instagram-research

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Original

English

🇨🇳

Translation

Chinese

Instagram Research

Instagram研究

Research high-performing Instagram posts and reels, identify outliers, and analyze top video content for hooks and structure.

研究表现优异的Instagram帖子和Reels，识别异常值内容，并分析顶级视频内容的钩子和结构。

Prerequisites

前置条件

```
APIFY_TOKEN
```
environment variable or in
```
.env
```
```
GEMINI_API_KEY
```
environment variable or in
```
.env
```
```
apify-client
```
and
```
google-genai
```
Python packages
Accounts configured in
```
.claude/context/instagram-accounts.md
```

Verify setup:

bash

python3 -c "
import os
try:
    from dotenv import load_dotenv
    load_dotenv()
except ImportError:
    pass
from apify_client import ApifyClient
from google import genai
assert os.environ.get('APIFY_TOKEN'), 'APIFY_TOKEN not set'
assert os.environ.get('GEMINI_API_KEY'), 'GEMINI_API_KEY not set'
" && echo "Prerequisites OK"

需设置
```
APIFY_TOKEN
```
环境变量或在
```
.env
```
文件中配置
需设置
```
GEMINI_API_KEY
```
环境变量或在
```
.env
```
文件中配置
需安装
```
apify-client
```
和
```
google-genai
```
Python包
需在
```
.claude/context/instagram-accounts.md
```
中配置好账号

验证设置：

bash

python3 -c "
import os
try:
    from dotenv import load_dotenv
    load_dotenv()
except ImportError:
    pass
from apify_client import ApifyClient
from google import genai
assert os.environ.get('APIFY_TOKEN'), 'APIFY_TOKEN not set'
assert os.environ.get('GEMINI_API_KEY'), 'GEMINI_API_KEY not set'
" && echo "Prerequisites OK"

Workflow

工作流程

1. Create Run Folder

1. 创建运行文件夹

bash

RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"

bash

RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"

2. Fetch Content

2. 获取内容

bash

python3 .claude/skills/instagram-research/scripts/fetch_instagram.py \
  --type reels \
  --days 30 \
  --limit 50 \
  --output {RUN_FOLDER}/raw.json

Parameters:

```
--type
```
: "posts", "reels", or "stories"
```
--days
```
: Days back to search (default: 30)
```
--limit
```
: Max items per account (default: 50)

bash

python3 .claude/skills/instagram-research/scripts/fetch_instagram.py \
  --type reels \
  --days 30 \
  --limit 50 \
  --output {RUN_FOLDER}/raw.json

参数说明：

```
--type
```
：可选值为"posts"、"reels"或"stories"
```
--days
```
：回溯搜索的天数（默认：30）
```
--limit
```
：每个账号最多获取的内容数量（默认：50）

3. Identify Outliers

3. 识别异常值内容

bash

python3 .claude/skills/instagram-research/scripts/analyze_posts.py \
  --input {RUN_FOLDER}/raw.json \
  --output {RUN_FOLDER}/outliers.json \
  --threshold 2.0

Output JSON contains:

```
total_posts
```
: Number of posts analyzed
```
outlier_count
```
: Number of outliers found
```
topics
```
: Top hashtags and keywords
```
accounts
```
: List of accounts analyzed
```
outliers
```
: Array of outlier posts with engagement metrics

bash

python3 .claude/skills/instagram-research/scripts/analyze_posts.py \
  --input {RUN_FOLDER}/raw.json \
  --output {RUN_FOLDER}/outliers.json \
  --threshold 2.0

输出的JSON包含：

```
total_posts
```
：分析的帖子总数
```
outlier_count
```
：找到的异常值数量
```
topics
```
：热门话题标签和关键词
```
accounts
```
：分析的账号列表
```
outliers
```
：包含互动数据的异常值帖子数组

4. Analyze Top Videos with AI

4. 用AI分析顶级视频

bash

python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py \
  --input {RUN_FOLDER}/outliers.json \
  --output {RUN_FOLDER}/video-analysis.json \
  --platform instagram \
  --max-videos 5

Extracts from each video:

Hook technique and replicable formula
Content structure and sections
Retention techniques
CTA strategy

See the

video-content-analyzer

skill for full output schema and hook/format types.

bash

python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py \
  --input {RUN_FOLDER}/outliers.json \
  --output {RUN_FOLDER}/video-analysis.json \
  --platform instagram \
  --max-videos 5

从每个视频中提取以下信息：

钩子技巧和可复制的公式
内容结构和板块
留存用户的技巧
CTA策略

查看

video-content-analyzer

技能获取完整的输出 schema 和钩子/格式类型。

5. Generate Report

5. 生成报告

Read

{RUN_FOLDER}/outliers.json

and

{RUN_FOLDER}/video-analysis.json

, then generate

{RUN_FOLDER}/report.md

Report Structure:

markdown

undefined

读取

{RUN_FOLDER}/outliers.json

和

{RUN_FOLDER}/video-analysis.json

，然后生成

{RUN_FOLDER}/report.md

。

报告结构：

markdown

undefined

Instagram Research Report

Instagram研究报告

Generated: {date}

生成日期：{date}

Top Performing Hooks

顶级表现钩子

Ranked by engagement. Use these formulas for your content.

