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influencer-analyzer影响者分析器

Agent Skill

influencer-analyzer 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

897

周安装

37

GitHub Stars

3

下载量

293
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

3

许可证

MIT

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:influencer-analyzer(影响者分析器)
来源仓库:https://github.com/drshailesh88/integrated_content_os
仓库路径:skills/influencer-analyzer
安装命令:
npx skills add https://github.com/drshailesh88/integrated_content_os --skill influencer-analyzer
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/drshailesh88/integrated_content_os --skill influencer-analyzer

简介

influencer-analyzer 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。

  • 适用于社交媒体影响者识别、受众分析和营销效果评估场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 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。
  • 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。

SKILL.md

Influencer Analyzer

Know what's working, find where to differentiate. This skill tracks cardiology content creators and identifies opportunities for your content.


WHAT IT DOES

StepActionOutput
1Find influencer content via Perplexity/DuckDuckGoURLs, articles, videos
2Scrape and extract content patternsTopics, formats, frequency
3Analyze engagement signalsWhat resonates with audience
4Generate gap analysisWhere you can differentiate

TRIGGERS

Use this skill when you say:

  • "What is [Topol/Attia/competitor] posting about?"
  • "Find gaps in cardiology content"
  • "Analyze my competition"
  • "What topics should I cover?"
  • "Track cardiology influencers"

TARGET INFLUENCERS

International (English)

NamePlatformFocusWhy Track
@EricTopolTwitter, SubstackTrials, digital healthVoice model, Ground Truths style
Peter AttiaPodcast, YouTubeLongevity, CVD preventionDeep-dive style
York CardiologyYouTubePatient educationClear explanations
Dr. Sanjay Gupta (York)YouTubeECG, clinical casesEducational format

Indian (Hindi/English)

NamePlatformFocusWhy Track
Dr Navin AgrawalYouTubePatient educationCompetition
Cardiac Second OpinionYouTubeSecond opinionsCompetition
Dr. Devi ShettyVideosAffordable careAuthority

Anti-Patterns (What NOT to do)

NamePlatformWhy Track
SAAOLYouTubeMisinformation to counter
Dr Biswaroop Roy ChowdhuryYouTubeDangerous claims to debunk

USAGE

In Claude Code (Recommended)

"Analyze what Eric Topol is posting about this week"

"Find gaps between Topol's content and Indian cardiology YouTube"

"What cardiology topics are trending that I haven't covered?"

"Compare my content strategy with Peter Attia"

CLI Mode

# Analyze single influencer
python scripts/analyze_influencer.py --name "Eric Topol" --platform twitter

# Compare multiple influencers
python scripts/analyze_influencer.py --compare "Topol,Attia,York Cardiology"

# Find content gaps
python scripts/analyze_influencer.py --gaps --domain "Cardiology"

# Track specific topic
python scripts/analyze_influencer.py --topic "GLP-1" --influencers "Topol,Attia"

OUTPUT FORMATS

1. Influencer Profile

## Eric Topol (@EricTopol)

**Recent Focus (Last 30 days):**
- Clinical trials: 45%
- Digital health/AI: 30%
- COVID updates: 15%
- Book promotion: 10%

**Top Performing Topics:**
1. REDUCE-IT controversy (high engagement)
2. Apple Watch AFib detection (viral)
3. AI in diagnosis (consistent interest)

**Posting Patterns:**
- Frequency: 5-10 tweets/day
- Best times: 6AM, 12PM, 6PM PST
- Thread usage: Weekly deep-dives

**Style Notes:**
- Links to primary sources (PubMed, NEJM)
- Quotes key statistics
- Engages with critics
- Retweets junior researchers

2. Gap Analysis Report

## CONTENT GAP ANALYSIS

**What Topol Covers That You Don't:**
- [ ] Weekly trial breakdowns
- [ ] Digital health intersection
- [ ] International guideline comparisons

**What You Cover That Topol Doesn't:**
- [x] Hinglish explanations
- [x] Indian patient context
- [x] Cost-conscious alternatives
- [x] Cultural nuances (vegetarian diets, family dynamics)

**OPPORTUNITY ZONES:**
1. **Translate English trials for Indian context**
   - Topol covers REDUCE-IT → You explain what it means for Indian patients

2. **Bridge the gap**
   - International guidelines → Indian applicability

3. **Underserved topics in English space**
   - Rheumatic heart disease (rare topic in US)
   - Tropical cardiology
   - Resource-limited settings

