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Agent Skill · 效率

viral-video-analysis(病毒式视频分析)

用于辅助视频生成、动画合成、脚本化剪辑或 Remotion 等视频项目开发。它适合让 Agent 组织镜头、生成素材说明、维护合成代码或排查渲染问题。使用时需要确认分辨率、时长、素材路径和导出格式;涉及外部素材、人物肖像或商业发布时,应先核对版权授权和内容审核要求。

viral-video-analysis 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 1,551。

总安装 40,689周安装 1,646GitHub Stars 公开资料未说明下载 12,773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-0
版本
1.0.1

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-video-analysis(病毒式视频分析)
来源仓库:https://github.com/shawnshenopeninterx/viral-video-analysis
安装命令:
openclaw skills install viral-video-analysis
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install viral-video-analysis

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

分析视频广告表现并提供创作者改进反馈。识别低播放率、完播率低等问题的潜在原因。适合短视频团队复盘与迭代内容质量时使用。需上传视频文件或链接以启动分析流程。viral-video-analysis 属于效率类 Skill,可作为该场景下的辅助能力补充。建议结合多维度指标综合评估而非单一结论。

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 OpenClaw;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 openclaw skills install viral-video-analysis;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

    它需要接触敏感数据,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

viral-video-analysis 怎么安装?

在终端执行 openclaw skills install viral-video-analysis。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

viral-video-analysis 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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-video-analysis 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

viral-video-analysis 是免费的吗?

来源标注的许可证是 MIT-0。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-video-analysis 用的人多吗?

公开数据是累计安装 40,689 次,近一周安装 1,646 次,下载 12,773 次。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-video-analysis 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
viral-video-analysis

病毒式视频分析 · 本页

40,689—接触敏感数据MIT-0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

name
viral-video-analysis
description
Analyze video ad performance and provide actionable feedback to creators. Use when asked to analyze why videos underperform, give creator coaching feedback, compare high vs low ROI content, or identify video structure issues. Combines audio transcripts, visual analysis, and performance metrics. Supports YouTube, TikTok, Instagram Reels, and Twitter.
metadata
{"openclaw": {"requires": {"env": ["MEMORIES_API_KEY"]}, "primaryEnv": "MEMORIES_API_KEY", "homepage": "https://api-tools.memories.ai"}}

Requirements

  • API Key: Requires MEMORIES_API_KEY from Memories.ai
  • External API: Sends video URLs to https://mavi-backend.memories.ai for transcription
  • Python packages: generate_report.py will auto-install fpdf2, pandas, openpyxl if missing

Privacy Note

  • Video URLs are sent to Memories.ai for transcription
  • Batch analysis reads Excel files with creator/ROI data
  • Review Memories.ai privacy policy before use

Viral Video Analysis

Analyze videos and provide actionable feedback for creators.

Core Insight

High ROI videos: <100 words, ~5s per product, visual-first + background music Low ROI videos: >150 words, >15s per product, too much explaining

The core problem: Creators spend too much time "selling" instead of "showing". Remember: Ads reach non-followers who need to be hooked in 3 seconds.

Quantitative Thresholds

Metric✅ GOOD (High ROI)❌ BAD (Low ROI)
Word Count<100 words>150 words
Time per Product~5 seconds>15 seconds
Shows All Products UpfrontYESNO
FormatVisual + MusicTalking/Explaining

Analysis Workflow

Setup

Requires Memories.ai API key. Get one at https://api-tools.memories.ai

Set environment variable:

export MEMORIES_API_KEY="sk-mavi-your-key-here"

1. Get Audio Transcript (Word Count)

import os
import requests

BASE_URL = "https://mavi-backend.memories.ai/serve/api/v2"
API_KEY = os.environ.get("MEMORIES_API_KEY")
HEADERS = {"Authorization": API_KEY}

def get_transcript(url: str, platform: str = "instagram"):
    resp = requests.post(
        f"{BASE_URL}/{platform}/video/transcript",
        headers=HEADERS,
        json={"video_url": url, "channel": "rapid"},
        timeout=60
    )
    data = resp.json()
    if data.get("success"):
        text = data["data"]["transcripts"][0]["text"]
        return {"text": text, "word_count": len(text.split())}
    return {"error": data.get("msg")}

