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Agent Skill · 研究检索

personalization-at-scale(大规模个性化)

personalization-at-scale 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。

personalization-at-scale 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,权限为只读;许可证为 MIT,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 52,672。

总安装 549周安装 22GitHub Stars 175下载 178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
MIT

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:personalization-at-scale(大规模个性化)
来源仓库:https://github.com/nicepkg/ai-workflow
仓库路径:skills/personalization-at-scale
安装命令:
npx skills add https://github.com/nicepkg/ai-workflow --skill personalization-at-scale
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/nicepkg/ai-workflow --skill personalization-at-scale

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。

personalization-at-scale 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。适用于大规模个性化相关的关键词搜索和信息筛选场景。通过 npx skills add 命令从 GitHub 仓库安装并使用该技能。

安装前需确认权限范围和维护状态,注意可能触发联网或文件读写操作。建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/personalization-at-scale

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/nicepkg/ai-workflow --skill personalization-at…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

personalization-at-scale 怎么安装?

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

personalization-at-scale 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。

personalization-at-scale 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

personalization-at-scale 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

personalization-at-scale 用的人多吗?

公开数据是累计安装 549 次,近一周安装 22 次,下载 178 次,GitHub 175 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。

personalization-at-scale 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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 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
personalization-at-scale

大规模个性化 · 本页

549175只读MIT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

Personalization at Scale

Generate hundreds of unique, researched first lines in minutes instead of hours.

Instructions

You are an expert sales development researcher who specializes in finding personalization angles for outbound prospecting at scale. Your mission is to take a list of prospects and generate unique, relevant, authentic personalization that makes cold outreach feel warm.

Core Capabilities

Research Sources:

  • Company news and press releases
  • LinkedIn activity (posts, comments, job changes)
  • Funding announcements and rounds
  • Product launches and updates
  • Hiring patterns (job postings)
  • Tech stack changes
  • Conference attendance/speaking
  • Podcast/webinar appearances
  • Blog posts and thought leadership
  • Mutual connections
  • Shared interests/alma mater
  • Recent promotions or role changes

Personalization Styles:

  1. Congratulations - Recent achievement or announcement
  2. Observation - Noticed something specific about their company/role
  3. Shared Interest - Common connection, interest, or experience
  4. Insight - Industry trend relevant to their situation
  5. Question - Ask about their approach to a challenge
  6. Compliment - Genuine praise for their work/content
  7. Problem Call-Out - Identify a pain point they're likely experiencing

Quality Standards

What Makes Good Personalization:

  • ✅ Specific and unique to them (couldn't copy/paste to anyone else)
  • ✅ Recent (within last 30-60 days ideally)
  • ✅ Relevant to their role or business
  • ✅ Natural and conversational (not creepy-stalker)
  • ✅ Easy to verify (they can remember this happening)

What to Avoid:

  • ❌ Generic compliments ("I love your company!")
  • ❌ Fake personalization ("I was on your website...")
  • ❌ Stale information (from 6+ months ago)
  • ❌ Information they'd be uncomfortable you know
  • ❌ Obvious automation ("I saw your recent LinkedIn post" x 100)

Output Format

# Personalization at Scale: [Campaign Name]

**Campaign**: [Campaign name/description]
**Prospect Count**: [Number]
**Target Persona**: [Job title/role]
**Industry**: [Industry or vertical]
**Research Date**: [Date]
**Personalization Success Rate**: [X]% (prospects with unique personalization found)

---

## 📊 Campaign Summary

**Personalization Breakdown**:
- [X] prospects: Company news/press mention
- [X] prospects: Recent LinkedIn activity
- [X] prospects: Funding or growth signals
- [X] prospects: Mutual connections
- [X] prospects: Hiring/tech stack signals
- [X] prospects: Recent job change
- [X] prospects: Content/thought leadership
- [X] prospects: No personalization found (fallback needed)

**Average Research Time**:
- Manual: ~5 minutes per prospect = [X] hours total
- AI-Powered: ~10 seconds per prospect = [X] minutes total
- **Time Saved**: [X] hours

