social-media-analyzer by alirezarezvani/claude-skills
npx skills add https://github.com/alirezarezvani/claude-skills --skill social-media-analyzer通过互动指标、投资回报率计算和平台基准进行广告活动效果分析。
分析社交媒体广告活动效果:
| 字段 | 必需 | 描述 |
|---|---|---|
| platform | 是 | instagram, facebook, twitter, linkedin, tiktok |
| posts[] | 是 | 帖子数据数组 |
| posts[].likes | 是 | 点赞/反应数量 |
| posts[].comments |
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
| 是 |
| 评论数量 |
| posts[].reach | 是 | 触达的独立用户数 |
| posts[].impressions | 否 | 总浏览次数 |
| posts[].shares | 否 | 分享/转发数量 |
| posts[].saves | 否 | 收藏/书签数量 |
| posts[].clicks | 否 | 链接点击次数 |
| total_spend | 否 | 广告支出(用于计算投资回报率) |
分析前,请验证:
Engagement Rate = (Likes + Comments + Shares + Saves) / Reach × 100
| 指标 | 公式 | 解释 |
|---|---|---|
| Engagement Rate | Engagements / Reach × 100 | 受众互动水平 |
| CTR | Clicks / Impressions × 100 | 内容点击吸引力 |
| Reach Rate | Reach / Followers × 100 | 内容分发情况 |
| Virality Rate | Shares / Impressions × 100 | 值得分享的程度 |
| Save Rate | Saves / Reach × 100 | 内容价值 |
| 评级 | 互动率 | 行动 |
|---|---|---|
| Excellent | > 6% | 扩大规模和复制 |
| Good | 3-6% | 优化和扩展 |
| Average | 1-3% | 测试改进 |
| Poor | < 1% | 分析并调整方向 |
计算广告支出回报率:
| 指标 | 公式 |
|---|---|
| Cost Per Engagement (CPE) | Total Spend / Total Engagements |
| Cost Per Click (CPC) | Total Spend / Total Clicks |
| Cost Per Thousand (CPM) | (Spend / Impressions) × 1000 |
| Return on Ad Spend (ROAS) | Revenue / Ad Spend |
| 行动 | 价值 | 依据 |
|---|---|---|
| Like | $0.50 | 品牌知名度 |
| Comment | $2.00 | 主动互动 |
| Share | $5.00 | 放大效应 |
| Save | $3.00 | 意向信号 |
| Click | $1.50 | 流量价值 |
| 投资回报率 % | 评级 | 建议 |
|---|
500% | Excellent | 大幅增加预算 200-500% | Good | 适度增加预算 100-200% | Acceptable | 优化后再扩大规模 0-100% | Break-even | 审查定位和创意 < 0% | Negative | 暂停并重组
| 平台 | 平均值 | 良好 | 优秀 |
|---|---|---|---|
| 1.22% | 3-6% | >6% | |
| 0.07% | 0.5-1% | >1% | |
| Twitter/X | 0.05% | 0.1-0.5% | >0.5% |
| 2.0% | 3-5% | >5% | |
| TikTok | 5.96% | 8-15% | >15% |
| 平台 | 平均值 | 良好 | 优秀 |
|---|---|---|---|
| 0.22% | 0.5-1% | >1% | |
| 0.90% | 1.5-2.5% | >2.5% | |
| 0.44% | 1-2% | >2% | |
| TikTok | 0.30% | 0.5-1% | >1% |
| 平台 | 平均值 | 良好 |
|---|---|---|
| $0.97 | <$0.50 | |
| $1.20 | <$0.70 | |
| $5.26 | <$3.00 | |
| TikTok | $1.00 | <$0.50 |
完整基准数据请参见 references/platform-benchmarks.md。
python scripts/calculate_metrics.py assets/sample_input.json
计算每条帖子和广告活动总计的互动率、点击率、触达率。
python scripts/analyze_performance.py assets/sample_input.json
生成包含投资回报率、基准和建议的完整表现分析。
输出包括:
参见 assets/sample_input.json:
{
"platform": "instagram",
"total_spend": 500,
"posts": [
{
"post_id": "post_001",
"content_type": "image",
"likes": 342,
"comments": 28,
"shares": 15,
"saves": 45,
"reach": 5200,
"impressions": 8500,
"clicks": 120
}
]
}
参见 assets/expected_output.json:
{
"campaign_metrics": {
"total_engagements": 1521,
"avg_engagement_rate": 8.36,
"ctr": 1.55
},
"roi_metrics": {
"total_spend": 500.0,
"cost_per_engagement": 0.33,
"roi_percentage": 660.5
},
"insights": {
"overall_health": "excellent",
"benchmark_comparison": {
"engagement_status": "excellent",
"engagement_benchmark": "1.22%",
"engagement_actual": "8.36%"
}
}
}
示例广告活动显示:
references/platform-benchmarks.md 包含:
| 当您要求... | 您将获得... |
|---|---|
| "社交媒体审计" | 跨平台的绩效分析与基准比较 |
| "什么内容表现好?" | 包含模式和推荐的最佳内容分析 |
| "竞争对手社交媒体分析" | 包含差距分析的竞争性社交媒体比较 |
所有输出均通过质量验证:
每周安装次数
386
代码仓库
GitHub 星标数
6.5K
首次出现
Jan 20, 2026
安全审计
安装于
opencode302
gemini-cli286
claude-code274
codex268
cursor260
github-copilot248
Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.
