validateMentioned as invoked in the narrative or agent-metadata
azure-deployMentioned as invoked in the narrative or agent-metadata

Build a per-test invocation matrix (Yes/No for each skill) and compute rates:

SkillInvocation Rate
azure-deployX% (n/total)
azure-prepareX% (n/total)
azure-validateX% (n/total)
Full skill chain (P→V→D)X% (n/total)
The azure-deploy integration tests exercise the full deployment workflow where the agent is expected to invoke azure-prepare, azure-validate, and azure-deploy in sequence. This three-skill chain tracking is specific to azure-deploy tests only.

All other integration tests — track only the skill under test:

SkillInvocation Rate
{skill-under-test}X% (n/total)

For non-deploy tests (e.g. azure-prepare, azure-ai, azure-kusto), only track whether the primary skill under test was invoked. Do not include azure-prepare/azure-validate/azure-deploy chain columns.

Section 3 — Report Confidence & Pass Rate

Extract from SKILL-REPORT.md:

  • Skill Invocation Success Rate (from the report's statistics section)
  • Overall Test Pass Rate (from the report's statistics section)
  • Average Confidence (from the report's statistics section)

Section 4 — Comparison (only when a second run is provided)

Repeat Phase 1–3 for the second run, then produce a side-by-side delta table. See report-format.md § Comparison.

Phase 3 — File Issues for Failures

For every test with a <failure> element in junit.xml:

  1. Read the failure message and file:line from the XML
  2. Read the actual line of code from the test file at that location
  3. Read the agent-metadata-*.md for that test from the artifacts
  4. Read the corresponding section in the SKILL-REPORT.md for context on what the agent did
  5. Determine root cause category:

- Skill not invoked — agent bypassed skills and used manual commands - Deployment failure — infrastructure or RBAC error during deployment - Timeout — test exceeded time limit - Assertion mismatch — expected files/links not found - Quota exhaustion — Azure region quota prevented deployment

  1. Search for existing open issue before creating a new one using the search_issues MCP tool: search_issues({owner: "microsoft", repo: "GitHub-Copilot-for-Azure", query: "Integration test failure: {skill} in:title is:open"}) Match criteria: an open issue whose title and body describe a similar problem. If a match is found, skip issue creation for this failure and note the existing issue number(s) in the summary report.
  2. If no existing issue was found, create a GitHub issue using the create_issue MCP tool, assign the label with the name of the skill, and assign it to the code owners listed in.github/CODEOWNERS file based on which skill it is for:
create_issue({
  owner: "microsoft", repo: "GitHub-Copilot-for-Azure",
  title: "Integration test failure: <skill> – <keywords> [<root-cause-category>]",
  labels: ["bug", "integration-test", "test-failure", "<skill>"],
  body: "<body>",
  assignees: ["<codeowners>"]
})

Title format: Integration test failure: {skill} – {keywords} [{root-cause-category}]

  • {keywords}: 2-4 words from the test name — app type (function app, static web app) + IaC type (Terraform, Bicep) + trigger if relevant
  • {root-cause-category}: one of the categories from step 5 in brackets

Issue body template — see issue-template.md.

⚠️ Note: Do NOT include the Error Details (JUnit XML) or Agent Metadata sections in the issue body. Keep issues concise with the diagnosis, prompt context, skill report context, and environment sections only. ⚠️ Note: Do NOT create issues for skill invocation test failures.
For azure-deploy integration tests, include an "azure-deploy Skill Invocation" section showing whether azure-deploy was invoked (Yes/No), with a note that the full chain is azure-prepare → azure-validate → azure-deploy. For all other integration tests, include a "{skill} Skill Invocation" section showing only whether the primary skill under test was invoked.

Error Handling

ErrorCauseFix
no artifacts foundRun has no uploadable reportsVerify the run completed the "Export report" step
HTTP 404 on actions_getInvalid run ID or no accessCheck the run ID and ensure the MCP token has repo access
rate limit exceededToo many GitHub API callsWait and retry; reduce concurrent MCP tool calls
Artifact ZIP download blockedAWF sandbox restricts outbound HTTP to blob storageUse get_job_logs MCP tool to get failure details from job logs; produce best-effort analysis from metadata

References

平台分布

Codex

31.29%
按下载量换算2,348

Claude

30.68%
按下载量换算2,302

Cursor

19.01%
按下载量换算1,427

Gemini CLI

9.15%
按下载量换算687

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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