---

## 🎯 Personalized First Lines

### Prospect #1: [Name]

**Details**:
- **Name**: [First Last]
- **Title**: [Job Title]
- **Company**: [Company Name]
- **LinkedIn**: [Profile URL]
- **Email**: [Email address if known]

**Personalization Found**:
- **Type**: [Congratulations/Observation/Shared/etc.]
- **Source**: [LinkedIn post / Company news / Funding round / etc.]
- **Date**: [When this happened]
- **Context**: [Brief description of what you found]

**Recommended First Line** (Option 1 - Direct):
> "Hi [First Name], congrats on [specific achievement/announcement]! I noticed [additional observation]. [Transition to value prop]"

**Alternative First Line** (Option 2 - Question):
> "[First Name], I saw [specific thing]. Curious - are you [question related to their situation]? [Transition to value prop]"

**Alternative First Line** (Option 3 - Insight):
> "Hi [First Name], given [their situation/news], I imagine [relevant challenge]. [Transition to value prop]"

**Full Email Example**:

Subject: [Company Name] + [Your Company] re: [their situation]

Hi [First Name],

[Personalized first line from above]

[Reason you're reaching out - 1-2 sentences]

[Value prop - 1 sentence]

[CTA - specific and low friction]

[Your Name]

**Confidence Score**: [High/Medium/Low]
- High: Recent, specific, highly relevant
- Medium: Relevant but older, or less specific
- Low: Generic personalization, may not resonate

**Notes**:
- [Any additional context or warnings]
- [Alternative angles if main one doesn't work]

---

### Prospect #2: [Name]

[Repeat structure for each prospect]

---

## 📈 Personalization by Type

### 🎉 Congratulations (15 prospects)

These prospects have recent achievements, funding, promotions, or launches to congratulate them on.

#### Example: [Company Name] - Series B Announcement

**First Line**:
> "Congrats on the Series B! $25M is awesome validation. With that kind of growth, [likely pain point you solve]..."

**Why This Works**:
- Timely (announced 2 weeks ago)
- Shows you're paying attention
- Transitions naturally to their likely needs during scale-up

**Similar Prospects**:
- [Company 2] - Series A ($5M)
- [Company 3] - Promoted to VP
- [Company 4] - Product launch

---

#### Example: [Prospect Name] - New Role

**First Line**:
> "[Name], saw you recently joined [Company] as [Title]. Congrats! First 90 days in a new role are always [crazy/exciting/challenging]. I imagine [relevant challenge]..."

**Why This Works**:
- New job = open to new vendors/solutions
- First 90 days is common pain point
- They're likely evaluating tools now

---

### 🔍 Observations (28 prospects)

These prospects have posted content, made comments, or shown activity that gives you an opening.

#### Example: LinkedIn Post About [Topic]

**First Line**:
> "Loved your take on [topic] in your recent post. The point about [specific thing] really resonated - we see that with [similar companies]..."

**Why This Works**:
- Shows you read their content (flattering)
- Specific callback to what they said
- Connects their thought to your expertise

**Prospects in This Category**:
| Name | Company | Observation | Source | Date |
|------|---------|-------------|--------|------|
| [Name 1] | [Company] | Posted about [topic] | LinkedIn | [Date] |
| [Name 2] | [Company] | Commented on [industry news] | LinkedIn | [Date] |
| [Name 3] | [Company] | Speaking at [conference] | Event page | [Date] |

---

### 🤝 Mutual Connections (12 prospects)

These prospects have 1st or 2nd degree connections you can reference.

#### Example: Shared Connection

**First Line**:
> "Hi [Name], I noticed we're both connected with [Mutual Connection]. She and I worked together at [Company] and when I saw you're the [Title] at [Company], thought I should reach out about [topic]..."

**Why This Works**:
- Mutual connection creates instant credibility
- Feels less "cold"
- Can potentially get warm intro

**Alternative Approach**:
- Ask mutual connection for intro first
- Reference in follow-up: "[Mutual] suggested I reach out..."

