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Agent Skill · 研究检索

viral-content-predictor(病毒内容预测器)

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

viral-content-predictor 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 46,384。

总安装 783周安装 32GitHub Stars 3下载 251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:viral-content-predictor(病毒内容预测器)
来源仓库:https://github.com/drshailesh88/integrated_content_os
仓库路径:skills/viral-content-predictor
安装命令:
npx skills add https://github.com/drshailesh88/integrated_content_os --skill viral-content-predictor
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 viral-content-predictor

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。

用于辅助文档、README、Markdown 和内容稿件的整理与改写。适合提炼结构、补齐章节、统一术语或检查链接。保留项目已有事实和路径,不写成确定结论。涉及对外文案时需控制语气,避免过度营销。安装前建议确认权限范围和维护状态。

viral-content-predictor 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/viral-content-predictor

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/drshailesh88/integrated_content_os --skill vir…;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

viral-content-predictor 怎么安装?

在终端执行 npx skills add https://github.com/drshailesh88/integrated_content_os --skill viral-content…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

viral-content-predictor 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。

viral-content-predictor 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。

viral-content-predictor 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。

viral-content-predictor 用的人多吗?

公开数据是累计安装 783 次,近一周安装 32 次,下载 251 次,GitHub 3 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。

viral-content-predictor 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
viral-content-predictor

病毒内容预测器 · 本页

7833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

Viral Content Predictor for Medical Education

This skill analyzes healthcare/medical education content ideas and predicts their viral potential using multi-factor analysis, trend research, and YouTube audience insights.

Core Capabilities

  1. Content Idea Analysis: Extract and score content ideas from uploaded documents
  2. Viral Potential Prediction: Estimate views, engagement, and AVD based on multiple factors
  3. Trend Research: Identify hot topics and emerging trends in medical education
  4. Audience Intelligence: Analyze YouTube comments to understand knowledge gaps and concerns
  5. Content Optimization: Provide subtopics, myths to address, and structural recommendations

Workflow

Phase 1: Content Extraction & Initial Scoring

When the user provides PDF/DOCX files with content ideas:

  1. Extract all content ideas from the document
  2. Initial categorization by topic, complexity, and format
  3. Preliminary viral score (0-100) based on:

- Topic relevance and timeliness - Emotional appeal (fear, hope, relief, empowerment) - Searchability and SEO potential - Educational value vs entertainment balance - Novelty factor

Phase 2: Deep Research & Validation

For top-scoring ideas (score >70) or user-selected ideas:

  1. Search current trends: Use web_search to find:

- Recent high-performing videos on the topic - News articles and medical publications - Reddit/forum discussions - Trending searches related to the topic

  1. Competitive analysis:

- Identify top-performing videos in the niche - Analyze view counts, engagement ratios, and video length - Note common patterns and differentiators

  1. Knowledge gap identification:

- What questions are people asking? - What misconceptions exist? - What information is missing from existing content?

Phase 3: Predictive Analytics

For each analyzed idea, calculate:

  1. Predicted View Range: Based on:

- Search volume data (estimated from trends) - Similar video performance benchmarks - Topic saturation level - Seasonal/temporal relevance - Channel authority factor (assumed moderate for interventional cardiology niche)

  1. Engagement Prediction:

- Estimated likes, shares, comments - Expected like-to-view ratio - Share potential score

  1. AVD (Average View Duration) Optimization Score:

- Topic retention potential (inherent interest) - Complexity level (optimal: moderate complexity for patient education) - Hook strength assessment - Pacing recommendations

Phase 4: Content Blueprint

For prioritized ideas, provide:

  1. Video Structure Recommendation:

- Optimal video length - Hook suggestions (first 10 seconds) - Chapter breakdown with timestamps - Pacing guidance for high retention

  1. Subtopics to Include (in priority order):

- Core information (must-have) - High-interest tangents (AVD boosters) - Myth-busting segments (engagement drivers) - Practical takeaways (satisfaction & shareability)

  1. Psychological Triggers to Address:

- Common fears related to the topic - Misconceptions to debunk - Hope/empowerment angles - Trust-building elements

  1. SEO & Discoverability:

- Title suggestions (tested patterns) - Thumbnail concepts - Keyword recommendations - Description template

Scoring Methodology

Viral Potential Score (0-100)

Topic Factors (40 points):

  • Search demand: 15 pts (estimated from trend data)
  • Emotional resonance: 10 pts (fear, hope, curiosity)
  • Timeliness: 10 pts (recent news, seasonal relevance)
  • Novelty: 5 pts (unique angle or new information)

Engagement Factors (30 points):

  • Shareability: 10 pts (will people send to family/friends?)
  • Comment-worthiness: 10 pts (controversial or discussion-inducing?)
  • Practical value: 10 pts (actionable information)

Retention Factors (30 points):

  • Hook potential: 10 pts (compelling opening)
  • Information density: 10 pts (value per minute)
  • Narrative flow: 10 pts (story or logical progression)

View Prediction Formula

Estimated Views = Base_Audience × Topic_Multiplier × Quality_Factor × Trend_Factor

Where:
- Base_Audience: 5,000-15,000 (typical for established medical education channel)
- Topic_Multiplier: 0.5-10.0 (based on search volume and competition)
- Quality_Factor: 0.8-1.5 (based on production quality, assumed 1.0)
- Trend_Factor: 0.5-3.0 (based on current trending status)