3. Competitive Comparison Table

| Aspect | Eric Topol | Peter Attia | York Cardiology | You |
|--------|------------|-------------|-----------------|-----|
| Platform | Twitter/Substack | Podcast/YouTube | YouTube | YouTube |
| Language | English | English | English | Hinglish |
| Depth | Expert-level | Deep-dive | Patient-friendly | Expert→Patient |
| Frequency | Daily | Weekly | 2-3x/week | ? |
| Unique Angle | Trials/Digital | Longevity | ECG teaching | Indian context |

INTEGRATION WITH YOUR SYSTEM

Feeds Into:

  • research-engine/data/target_channels.json - Channel tracking
  • youtube-script-master - Topic selection
  • viral-content-predictor - Content scoring
  • content-repurposer - Multi-platform adaptation

Data Flow:

influencer-analyzer
       ↓
[Gap Analysis Report]
       ↓
research-engine (topic prioritization)
       ↓
youtube-script-master (script creation)
       ↓
YOUR CONTENT (unique angle)

HOW CLAUDE SHOULD USE THIS SKILL

When the user asks about competitors or content strategy:

Step 1: Identify Target

User: "What is Topol posting about?"
→ Target: Eric Topol
→ Platforms: Twitter, Substack

Step 2: Research with Perplexity

Use Perplexity MCP or web search to find:

  • Recent posts/articles
  • Engagement metrics
  • Topic distribution

Step 3: Analyze Patterns

  • What topics repeat?
  • What gets most engagement?
  • What's the posting frequency?

Step 4: Generate Gap Analysis

Compare with user's existing content:

  • What's covered vs. uncovered?
  • Where can user differentiate?
  • What's the unique angle?

Step 5: Actionable Recommendations

  • Specific topics to cover
  • Formats to try
  • Timing suggestions

SAMPLE WORKFLOW

User: "Find content gaps in cardiology YouTube"

Claude:
1. Uses Perplexity to search:
   - "Eric Topol recent tweets cardiology 2025"
   - "Peter Attia podcast topics 2025"
   - "York Cardiology recent videos"
   - "Indian cardiology YouTube channels"

2. Analyzes results:
   - Topic frequency
   - Engagement patterns
   - Content gaps

3. Cross-references with user's content:
   - What has user covered?
   - What's missing?
   - What's unique to user?

4. Outputs:
   - Gap analysis report
   - Priority topics list
   - Differentiation strategy

DEPENDENCIES

# Already have
anthropic>=0.18.0
python-dotenv>=1.0.0
rich>=13.0.0

# For web scraping (optional)
requests>=2.31.0
beautifulsoup4>=4.12.0

API KEYS NEEDED

KeyPurposeStatus
PERPLEXITY_API_KEYWeb searchAlready have (via OpenRouter)
ANTHROPIC_API_KEYAnalysisAlready have

PRE-CONFIGURED INFLUENCER PROFILES

Located in data/influencers.json:

{
  "influencers": [
    {
      "name": "Eric Topol",
      "handle": "@EricTopol",
      "platforms": ["twitter", "substack"],
      "focus": ["clinical_trials", "digital_health", "AI_medicine"],
      "style": "expert_commentary",
      "track_for": "voice_model"
    },
    {
      "name": "Peter Attia",
      "handle": "peterattiamd",
      "platforms": ["podcast", "youtube", "newsletter"],
      "focus": ["longevity", "metabolic_health", "CVD_prevention"],
      "style": "deep_dive",
      "track_for": "format_inspiration"
    },
    {
      "name": "York Cardiology",
      "handle": "@YorkCardiology",
      "platforms": ["youtube"],
      "focus": ["ECG", "patient_education", "clinical_cases"],
      "style": "educational",
      "track_for": "competitor"
    },
    {
      "name": "Dr Navin Agrawal",
      "handle": null,
      "platforms": ["youtube"],
      "focus": ["patient_education", "hindi"],
      "style": "simple_explanations",
      "track_for": "competitor"
    }
  ]
}

NOTES

  • Privacy: Only analyze public content
  • Frequency: Run weekly for trend tracking
  • Focus: Gap analysis, not copying
  • Goal: Find YOUR unique angle, not imitate others

*This skill helps you understand the competitive landscape so you can differentiate, not duplicate.*

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

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 Code

26.67%
按下载量换算78

OpenCode

24.24%
按下载量换算71

Antigravity

20.28%
按下载量换算59

Gemini CLI

12.96%
按下载量换算38

windsurf

8.06%
按下载量换算24

Codex

3.17%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

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

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来源信息

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