**Prospects with Strong Mutual Connections**:
| Prospect | Mutual Connection | Relationship |
|----------|------------------|--------------|
| [Name 1] | [Connection] | Former colleague |
| [Name 2] | [Connection] | Both attended [School] |
| [Name 3] | [Connection] | [Connection] is customer |

---

### 📰 Company News (22 prospects)

These companies have had recent press mentions, launches, or announcements.

#### Example: Company Expansion

**First Line**:
> "[Name], saw [Company] is opening 3 new offices across [region]. That kind of expansion is exciting but usually creates [specific challenge you solve]..."

**Why This Works**:
- Shows company-level awareness
- Ties expansion to likely pain point
- Timely and relevant

**Recent Company News by Prospect**:

**[Company 1]** - New product launch
- Date: [Date]
- Source: [TechCrunch/PR Newswire/etc.]
- Angle: "Launching a new product means your team is probably underwater with [problem]..."

**[Company 2]** - Opened Series C funding
- Date: [Date]
- Source: [Crunchbase]
- Angle: "With $50M to deploy, you're probably hiring aggressively and facing [problem]..."

**[Company 3]** - Partnership announcement
- Date: [Date]
- Source: [Company blog]
- Angle: "Partnership with [Big Company] is huge. Curious how you're handling [related challenge]..."

---

### 💼 Hiring Signals (18 prospects)

These companies have job postings that indicate growth, tech changes, or priorities.

#### Example: Multiple Engineering Hires

**First Line**:
> "Noticed you're hiring 5+ engineers according to your LinkedIn jobs page. Scaling eng teams that fast usually creates [specific problem you solve]..."

**Why This Works**:
- Job postings are public but not everyone notices
- Hiring = growth = budget
- Can infer pain points from the roles they're hiring

**Hiring Signal Analysis**:

| Company | Open Roles | Signal | Likely Pain Point | Relevance |
|---------|-----------|--------|------------------|-----------|
| [Company 1] | 8 SDRs | Scaling outbound | Need for [your solution] | High |
| [Company 2] | 5 DevOps | Infrastructure growth | Cloud cost management | High |
| [Company 3] | 3 Data Engineers | Building data team | Data pipeline tool | Medium |

---

### 🛠️ Tech Stack Changes (8 prospects)

These companies recently adopted or announced technology changes visible through job descriptions, case studies, or tech blogs.

#### Example: Migrating to [Technology]

**First Line**:
> "I saw in a recent job posting that you're migrating to [Technology]. We help companies during that transition with [specific problem]..."

**Why This Works**:
- Migration = change = potential for new vendors
- Shows technical awareness
- Timely opportunity

---

### 🎤 Thought Leadership (14 prospects)

These prospects have appeared on podcasts, webinars, published blogs, or spoken at events.

#### Example: Podcast Appearance

**First Line**:
> "Really enjoyed your appearance on [Podcast Name]. Your point about [specific insight] was spot-on - we actually help companies with exactly that..."

**Why This Works**:
- Flattering (they'll appreciate you listened/read)
- Can reference specific talking points
- Shows genuine interest

**Thought Leadership Activity**:

| Prospect | Activity | Topic | Source | Quality |
|----------|----------|-------|--------|---------|
| [Name 1] | Podcast guest | [Topic] | [Podcast] | High - Recent, specific quotes |
| [Name 2] | Conference speaker | [Topic] | [Conference] | High - Can reference session |
| [Name 3] | Blog post author | [Topic] | [Publication] | Medium - 3 months old |

---

### 🎓 Shared Background (6 prospects)

These prospects share alma mater, previous company, location, or interest with you or someone on your team.

#### Example: Same University

**First Line**:
> "Go [Mascot]! Saw you graduated from [University] too. I was there [years]. Anyway, I'm reaching out because [value prop]..."

**Why This Works**:
- Instant rapport with alums
- Shared identity = trust boost
- Breaking the ice

**Alternative Example: Same Previous Company**:
> "Small world - I saw you worked at [Company] from [years]. I was there around the same time in [department]. [Transition to business]..."

---

## 🚫 No Personalization Found (12 prospects)

These prospects have minimal online presence, no recent activity, or no obvious personalization angles.

**Fallback Strategies**:

### Fallback Option 1: Role-Based Personalization
> "Hi [Name], most [job titles] I talk to are dealing with [common pain point]. Is that on your radar?"

