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instagram-reel-extractorInstagram 卷轴提取器

Agent Skill

instagram-reel-extractor 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

329

周安装

14

GitHub Stars

6

下载量

115
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:instagram-reel-extractor(Instagram 卷轴提取器)
来源仓库:https://github.com/jaycebatallones/instagram-reel-extractor
仓库路径:skills/instagram-reel-extractor
安装命令:
npx skills add https://github.com/jaycebatallones/instagram-reel-extractor --skill instagram-reel-extractor
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jaycebatallones/instagram-reel-extractor --skill instagram-reel-extractor

简介

instagram-reel-extractor 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合围绕项目状态和变更进行整理。

  • 适用于开发流程管理、代码审查支持或团队协作事项跟踪等场景。
  • 通过 npx skills add 命令安装,需确认权限范围和是否触发联网或文件读写操作。
  • 建议结合原始 README 核验具体用法,并检查仓库维护状态与安全性。
  • 使用前应评估是否会执行命令或访问外部资源,避免意外行为。

SKILL.md

Instagram Reel Extractor

Extract the spoken transcript, metadata, music, comments, and key frames from an Instagram Reel.

Prerequisites

The following must be installed on the user's machine:

  • yt-dlpbrew install yt-dlp
  • ffmpegbrew install ffmpeg
  • Python packagespip3 install openai-whisper ShazamAPI

Or run the setup script: bash setup.sh

If any dependency is missing, tell the user which ones to install before proceeding.

Usage

Run the extraction script with the Reel URL:

python3 scripts/extract_reel.py "REEL_URL"

Save the extraction to a specific directory:

python3 scripts/extract_reel.py "REEL_URL" --save-dir ~/notes/reels

For better transcription accuracy (slower, uses more memory):

python3 scripts/extract_reel.py "REEL_URL" --whisper-model small

For raw JSON output:

python3 scripts/extract_reel.py "REEL_URL" --json

Skip frame extraction (faster, audio/metadata only):

python3 scripts/extract_reel.py "REEL_URL" --no-frames

Adjust frame interval (default every 2 seconds):

python3 scripts/extract_reel.py "REEL_URL" --frame-interval 1.0

What Gets Extracted

  • Original URL — link back to the source reel (always included)
  • Creator — username and handle
  • Metrics — likes, comments count (as of extraction date)
  • Music — song title, artist, album, genre, Shazam link (via audio fingerprinting)
  • Caption — the original post caption
  • Hashtags — all tags from the post
  • Top Comments — comment text with author and like count
  • Duration — video length
  • Upload date
  • Transcript — full spoken text from the audio, with timestamps
  • Language — auto-detected language of the speech
  • Key frames — screenshots extracted every N seconds, saved as JPGs with timestamps

Output

The script outputs a structured markdown summary with the original reel URL at the top. Present this to the user as-is — do not modify the transcript text itself, though you can clean up line breaks and paragraph flow for readability.

The frame images are saved to a frames/ subdirectory inside the working directory. When presenting the extraction, use the Read tool to view the frame images so you can describe what's happening visually at each timestamp (talking head, B-roll, text overlay, product shot, etc.).

When --save-dir is provided, the extraction is automatically saved as <creator>-reel-<upload_date>.md. If a file already exists, a number is appended (e.g., garyvee-reel-2026-04-08-2.md).

Configuring a Default Save Location

To always save extractions to a specific folder, tell Claude: "save reel extractions to ~/my/folder". Claude will pass --save-dir automatically on future runs.

Whisper Model Sizes

ModelSpeedAccuracyMemory
tinyFastestLower~1 GB
baseFastGood~1 GB
smallModerateBetter~2 GB
mediumSlowGreat~5 GB
largeSlowestBest~10 GB

Default is base — a good balance for short-form reel content. Suggest small if the user reports transcription quality issues.

Troubleshooting

  • Login required errors: Some reels may require authentication. Pass --cookies-from chrome (or firefox) to use browser cookies.
  • No speech detected: The reel may be music-only or use on-screen text instead of speech. Let the user know.
  • Slow transcription: Whisper runs on CPU by default. On Apple Silicon Macs, it uses the Neural Engine automatically. For faster runs, suggest the tiny model.
  • Music not identified: Shazam works best with distinct background music. Speech-heavy reels with no music will return no match.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

31.93%
按下载量换算37

Claude

32.14%
按下载量换算37

Cursor

20.62%
按下载量换算24

Gemini CLI

8.38%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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