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qa-team质量保证小组

Agent Skill

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。

总安装

466

周安装

20

GitHub Stars

34,189

下载量

163
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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