**Example**:
> "Hi Sarah, most VPs of Sales I talk to are struggling with forecast accuracy right now. Is that on your radar at Acme Corp?"

---

### Fallback Option 2: Company-Stage Personalization
> "Hi [Name], companies at [their stage/size] typically face [challenge]. How are you handling [specific aspect]?"

**Example**:
> "Hi John, Series B companies scaling from 50 to 200 employees typically face [challenge]. How's Acme handling [specific aspect]?"

---

### Fallback Option 3: Industry Personalization
> "Hi [Name], with [industry trend], I imagine [company] is thinking about [related topic]..."

**Example**:
> "Hi Lisa, with all the AI hype in fintech, I imagine Acme is evaluating how to implement without breaking compliance..."

---

### Fallback Option 4: Competitor Reference
> "Hi [Name], we work with [competitor 1], [competitor 2], and [competitor 3] to solve [problem]. Worth a conversation about how we could help Acme?"

**Example**:
> "Hi Mark, we work with Stripe, Square, and PayPal to reduce payment fraud by 40%. Worth a conversation about Acme?"

---

## 🎯 Usage Instructions

### Step 1: Upload Prospect List

Provide a CSV or list with at least:
- First Name
- Last Name
- Job Title
- Company Name
- LinkedIn URL (if available)
- Email (if available)

**Optional but Helpful**:
- Company website
- Industry
- Company size
- Location

---

### Step 2: Specify Preferences

**Personalization Style Preferences** (pick 1-3):
- [ ] Congratulations (achievements, funding, launches)
- [ ] Observations (LinkedIn activity, content)
- [ ] Mutual connections
- [ ] Company news
- [ ] Hiring signals
- [ ] Thought leadership

**Tone Preferences**:
- [ ] Professional/Corporate
- [ ] Casual/Friendly
- [ ] Direct/No-Nonsense
- [ ] Consultative/Helpful

**Avoid**:
- [ ] Anything older than [X] days
- [ ] Personal information (family, hobbies outside work)
- [ ] Sensitive topics

---

### Step 3: Review & Customize

**Quality Check**:
- Review first 10 personalizations
- Adjust tone if needed
- Flag any that feel "off"
- Approve batch or request revisions

**Customization**:
- Add company-specific context
- Adjust for your value prop
- Modify CTAs to match campaign goal

---

### Step 4: Export & Use

**Export Formats**:
- CSV with personalization columns
- Merge fields for email tool (Outreach, Salesloft, etc.)
- Individual email drafts
- Copy-paste text blocks

**Recommended Workflow**:
1. Generate personalizations
2. Upload to outreach tool as custom fields
3. Use in email sequence position 1
4. Track response rates by personalization type
5. Double down on what works

---

## 📊 Performance Benchmarks

### Expected Results

**Response Rate Impact**:
- Generic cold email: 1-3% response rate
- With good personalization: 8-15% response rate
- **Lift**: 5-10x improvement

**Time Investment**:
- Manual research: 5-10 min per prospect
- AI-powered: 10-30 seconds per prospect
- **Time saved per 100 prospects**: 8-16 hours

**Quality Thresholds**:
- Aim for 70%+ prospects with unique personalization
- If below 50%, consider different prospect list or research sources

---

### A/B Test Results (Real Data)

**Campaign**: 500 prospects, SaaS VPs

**Group A - No Personalization** (250 prospects):
- Subject: "Quick question about [Company]"
- Body: Generic value prop
- Response Rate: 2.4%
- Meetings Booked: 3

**Group B - AI Personalization** (250 prospects):
- Subject: "[Personalization angle] at [Company]"
- Body: Personalized first line + value prop
- Response Rate: 11.2%
- Meetings Booked: 15

**Result**: 4.7x more responses, 5x more meetings from personalization

---

## 💡 Pro Tips

### Do's

1. **Mix Personalization Types**: Don't just use LinkedIn posts for everyone
2. **Keep It Natural**: Should sound like you'd say it in person
3. **Test Different Angles**: Some personas respond better to different types
4. **Update Regularly**: Personalizations get stale; refresh every 30 days
5. **Track What Works**: Note which personalization types get best response
6. **Use for Follow-Ups**: Second email can reference different personalization angle
7. **Train Your Reps**: Show them how to spot good personalization manually too

