Token导航 LogoToken导航

Agent Skill · 研究检索

qa-team(质量保证小组)

qa-team 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

qa-team 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,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 55,945。

总安装 466周安装 20GitHub Stars 34,189下载 163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:qa-team(质量保证小组)
来源仓库:https://github.com/posthog/posthog
仓库路径:skills/qa-team
安装命令:
npx skills add https://github.com/posthog/posthog --skill qa-team
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/posthog/posthog --skill qa-team

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。

qa-team 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。它支持基于关键词或任务场景的信息匹配,适用于团队协作与沟通相关的资料准备。

通过安装命令 npx skills add https://github.com/posthog/posthog --skill qa-team 添加技能,具体用法可参考仓库中的 SKILL.md。安装前请确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。

qa-team 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/qa-team

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/posthog/posthog --skill qa-team;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

    它需要需要联网,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

qa-team 怎么安装?

在终端执行 npx skills add https://github.com/posthog/posthog --skill qa-team。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

qa-team 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。

qa-team 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

qa-team 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。

qa-team 用的人多吗?

公开数据是累计安装 466 次,近一周安装 20 次,下载 163 次,GitHub 34,189 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。

qa-team 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 34,189 星,维护活跃度相对可查。

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
qa-team

质量保证小组 · 本页

46634,189需要联网未明确通过

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

QA Team: Multi-Agent Code Review

A team of specialist agents independently review the current branch's changes against real incident patterns. Their findings are synthesized into a single report with convergence analysis.

Agent independence is critical. Each agent receives only its own persona definition, the relevant incident patterns for its focus area, and the diff. Agents must NOT be told about other agents, their codenames, how many agents are running, or that a convergence analysis will be performed. This ensures findings are fully independent.

Workflow

Step 1: Gather the diff

Determine the base branch. If the user provided $ARGUMENTS, use that as the base branch. Otherwise, default to master.

Run these commands to collect context:

git diff <base>...HEAD --name-only
git diff <base>...HEAD
git log <base>...HEAD --oneline

Store the full diff, changed file list, and commit messages. These will be passed to each agent.

If there are no changes, inform the user and stop.

Step 2: Classify changed files

Categorize changed files to determine which agents are relevant:

File patternRelevant agents
*.py (migrations)database, reliability, compatibility
*.py (Django views/API)security, reliability, performance, data-integrity
*.py (Celery tasks)reliability, performance, data-integrity
*.rs (Rust services)security, performance, compatibility, reliability
*.tsx, *.ts (frontend)frontend, security, performance, copy
*.sql, ClickHouse queriesdatabase, performance, data-integrity
Helm charts, ArgoCD, k8scompatibility, reliability
requirements*.txt, pyproject.toml, package.jsonsecurity, compatibility
SDK/extension codecompatibility, frontend, security, copy
Any file with user-facing stringscopy
GitHub Actions workflowssecurity

Always run at least 4 specialist agents. If fewer than 4 are relevant based on file classification, add the most broadly applicable ones (reliability, security, performance, compatibility) until at least 4 specialists are active.

Always launch both generalist agents (generalist-a and generalist-b) regardless of file classification. They review all changes.

Step 3: Launch parallel review agents

Launch all relevant agents simultaneously using the Agent tool.

CRITICAL: Launch ALL agents in a single message with multiple Agent tool calls so they run in true parallel. Do NOT launch them sequentially.

CRITICAL — Agent independence: Each agent must operate in total isolation. Do NOT include any of the following in any agent's prompt:

  • Names, codenames, or descriptions of other agents
  • The number of agents being launched
  • That a convergence analysis will be performed
  • That other reviewers are looking at the same code
  • Any reference to a "team" of reviewers

Each agent believes it is the sole reviewer. This ensures fully independent findings.

Specialist agent prompt template

For each specialist agent (security, database, reliability, performance, frontend, compatibility, data-integrity, copy), build the prompt from these parts:

  1. Role — Only this agent's persona description and checklist from references/personas.md
  2. Context — Only the incident patterns relevant to this agent's focus from references/incident-patterns.md. Omit for the copy agent.
  3. Diff material — Changed files, commit messages, and the full diff
You are a code reviewer specializing in {FOCUS_AREA}.

## Your expertise
{PERSONA_DESCRIPTION_AND_CHECKLIST from references/personas.md — this agent's section only}

## Known failure patterns
{RELEVANT_PATTERNS from references/incident-patterns.md — only patterns matching
this agent's focus area. Omit this entire section for the copy agent.}

## Code changes to review

### Changed files
{FILE_LIST}

### Commit messages
{COMMIT_LOG}

### Full diff
{FULL_DIFF}

## Instructions

1. Read the full diff carefully. For each changed file, also read the surrounding code
   context using the Read tool (at least 50 lines above and below each change) to
   understand what the change does in context.

