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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,权限为需要联网;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 55,572。

总安装 474周安装 19GitHub Stars 40下载 154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:personalization-at-scale(大规模个性化)
来源仓库:https://github.com/manojbajaj95/gtm-skills
仓库路径:skills/personalization-at-scale
安装命令:
npx skills add https://github.com/manojbajaj95/gtm-skills --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。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/manojbajaj95/gtm-skills --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。

大规模个性化技能用于动态内容生成与用户分群。personalization-at-scale 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适用于研究检索场景,支持行为数据与偏好建模。通过 GitHub 安装后可在 Codex、Claude 等工具中调用。需确保数据处理符合隐私法规,避免存储敏感信息。

建议查阅原始文档了解实时渲染延迟与缓存策略。

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/manojbajaj95/gtm-skills --skill personalizatio…;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

personalization-at-scale 怎么安装?

在终端执行 npx skills add https://github.com/manojbajaj95/gtm-skills --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。

personalization-at-scale 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。

personalization-at-scale 用的人多吗?

公开数据是累计安装 474 次,近一周安装 19 次,下载 154 次,GitHub 40 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。

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
personalization-at-scale

大规模个性化 · 本页

47440需要联网未明确有提醒

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

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

For each prospect, produce:

  1. Prospect details — Name, title, company, LinkedIn URL
  2. Personalization found — Type, source, date, context
  3. 3 first line options — Direct, Question, Insight styles
  4. Full email example — Subject + body using selected first line
  5. Confidence score — High / Medium / Low with reasoning

Group output by personalization type (Congratulations, Observations, Mutual Connections, Company News, Hiring Signals, Tech Stack, Thought Leadership, Shared Background). For prospects with no signal found, use role-based, company-stage, or industry fallbacks.

See references/output-template.md for the full example output format with sample first lines per type.

🎯 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

Codex

36.61%
按下载量换算56

Claude

29.52%
按下载量换算45

Cursor

18.09%
按下载量换算28

Gemini CLI

7.81%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。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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