# Platform detection
def detect_platform(url):
    url = url.lower()
    if "tiktok" in url: return "tiktok"
    if "instagram" in url: return "instagram"
    if "twitter" in url or "x.com" in url: return "twitter"
    return "youtube"

2. Analyze Against Thresholds

def analyze_video(url):
    platform = detect_platform(url)
    result = get_transcript(url, platform)
    
    if "error" in result:
        return result
    
    word_count = result["word_count"]
    
    return {
        "url": url,
        "word_count": word_count,
        "word_count_status": "GOOD" if word_count < 100 else "OK" if word_count < 150 else "BAD",
        "issues": [],
        "transcript_preview": result["text"][:200]
    }

3. Generate Creator Feedback

Based on analysis, provide specific feedback:

If word_count > 150:

"Your video has {X} words. Top performers use <100 words. Try replacing verbal explanations with visual demonstrations - stretch the fabric, spin around, show the fit."

If pace is slow (>15s per product):

"You're spending ~{X} seconds per product. High-performers show each item in ~5 seconds. Try quick cuts - one outfit = one scene transition."

If no upfront overview:

"Show ALL products in the first 2-3 seconds. Let viewers see the full haul immediately - it sets expectations and keeps them watching."

Always remind:

"Remember: Ads reach people who DON'T follow you. You have 3 seconds to grab a stranger's attention - don't waste it on intros."

The Exception: Kirstin Approach

Detailed verbal reviews CAN work if:

  1. Show all products FIRST before explaining
  2. Use low-pressure language: "if it doesn't fit, just return it"
  3. Focus on introducing products, not "selling" them

Word count: 373 words can still perform if structure is right.

Reference Videos

GOOD Examples (share with creators)

  • instagram.com/reel/Cy1zs4gLGFG - 46 words, 15s for 3 outfits, pure visual
  • instagram.com/reel/DEybxPbNeOl - 56 words, quick showcase, background music
  • instagram.com/reel/DHHr5o2s1LG - 91 words, fast cuts, shows product features
  • instagram.com/reel/DBd6NxbOeBb - 91 words, demonstrates fit visually

EXCEPTION Example (detailed review done RIGHT)

  • instagram.com/reel/DCQJ355RWSE - 373 words but works: shows all upfront, low-pressure

BAD Example (avoid)

  • instagram.com/reel/DRCdjLlDcla - 168 words, 30s per outfit, too much explaining

Feedback Template

Hi [Creator],

Thanks for your video! Here's some feedback to help improve performance:

**What's Working:**
- [Specific positive]

**Opportunities:**
1. **Pacing**: Currently ~{X}s per product. Try ~5s per item with quick cuts.
2. **Word Count**: {X} words detected. Top performers use <100. Show more, tell less.
3. **Opening**: Consider showing all products in first 2-3 seconds.

**Key Reminder:**
Ads reach people who don't follow you yet. They need to be hooked in 3 seconds!

**Reference Videos:**
[Link to good example]

Best,
[Team]

Batch Analysis

def analyze_batch(excel_path, sample_size=20):
    import pandas as pd
    df = pd.read_excel(excel_path)
    df.columns = [c.lower().replace('sum of ', '').replace(' ', '_') for c in df.columns]
    
    # Get top and bottom performers
    top = df.nlargest(sample_size // 2, 'roi')
    bottom = df.nsmallest(sample_size // 2, 'roi')
    
    results = []
    for _, row in pd.concat([top, bottom]).iterrows():
        url = row.get('video_url') or row.get('row_labels')
        analysis = analyze_video(url)
        analysis['roi'] = row['roi']
        analysis['tier'] = 'TOP' if row['roi'] > 1.0 else 'BOTTOM'
        results.append(analysis)
    
    return results

Quick Commands

  • "Analyze this video: [url]" → Word count + feedback
  • "Why is this video underperforming?" → Detailed analysis
  • "Give me feedback for [creator]" → Coaching template
  • "Compare these videos" → Side-by-side analysis
  • "Analyze my performance data" → Batch analysis from Excel

平台分布

OpenClaw

80.79%
按下载量换算10,319

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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