instagram-research by bradautomates/head-of-content
npx skills add https://github.com/bradautomates/head-of-content --skill instagram-research研究高表现的 Instagram 帖子和 Reels,识别异常值,并分析热门视频内容的钩子和结构。
APIFY_TOKEN 或在 .env 文件中GEMINI_API_KEY 或在 .env 文件中apify-client 和 google-genai.claude/context/instagram-accounts.md 中配置的账户验证设置:
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"
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RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"
python3 .claude/skills/instagram-research/scripts/fetch_instagram.py \
--type reels \
--days 30 \
--limit 50 \
--output {RUN_FOLDER}/raw.json
参数:
--type: "posts"、"reels" 或 "stories"--days: 回溯搜索的天数(默认:30)--limit: 每个账户的最大项目数(默认:50)python3 .claude/skills/instagram-research/scripts/analyze_posts.py \
--input {RUN_FOLDER}/raw.json \
--output {RUN_FOLDER}/outliers.json \
--threshold 2.0
输出 JSON 包含:
total_posts: 分析的帖子数量outlier_count: 发现的异常值数量topics: 热门话题标签和关键词accounts: 分析的账户列表outliers: 包含互动指标的异常值帖子数组python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py \
--input {RUN_FOLDER}/outliers.json \
--output {RUN_FOLDER}/video-analysis.json \
--platform instagram \
--max-videos 5
从每个视频中提取:
完整的输出模式和钩子/格式类型,请参阅 video-content-analyzer 技能。
读取 {RUN_FOLDER}/outliers.json 和 {RUN_FOLDER}/video-analysis.json,然后生成 {RUN_FOLDER}/report.md。
报告结构:
# Instagram 研究报告
生成时间:{date}
## 表现最佳的钩子
按互动度排序。将这些公式用于你的内容。
### 钩子 1:{technique} - @{username}
- **开场**:"{opening_line}"
- **为何有效**:{attention_grab}
- **可复制公式**:{replicable_formula}
- **互动数据**:{likes} 点赞,{comments} 评论,{views} 观看次数
- [观看视频]({url})
[为每个分析的视频重复此部分]
## 内容结构模式
| 视频 | 格式 | 节奏 | 关键留存技巧 |
|-------|--------|--------|--------------------------|
| @username | {format} | {pacing} | {techniques} |
## 行动号召策略
| 视频 | CTA 类型 | CTA 文本 | 放置位置 |
|-------|----------|----------|-----------|
| @username | {type} | "{cta_text}" | {placement} |
## 所有异常值
| 排名 | 用户名 | 点赞数 | 评论数 | 观看次数 | 互动率 |
|------|----------|-------|----------|-------|-----------------|
[列出所有带有指标和链接的异常值]
## 热门话题
### 热门话题标签
[来自 outliers.json 的 topics.hashtags]
### 热门关键词
[来自 outliers.json 的 topics.keywords]
## 可执行的要点
[将模式综合成 4-6 条具体建议]
## 分析的账户
[列出账户]
专注于可执行的见解。"表现最佳的钩子"部分及其可复制公式应突出显示。
完整流程:
RUN_FOLDER="instagram-research/$(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。
互动分数 : likes + (3 × comments) + (0.1 × views)
异常值检测 : 互动率 > 平均值 + (阈值 × 标准差) 的帖子
互动率 : (分数 / 粉丝数) × 100
每周安装量
103
仓库
GitHub 星标
30
首次出现
2026年1月28日
安全审计
安装于
gemini-cli94
opencode94
codex90
github-copilot81
amp77
cursor77
Research high-performing Instagram posts and reels, identify outliers, and analyze top video content for hooks and structure.
APIFY_TOKEN environment variable or in .envGEMINI_API_KEY environment variable or in .envapify-client and google-genai Python packages.claude/context/instagram-accounts.mdVerify setup:
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"
RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"
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)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 analyzedoutlier_count: Number of outliers foundtopics: Top hashtags and keywordsaccounts: List of accounts analyzedoutliers: Array of outlier posts with engagement metricspython3 .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:
See the video-content-analyzer skill for full output schema and hook/format types.
Read {RUN_FOLDER}/outliers.json and {RUN_FOLDER}/video-analysis.json, then generate {RUN_FOLDER}/report.md.
Report Structure:
# Instagram Research Report
Generated: {date}
## Top Performing Hooks
Ranked by engagement. Use these formulas for your content.
### Hook 1: {technique} - @{username}
- **Opening**: "{opening_line}"
- **Why it works**: {attention_grab}
- **Replicable Formula**: {replicable_formula}
- **Engagement**: {likes} likes, {comments} comments, {views} views
- [Watch Video]({url})
[Repeat for each analyzed video]
## Content Structure Patterns
| Video | Format | Pacing | Key Retention Techniques |
|-------|--------|--------|--------------------------|
| @username | {format} | {pacing} | {techniques} |
## CTA Strategies
| Video | CTA Type | CTA Text | Placement |
|-------|----------|----------|-----------|
| @username | {type} | "{cta_text}" | {placement} |
## All Outliers
| Rank | Username | Likes | Comments | Views | Engagement Rate |
|------|----------|-------|----------|-------|-----------------|
[List all outliers with metrics and links]
## Trending Topics
### Top Hashtags
[From outliers.json topics.hashtags]
### Top Keywords
[From outliers.json topics.keywords]
## Actionable Takeaways
[Synthesize patterns into 4-6 specific recommendations]
## Accounts Analyzed
[List accounts]
Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent.
Full pipeline:
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.
Engagement Score : likes + (3 × comments) + (0.1 × views)
Outlier Detection : Posts with engagement rate > mean + (threshold × std_dev)
Engagement Rate : (score / followers) × 100
Weekly Installs
103
Repository
GitHub Stars
30
First Seen
Jan 28, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykWarn
Installed on
gemini-cli94
opencode94
codex90
github-copilot81
amp77
cursor77
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