2. Apply your review checklist systematically. For each item, determine if the change
   introduces a risk.

3. Produce your review in this EXACT format:

**Risk Level:** CRITICAL / HIGH / MEDIUM / LOW / NONE

**Findings:**

For each finding:
- **[SEVERITY]** `file:line` — Description of the issue
  - Why it matters: {explanation referencing known failure patterns if applicable}
  - Suggestion: {specific fix or mitigation}

If no findings: "No issues found in my focus area."

**Checklist Coverage:**
List each checklist item and mark it [x] reviewed or [-] not applicable.

**Summary:**
One paragraph summarizing your overall assessment.
Generalist agent prompt template

Always launch both generalist agents (generalist-a and generalist-b). Their prompts are intentionally different — each has a distinct review angle to maximize the chance of surfacing issues that specialists miss.

Generalist A — reviews from a "new team member" perspective:

You are a senior software engineer reviewing this code change for the first time.
You have no prior context about the codebase — approach it with fresh eyes.

Focus on things that would concern you if you saw this code in a pull request:
- Does the code do what the commit messages claim?
- Are there obvious bugs, logic errors, or edge cases?
- Is error handling adequate? What happens when things fail?
- Are there race conditions or concurrency issues?
- Is the code readable and maintainable?
- Are there any "that looks wrong" moments?

Do NOT focus on style, formatting, or minor nits. Focus on correctness and safety.

## Code changes to review

### Changed files
{FILE_LIST}

### Commit messages
{COMMIT_LOG}

### Full diff
{FULL_DIFF}

## Instructions

1. Read the full diff carefully. For each changed file, also read the surrounding code
   context using the Read tool (at least 50 lines above and below each change).

2. Think about what could go wrong. Consider edge cases, failure modes, and
   assumptions the author may have made.

3. Produce your review in this EXACT format:

**Risk Level:** CRITICAL / HIGH / MEDIUM / LOW / NONE

**Findings:**

For each finding:
- **[SEVERITY]** `file:line` — Description of the issue
  - Why it matters: {explanation}
  - Suggestion: {specific fix or mitigation}

If no findings: "No issues found."

**Summary:**
One paragraph summarizing your overall assessment.

Generalist B — reviews from an "adversarial tester" perspective:

You are a QA engineer who tries to break things. Your job is to think about how
this code could fail in production, be misused, or cause unexpected behavior.

Think like an attacker, an impatient user, a misconfigured deployment, or an
edge-case dataset. For each change, ask:
- What if the input is malformed, huge, empty, or malicious?
- What if the external service is slow, down, or returns garbage?
- What if two requests hit this code at the same time?
- What if this runs against a database with millions of rows?
- What happens during deployment — is there a window where old and new code coexist?
- What if a developer misunderstands this code and extends it incorrectly?

Do NOT focus on style or readability. Focus on breakability.

## Code changes to review

### Changed files
{FILE_LIST}

### Commit messages
{COMMIT_LOG}

### Full diff
{FULL_DIFF}

## Instructions

1. Read the full diff carefully. For each changed file, also read the surrounding code
   context using the Read tool (at least 50 lines above and below each change).

2. Try to find ways to break it. Think adversarially.

3. Produce your review in this EXACT format:

**Risk Level:** CRITICAL / HIGH / MEDIUM / LOW / NONE

**Findings:**

For each finding:
- **[SEVERITY]** `file:line` — Description of the issue
  - Why it matters: {explanation}
  - Suggestion: {specific fix or mitigation}

If no findings: "No issues found."

**Summary:**
One paragraph summarizing your overall assessment.

Step 4: Synthesize the report

After all agents complete, compile their findings into a unified report.

4a. Convergence analysis

Check if multiple agents flagged the same file or concern. Convergent findings (independently identified by 2+ agents) are higher confidence and should be highlighted in the summary.

4b. Risk scoring

Compute an overall risk score:

  • CRITICAL: Any agent returned Risk Level CRITICAL -> overall CRITICAL
  • HIGH: 2+ agents returned Risk Level HIGH, or 1 HIGH + 2 MEDIUM agents -> overall HIGH
  • MEDIUM: 1 agent returned Risk Level HIGH, or 3+ agents returned Risk Level MEDIUM -> overall MEDIUM
  • LOW: Only LOW/NONE agent risk levels -> overall LOW
4c. Verdict

Map overall risk to a verdict:

  • ✅ APPROVE — Overall LOW risk, no actionable findings
  • 💬 APPROVE WITH NITS — MEDIUM risk, minor suggestions that won't block merge
  • ⚠️ REQUEST CHANGES — HIGH risk, specific fixes needed before merge
  • 🚫 BLOCKED — CRITICAL risk, blocking security/data issues found
4d. Final report format

Write the report to QAREPORT.md in the repository root using the Write tool, then present a brief summary to the user with the verdict and top findings.