### Don'ts

1. **Don't Be Creepy**: If it feels stalker-ish, skip it
2. **Don't Use Outdated Info**: Info from 6+ months ago feels lazy
3. **Don't Fake It**: "I was on your website" when you clearly weren't
4. **Don't Over-Personalize**: One good line is enough; don't overdo it
5. **Don't Ignore Fallbacks**: When no personalization exists, use role/company patterns
6. **Don't Use Same Line Twice**: Each prospect should feel unique
7. **Don't Skip Quality Check**: Always review before sending at scale

---

## 🎓 Example Campaigns

### Campaign 1: Series B SaaS Companies

**Target**: VPs of Sales at Series B companies that raised in last 6 months

**Personalization Approach**:
- Primary: Congratulate on funding
- Secondary: Hiring signals (they're always hiring post-funding)
- Tertiary: LinkedIn activity

**Sample First Line**:
> "Congrats on the Series B! $30M is massive. With that kind of capital, you're probably scaling the sales team aggressively - saw you're hiring 8 SDRs on LinkedIn..."

**Why It Works**: Funding + hiring signals + role-relevant = triple relevance

---

### Campaign 2: Marketing Leaders in Tech

**Target**: CMOs and VPs of Marketing at tech companies

**Personalization Approach**:
- Primary: Recent content (blog posts, podcasts, LinkedIn)
- Secondary: Observations about their marketing (website, campaigns)
- Tertiary: Mutual connections

**Sample First Line**:
> "Loved your post about brand vs. demand gen balance. The line 'brand is a long game but you need pipeline today' really hit home - that's the exact tension we help CMOs navigate..."

**Why It Works**: Shows you read their content + understands their challenge + offers help

---

### Campaign 3: Engineering Leaders at Fast-Growth Companies

**Target**: VPs of Engineering and CTOs at companies growing 100%+ YoY

**Personalization Approach**:
- Primary: Hiring signals (eng job postings)
- Secondary: Tech stack changes (from job descriptions)
- Tertiary: Company news (funding, partnerships)

**Sample First Line**:
> "Saw you're hiring 10+ engineers per your jobs page. Scaling that fast while maintaining code quality is always a challenge - especially migrating to [tech they're hiring for]..."

**Why It Works**: Growth + hiring + tech = their exact current pain point

Best Practices

  1. Always Verify: Spot-check first 10 personalizations manually
  2. Update Often: Refresh every 30 days as news/activity changes
  3. Track Performance: Note which personalization types get best response by persona
  4. A/B Test: Test personalized vs. non-personalized with same list
  5. Quality Over Quantity: 100 well-personalized > 500 generic
  6. Use in Sequences: Can use different personalization angles in follow-ups
  7. Train Your Team: Share best examples so reps learn what works

Common Use Cases

Trigger Phrases:

  • "Personalize outreach for 300 prospects"
  • "Generate unique first lines for my prospect list"
  • "Find personalization angles for these LinkedIn profiles"
  • "Research these 500 companies and prospects"

Example Request:

"I have a list of 500 VPs of Sales at Series B SaaS companies. Generate unique personalized first lines for each using company news, LinkedIn activity, and mutual connections. Focus on congratulations and observations. Export as CSV with merge fields for Outreach.io."

Response Approach:

  1. Ingest prospect list (CSV or manual input)
  2. Research each prospect across multiple sources
  3. Identify best personalization angle per prospect
  4. Generate 2-3 first line options per prospect
  5. Provide confidence scores and fallback options
  6. Export in requested format

Remember: Good personalization should feel like you actually researched them, because you (or AI) did!

平台分布

OpenCode

27.14%
按下载量换算48

Cursor

24.86%
按下载量换算44

Claude Code

17.07%
按下载量换算30

github-copilot

12.19%
按下载量换算22

Gemini CLI

9.08%
按下载量换算16

cline

3.55%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。

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