按互动量排序。可将这些公式用于你的内容创作。

Hook 1: {technique} - @{username}

钩子1：{technique} - @{username}

Opening: "{opening_line}"
Why it works: {attention_grab}
Replicable Formula: {replicable_formula}
Engagement: {likes} likes, {comments} comments, {views} views
Watch Video

[Repeat for each analyzed video]

开场： "{opening_line}"
为何有效： {attention_grab}
可复制公式： {replicable_formula}
互动数据： {likes} 点赞，{comments} 评论，{views} 浏览量
观看视频

[为每个分析的视频重复上述内容]

Content Structure Patterns

内容结构模式

Video	Format	Pacing	Key Retention Techniques
@username	{format}	{pacing}	{techniques}

视频	格式	节奏	核心留存技巧
@username	{format}	{pacing}	{techniques}

CTA Strategies

CTA策略

Video	CTA Type	CTA Text	Placement
@username	{type}	"{cta_text}"	{placement}

视频	CTA类型	CTA文案	位置
@username	{type}	"{cta_text}"	{placement}

All Outliers

所有异常值内容

Rank	Username	Likes	Comments	Views	Engagement Rate
[List all outliers with metrics and links]

排名	用户名	点赞	评论	浏览量	互动率
[列出所有带数据和链接的异常值内容]

Actionable Takeaways

可落地的行动建议

[Synthesize patterns into 4-6 specific recommendations]

[将模式整合为4-6条具体的建议]

Accounts Analyzed

分析的账号列表

[List accounts]


Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent.

[列出所有分析的账号]


重点关注可落地的洞察。包含可复制公式的"顶级表现钩子"部分应突出显示。

Quick Reference

快速参考

Full pipeline:

bash

RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && \
python3 .claude/skills/instagram-research/scripts/fetch_instagram.py --type reels -o "$RUN_FOLDER/raw.json" && \
python3 .claude/skills/instagram-research/scripts/analyze_posts.py -i "$RUN_FOLDER/raw.json" -o "$RUN_FOLDER/outliers.json" && \
python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py -i "$RUN_FOLDER/outliers.json" -o "$RUN_FOLDER/video-analysis.json" -p instagram

Then read both JSON files and generate the report.

完整流程：

bash

RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && \
python3 .claude/skills/instagram-research/scripts/fetch_instagram.py --type reels -o "$RUN_FOLDER/raw.json" && \
python3 .claude/skills/instagram-research/scripts/analyze_posts.py -i "$RUN_FOLDER/raw.json" -o "$RUN_FOLDER/outliers.json" && \
python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py -i "$RUN_FOLDER/outliers.json" -o "$RUN_FOLDER/video-analysis.json" -p instagram

然后读取两个JSON文件并生成报告。

Engagement Metrics

互动数据指标

Engagement Score:

likes + (3 × comments) + (0.1 × views)

Outlier Detection: Posts with engagement rate > mean + (threshold × std_dev)

Engagement Rate: (score / followers) × 100

互动得分：

点赞 + (3 × 评论) + (0.1 × 浏览量)

异常值检测：互动率 > 平均值 + (阈值 × 标准差)

互动率：(得分 / 粉丝数) × 100

instagram-research

Original

Translation

Instagram Research

Instagram研究

Prerequisites

前置条件

Workflow

工作流程

1. Create Run Folder

1. 创建运行文件夹

2. Fetch Content

2. 获取内容

3. Identify Outliers

3. 识别异常值内容

4. Analyze Top Videos with AI

4. 用AI分析顶级视频

5. Generate Report

5. 生成报告

Instagram Research Report

Instagram研究报告

Top Performing Hooks

顶级表现钩子

Hook 1: {technique} - @{username}

钩子1：{technique} - @{username}

Content Structure Patterns

内容结构模式

CTA Strategies

CTA策略

All Outliers

所有异常值内容

Trending Topics

热门话题

Top Hashtags

顶级话题标签

Top Keywords

顶级关键词

Actionable Takeaways

可落地的行动建议

Accounts Analyzed

分析的账号列表

Quick Reference

快速参考

Engagement Metrics

互动数据指标