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instagram-research

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English
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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

VideoFormatPacingKey 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

VideoCTA TypeCTA TextPlacement
@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

RankUsernameLikesCommentsViewsEngagement 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]

Trending Topics

热门话题

Top Hashtags

顶级话题标签

[From outliers.json topics.hashtags]
[来自outliers.json的topics.hashtags]

Top Keywords

顶级关键词

[From outliers.json topics.keywords]
[来自outliers.json的topics.keywords]

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