Analyze social media campaign performance:
| Field | Required | Description |
|---|---|---|
| platform | Yes | instagram, facebook, twitter, linkedin, tiktok |
| posts[] | Yes | Array of post data |
| posts[].likes | Yes | Like/reaction count |
| posts[].comments | Yes | Comment count |
| posts[].reach | Yes | Unique users reached |
| posts[].impressions | No | Total views |
| posts[].shares | No | Share/retweet count |
| posts[].saves | No | Save/bookmark count |
| posts[].clicks | No | Link clicks |
| total_spend | No | Ad spend (for ROI) |
Before analysis, verify:
Engagement Rate = (Likes + Comments + Shares + Saves) / Reach × 100
| Metric | Formula | Interpretation |
|---|---|---|
| Engagement Rate | Engagements / Reach × 100 | Audience interaction level |
| CTR | Clicks / Impressions × 100 | Content click appeal |
| Reach Rate | Reach / Followers × 100 | Content distribution |
| Virality Rate | Shares / Impressions × 100 | Share-worthiness |
| Save Rate | Saves / Reach × 100 | Content value |
| Rating | Engagement Rate | Action |
|---|---|---|
| Excellent | > 6% | Scale and replicate |
| Good | 3-6% | Optimize and expand |
| Average | 1-3% | Test improvements |
| Poor | < 1% | Analyze and pivot |
Calculate return on ad spend:
| Metric | Formula |
|---|---|
| Cost Per Engagement (CPE) | Total Spend / Total Engagements |
| Cost Per Click (CPC) | Total Spend / Total Clicks |
| Cost Per Thousand (CPM) | (Spend / Impressions) × 1000 |
| Return on Ad Spend (ROAS) | Revenue / Ad Spend |
| Action | Value | Rationale |
|---|---|---|
| Like | $0.50 | Brand awareness |
| Comment | $2.00 | Active engagement |
| Share | $5.00 | Amplification |
| Save | $3.00 | Intent signal |
| Click | $1.50 | Traffic value |
| ROI % | Rating | Recommendation |
|---|
500% | Excellent | Scale budget significantly
200-500% | Good | Increase budget moderately
100-200% | Acceptable | Optimize before scaling
0-100% | Break-even | Review targeting and creative
< 0% | Negative | Pause and restructure
| Platform | Average | Good | Excellent |
|---|---|---|---|
| 1.22% | 3-6% | >6% | |
| 0.07% | 0.5-1% | >1% | |
| Twitter/X | 0.05% | 0.1-0.5% | >0.5% |
| 2.0% | 3-5% | >5% | |
| TikTok | 5.96% | 8-15% | >15% |
| Platform | Average | Good | Excellent |
|---|---|---|---|
| 0.22% | 0.5-1% | >1% | |
| 0.90% | 1.5-2.5% | >2.5% | |
| 0.44% | 1-2% | >2% | |
| TikTok | 0.30% | 0.5-1% | >1% |
| Platform | Average | Good |
|---|---|---|
| $0.97 | <$0.50 | |
| $1.20 | <$0.70 | |
| $5.26 | <$3.00 | |
| TikTok | $1.00 | <$0.50 |
See references/platform-benchmarks.md for complete benchmark data.
python scripts/calculate_metrics.py assets/sample_input.json
Calculates engagement rate, CTR, reach rate for each post and campaign totals.
python scripts/analyze_performance.py assets/sample_input.json
Generates full performance analysis with ROI, benchmarks, and recommendations.
Output includes:
See assets/sample_input.json:
{
"platform": "instagram",
"total_spend": 500,
"posts": [
{
"post_id": "post_001",
"content_type": "image",
"likes": 342,
"comments": 28,
"shares": 15,
"saves": 45,
"reach": 5200,
"impressions": 8500,
"clicks": 120
}
]
}
See assets/expected_output.json:
{
"campaign_metrics": {
"total_engagements": 1521,
"avg_engagement_rate": 8.36,
"ctr": 1.55
},
"roi_metrics": {
"total_spend": 500.0,
"cost_per_engagement": 0.33,
"roi_percentage": 660.5
},
"insights": {
"overall_health": "excellent",
"benchmark_comparison": {
"engagement_status": "excellent",
"engagement_benchmark": "1.22%",
"engagement_actual": "8.36%"
}
}
}
The sample campaign shows:
references/platform-benchmarks.md contains:
| When you ask for... | You get... |
|---|---|
| "Social media audit" | Performance analysis across platforms with benchmarks |
| "What's performing?" | Top content analysis with patterns and recommendations |
| "Competitor social analysis" | Competitive social media comparison with gaps |
All output passes quality verification:
Weekly Installs
386
Repository
GitHub Stars
6.5K
First Seen
Jan 20, 2026
Security Audits
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Installed on
opencode302
gemini-cli286
claude-code274
codex268
cursor260
github-copilot248
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