Range Output:
- Minimum (conservative): Lower quartile estimate
- Expected (median): Most likely scenario
- Maximum (optimistic): Upper quartile with viral potential

Research Tools & Techniques

Web Search Strategies

When researching topics, use these search patterns:

  1. Trend identification:

- "[topic] latest research 2024" - "most common questions about [topic]" - "[topic] myths debunked"

  1. Audience analysis:

- "reddit [topic] patient experience" - "[topic] what to expect forum" - "[topic] success stories"

  1. Competition analysis:

- "[topic] youtube popular" - "how to explain [topic] to patients" - "[topic] doctor explains"

YouTube Comment Analysis Strategy

When the user provides a topic or video URL:

  1. Search for top 5-10 videos on the topic
  2. Analyze comment patterns for:

- Most frequently asked questions - Common confusions or misconceptions - Emotional reactions (fear, gratitude, skepticism) - Requests for specific information - Demographic clues (age, situation)

  1. Categorize insights into:

- Knowledge gaps: What people don't understand - Fears: What worries them - Desires: What they hope to learn - Trust signals: What builds credibility

Output Format

Content Idea Report

For each analyzed idea, provide:

## [Content Idea Title]

### 🎯 Viral Potential Score: [X/100]

**Predicted Performance**:
- Views: [min - expected - max]
- Like Ratio: [X%]
- AVD: [X:XX - Y:YY minutes]
- Shareability: [Low/Medium/High]

### 📊 Analysis

**Strengths**:
- [Key strength 1]
- [Key strength 2]

**Opportunities**:
- [Improvement area 1]
- [Improvement area 2]

**Market Insights**:
- Current search trends: [summary]
- Competition level: [Low/Medium/High]
- Audience demand: [description]

### 🎬 Content Blueprint

**Optimal Length**: [X-Y minutes]

**Video Structure**:
1. Hook (0:00-0:10): [specific suggestion]
2. Problem Setup (0:10-1:00): [what to cover]
3. Core Education (1:00-[X]:00): [main content]
4. Myth-Busting ([X]:00-[Y]:00): [misconceptions to address]
5. Practical Takeaways ([Y]:00-end): [actionable advice]

**Essential Subtopics** (in order of priority):
1. [Subtopic 1] - [why it matters for AVD]
2. [Subtopic 2] - [why it matters for AVD]
3. [Subtopic 3] - [why it matters for AVD]

**Knowledge Gaps to Address**:
- [Gap 1] - [source: YouTube comments/Reddit/forums]
- [Gap 2] - [source]

**Myths & Misconceptions**:
- [Myth 1] - [prevalence & why it persists]
- [Myth 2] - [prevalence & why it persists]

**Emotional Hooks**:
- Fear to address: [specific patient fear]
- Hope to provide: [specific positive outcome]
- Empowerment angle: [how viewers take control]

**SEO Recommendations**:
- Primary keyword: [keyword]
- Title suggestions:
  1. [Title option 1]
  2. [Title option 2]
  3. [Title option 3]
- Thumbnail concept: [description]

### 🔥 Hot Take / Unique Angle

[One compelling angle that differentiates this from existing content]

Trend Report

When analyzing current trends:

## 🚀 Trending Topics in [Niche]

### High Priority (Create ASAP)
1. **[Topic]** - Viral Score: [X/100]
   - Why now: [reason for timeliness]
   - Quick summary: [one-liner]

### Medium Priority (Plan for Next Month)
[Similar format]

### Emerging Trends (Watch Closely)
[Similar format]

### Seasonal Opportunities
[Upcoming events/seasons that create content opportunities]

Best Practices

For Medical Education Content

  1. Balance authority with accessibility: Use simple language but demonstrate expertise
  2. Lead with empathy: Acknowledge fears and concerns first
  3. Provide hope: Always include positive outcomes or management strategies
  4. Be specific: Concrete examples outperform abstractions
  5. Use visual analogies: Help patients visualize complex concepts
  6. Address "why": Explain mechanisms, not just recommendations
  7. Anticipate objections: Address common pushback or skepticism
  8. Include patient stories: Anonymized cases increase retention
  9. End with empowerment: Clear next steps or takeaways

AVD Optimization Tactics

  1. Pattern interrupt every 60-90 seconds: Change visual, topic, or energy
  2. Open loops: Tease information that comes later
  3. Progress indicators: "Three things you need to know..."
  4. Highlight surprising facts: "Most people don't know..."
  5. Use conversational pacing: Speak as if to one person
  6. Strategic repetition: Reinforce key points without being boring
  7. Maintain momentum: Cut dead air and unnecessary transitions

Reference Files

  • references/medical-content-patterns.md: Analysis of high-performing medical YouTube content patterns
  • references/cardiology-keywords.md: SEO-optimized keywords for cardiology topics
  • references/avd-tactics.md: Advanced retention strategies specific to educational content

When to Use Multiple Research Iterations

For content ideas scoring 85+:

  1. Run initial analysis
  2. Conduct deep competitive research
  3. Search for recent medical publications
  4. Analyze comment sections of top 5 competing videos
  5. Check Reddit/forums for patient perspectives
  6. Synthesize into comprehensive blueprint

This ensures the highest-potential ideas get the deepest analysis.

平台分布

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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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