apify-content-analytics

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Content Analytics

内容分析

Track and analyze content performance using Apify Actors to extract engagement metrics from multiple platforms.

使用Apify Actors跟踪和分析内容表现，从多个平台提取互动指标。

Prerequisites

前提条件

(No need to check it upfront)

```
.env
```
file with
```
APIFY_TOKEN
```
Node.js 20.6+ (for native
```
--env-file
```
support)
```
mcpc
```
CLI tool:
```
npm install -g @apify/mcpc
```

（无需预先检查）

包含
```
APIFY_TOKEN
```
的
```
.env
```
文件
Node.js 20.6+（支持原生
```
--env-file
```
功能）
```
mcpc
```
CLI工具：
```
npm install -g @apify/mcpc
```

Workflow

工作流程

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Identify content analytics type (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the analytics script
- [ ] Step 5: Summarize findings

复制此清单并跟踪进度：

任务进度：
- [ ] 步骤1：确定内容分析类型（选择Actor）
- [ ] 步骤2：通过mcpc获取Actor架构
- [ ] 步骤3：询问用户偏好（格式、文件名）
- [ ] 步骤4：运行分析脚本
- [ ] 步骤5：总结分析结果

Step 1: Identify Content Analytics Type

步骤1：确定内容分析类型

Select the appropriate Actor based on analytics needs:

User Need	Actor ID	Best For
Post engagement metrics	`apify/instagram-post-scraper`	Post performance
Reel performance	`apify/instagram-reel-scraper`	Reel analytics
Follower growth tracking	`apify/instagram-followers-count-scraper`	Growth metrics
Comment engagement	`apify/instagram-comment-scraper`	Comment analysis
Hashtag performance	`apify/instagram-hashtag-scraper`	Branded hashtags
Mention tracking	`apify/instagram-tagged-scraper`	Tag tracking
Comprehensive metrics	`apify/instagram-scraper`	Full data
API-based analytics	`apify/instagram-api-scraper`	API access
Facebook post performance	`apify/facebook-posts-scraper`	Post metrics
Reaction analysis	`apify/facebook-likes-scraper`	Engagement types
Facebook Reels metrics	`apify/facebook-reels-scraper`	Reels performance
Ad performance tracking	`apify/facebook-ads-scraper`	Ad analytics
Facebook comment analysis	`apify/facebook-comments-scraper`	Comment engagement
Page performance audit	`apify/facebook-pages-scraper`	Page metrics
YouTube video metrics	`streamers/youtube-scraper`	Video performance
YouTube Shorts analytics	`streamers/youtube-shorts-scraper`	Shorts performance
TikTok content metrics	`clockworks/tiktok-scraper`	TikTok analytics

根据分析需求选择合适的Actor：

用户需求	Actor ID	适用场景
帖子互动指标	`apify/instagram-post-scraper`	帖子表现分析
Reel表现	`apify/instagram-reel-scraper`	Reel分析
粉丝增长跟踪	`apify/instagram-followers-count-scraper`	增长指标
评论互动	`apify/instagram-comment-scraper`	评论分析
话题标签表现	`apify/instagram-hashtag-scraper`	品牌话题标签
提及跟踪	`apify/instagram-tagged-scraper`	标签跟踪
综合指标	`apify/instagram-scraper`	完整数据
基于API的分析	`apify/instagram-api-scraper`	API访问
Facebook帖子表现	`apify/facebook-posts-scraper`	帖子指标
互动类型分析	`apify/facebook-likes-scraper`	互动类型
Facebook Reels指标	`apify/facebook-reels-scraper`	Reels表现
广告表现跟踪	`apify/facebook-ads-scraper`	广告分析
Facebook评论分析	`apify/facebook-comments-scraper`	评论互动
主页表现审计	`apify/facebook-pages-scraper`	主页指标
YouTube视频指标	`streamers/youtube-scraper`	视频表现
YouTube Shorts分析	`streamers/youtube-shorts-scraper`	Shorts表现
TikTok内容指标	`clockworks/tiktok-scraper`	TikTok分析

Step 2: Fetch Actor Schema

步骤2：获取Actor架构

Fetch the Actor's input schema and details dynamically using mcpc:

bash

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"

Replace

ACTOR_ID

with the selected Actor (e.g.,

apify/instagram-post-scraper

This returns:

Actor description and README
Required and optional input parameters
Output fields (if available)

使用mcpc动态获取Actor的输入架构和详细信息：

bash

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"

将

ACTOR_ID

替换为所选Actor（例如

apify/instagram-post-scraper

）。

此命令将返回：

Actor描述和README
必填和可选输入参数
输出字段（若可用）

Step 3: Ask User Preferences

步骤3：询问用户偏好

Before running, ask:

Output format:
- Quick answer - Display top few results in chat (no file saved)
- CSV - Full export with all fields
- JSON - Full export in JSON format
Number of results: Based on character of use case

运行前，询问以下内容：

输出格式：
- 快速回复 - 在聊天中显示前几条结果（不保存文件）
- CSV - 导出包含所有字段的完整数据
- JSON - 以JSON格式导出完整数据
结果数量：根据使用场景确定

Step 4: Run the Script

步骤4：运行脚本

Quick answer (display in chat, no file):

bash

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

CSV:

bash

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

JSON:

bash

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

快速回复（在聊天中显示，不生成文件）：

bash

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

CSV格式：

bash

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

JSON格式：

bash

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

Step 5: Summarize Findings

步骤5：总结分析结果

After completion, report:

Number of content pieces analyzed
File location and name
Key performance insights
Suggested next steps (deeper analysis, content optimization)

完成后，报告以下内容：

分析的内容数量
文件位置和名称
关键表现洞察
建议的后续步骤（深入分析、内容优化）

Error Handling

错误处理

APIFY_TOKEN not found

- Ask user to create

.env

with

APIFY_TOKEN=your_token

mcpc not found

- Ask user to install

npm install -g @apify/mcpc

Actor not found

- Check Actor ID spelling

Run FAILED

- Ask user to check Apify console link in error output

Timeout

- Reduce input size or increase

--timeout

APIFY_TOKEN not found

- 请用户创建包含

APIFY_TOKEN=your_token

的

.env

文件

mcpc not found

- 请用户安装

npm install -g @apify/mcpc

Actor not found

- 检查Actor ID的拼写

Run FAILED

- 请用户查看错误输出中的Apify控制台链接

Timeout

- 减少输入数据量或增加

--timeout

参数