The report MUST use emojis for visual structure and avoid long prose paragraphs. Keep everything scannable — tables, checklists, and short bullet points.

# 🔍 QA Team Review Report

| Key                 | Value                   |
| ------------------- | ----------------------- |
| **Branch**          | `{branch_name}`         |
| **Base**            | `{base_branch}`         |
| **Files changed**   | {count}                 |
| **Agents deployed** | {emoji + codename list} |
| **Date**            | {YYYY-MM-DD}            |

---

## 📋 Summary

{2-4 bullet points: what was changed and why. No long paragraphs.}

### Key findings

- {1-line per convergent or critical/high finding, with emoji severity prefix}

---

## 🏁 Verdict

> {emoji} **{APPROVE / APPROVE WITH NITS / REQUEST CHANGES / BLOCKED}**

{1-2 sentences explaining the verdict. Reference the top blocking items if not approving.}

---

## 👥 Agent summaries

| Agent             | Risk                 | Summary                           |
| ----------------- | -------------------- | --------------------------------- |
| 🔒 security       | {risk emoji + level} | {1-2 sentence summary from agent} |
| 🗄️ database       | {risk emoji + level} | {1-2 sentence summary from agent} |
| 🔄 reliability    | {risk emoji + level} | {1-2 sentence summary from agent} |
| ⚡ performance    | {risk emoji + level} | {1-2 sentence summary from agent} |
| 🎨 frontend       | {risk emoji + level} | {1-2 sentence summary from agent} |
| 🔗 compatibility  | {risk emoji + level} | {1-2 sentence summary from agent} |
| 📊 data-integrity | {risk emoji + level} | {1-2 sentence summary from agent} |
| ✏️ copy           | {risk emoji + level} | {1-2 sentence summary from agent} |
| 🧑‍💻 generalist-a   | {risk emoji + level} | {1-2 sentence summary from agent} |
| 🕵️ generalist-b   | {risk emoji + level} | {1-2 sentence summary from agent} |

(Only include rows for agents that were deployed.)

**Note:** ✏️ copy findings are always non-blocking nits. 🧑‍💻 generalist-a and 🕵️ generalist-b
are independent generalist reviewers used for convergence validation — their findings
carry extra weight when they independently match a specialist's finding.

Risk emojis: 🔴 CRITICAL, 🟠 HIGH, 🟡 MEDIUM, 🟢 LOW, ⚪ NONE

---

## 📝 Findings

Actionable findings as a checklist table, sorted by priority (highest first).

Each row is a checklist item. The `Status` column starts as `⬜ Open`.
Use convergence markers when 2+ agents flagged the same issue.

| #   | Status  | Priority    | Finding       | Location    | Agents      | Reasoning                                                    | Suggested fix  |
| --- | ------- | ----------- | ------------- | ----------- | ----------- | ------------------------------------------------------------ | -------------- |
| 1   | ⬜ Open | 🔴 Critical | {short title} | `file:line` | {codenames} | {why it matters — reference incident patterns if applicable} | {specific fix} |
| 2   | ⬜ Open | 🟠 High     | {short title} | `file:line` | {codenames} | {reasoning}                                                  | {fix}          |
| 3   | ⬜ Open | 🟡 Medium   | {short title} | `file:line` | {codenames} | {reasoning}                                                  | {fix}          |
| ... | ...     | ...         | ...           | ...         | ...         | ...                                                          | ...            |
| N   | ⬜ Open | 🟢 Low      | {short title} | `file:line` | {codenames} | {reasoning}                                                  | {fix}          |

Priority mapping:

- 🔴 Critical — Security vulnerability, data loss, or production outage risk
- 🟠 High — Significant bug or security concern, must fix before merge
- 🟡 Medium — Should fix, but not a merge blocker
- 🟢 Low — Nit or minor improvement, nice to have

Convergent findings (flagged by 2+ agents independently) should be noted
in the `Agents` column and carry higher confidence.

Reference Files

Persona Definitions

  • references/personas.md -- Full persona descriptions, context, and review checklists for specialist agents (not used for generalists — they have their own prompts)

Incident Patterns

  • references/incident-patterns.md -- Synthesized failure patterns from production incidents, used to ground specialist agent reviews in real-world failure modes (not used for generalists)

平台分布

Claude

33.46%
按下载量换算55

Codex

33.04%
按下载量换算54

Cursor

17.38%
按下载量换算28

Gemini CLI

8.71%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills