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social-media-analyzer社交媒体分析器

Agent Skill

social-media-analyzer 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

196

周安装

8

GitHub Stars

3

下载量

63
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:social-media-analyzer(社交媒体分析器)
来源仓库:https://github.com/questnova502/claude-skills-sync
仓库路径:skills/social-media-analyzer
安装命令:
npx skills add https://github.com/questnova502/claude-skills-sync --skill social-media-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/questnova502/claude-skills-sync --skill social-media-analyzer

简介

social-media-analyzer 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 它可结合来源仓库、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。
  • 适用于 AI 工具类任务,支持多宿主环境集成。
  • 可通过 npx skills add 命令从 GitHub 仓库安装。

SKILL.md

Social Media Campaign Analyzer

This skill provides comprehensive analysis of social media campaign performance, helping marketing agencies deliver actionable insights to clients.

Capabilities

  • Multi-Platform Analysis: Track performance across Facebook, Instagram, Twitter, LinkedIn, TikTok
  • Engagement Metrics: Calculate engagement rate, reach, impressions, click-through rate
  • ROI Analysis: Measure cost per engagement, cost per click, return on ad spend
  • Audience Insights: Analyze demographics, peak engagement times, content performance
  • Trend Detection: Identify high-performing content types and posting patterns
  • Competitive Benchmarking: Compare performance against industry standards

Input Requirements

Campaign data including:

  • Platform metrics: Likes, comments, shares, saves, clicks
  • Reach data: Impressions, unique reach, follower growth
  • Cost data: Ad spend, campaign budget (for ROI calculations)
  • Content details: Post type (image, video, carousel), posting time, hashtags
  • Time period: Date range for analysis

Formats accepted:

  • JSON with structured campaign data
  • CSV exports from social media platforms
  • Text descriptions of key metrics

Output Formats

Results include:

  • Performance dashboard: Key metrics with trends
  • Engagement analysis: Best and worst performing posts
  • ROI breakdown: Cost efficiency metrics
  • Audience insights: Demographics and behavior patterns
  • Recommendations: Data-driven suggestions for optimization
  • Visual reports: Charts and graphs (Excel/PDF format)

How to Use

"Analyze this Facebook campaign data and calculate engagement metrics" "What's the ROI on this Instagram ad campaign with $500 spend and 2,000 clicks?" "Compare performance across all social platforms for the last month"

Scripts

  • calculate_metrics.py: Core calculation engine for all social media metrics
  • analyze_performance.py: Performance analysis and recommendation generation

Best Practices

  1. Ensure data completeness before analysis (missing metrics affect accuracy)
  2. Compare metrics within same time periods for fair comparisons
  3. Consider platform-specific benchmarks (Instagram engagement differs from LinkedIn)
  4. Account for organic vs. paid metrics separately
  5. Track metrics over time to identify trends
  6. Include context (seasonality, campaigns, events) when interpreting results

Limitations

  • Requires accurate data from social media platforms
  • Industry benchmarks are general guidelines and vary by niche
  • Historical data doesn't guarantee future performance
  • Organic reach calculations may vary by platform algorithm changes
  • Cannot access data directly from platforms (requires manual export or API integration)
  • Some platforms limit data availability (e.g., TikTok analytics for business accounts only)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

28.32%
按下载量换算18

windsurf

21.29%
按下载量换算13

trae

17.56%
按下载量换算11

OpenCode

11.14%
按下载量换算7

Codex

8.17%
按下载量换算5

Antigravity

3.81%
按下载量换算2

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。

来源信息

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