Social Media Scraper

Social Media Scraper

A skill that pulls all data from social media posts, analyzes them, and performs transcription + visual analysis for video/audio content.

First-Time Setup

When this skill is invoked for the first time or when the user says "install", "configure", "setup", run the setup mode first.

Setup Detection

Configuration file: ~/.social-media-scraper.env. If the file does not exist or the user requests reinstallation, ask the interactive questions below.

test -f ~/.social-media-scraper.env || echo "First-time setup required"

Setup Questions

Ask the questions one by one, letting the user answer each. Based on the answers, create the ~/.social-media-scraper.env file and install only the tools for the selected platforms.

Question 1 — Platforms

> "Which platforms do you want to use? Instagram, TikTok, Twitter/X, YouTube — pick all of them or only the ones you want. (default: all)"

Normalize the answer as a comma-separated lowercase list: instagram,tiktok,twitter,youtube.

Question 2 — Gemini visual analysis

> "Should Gemini visual video analysis be enabled? This reads on-screen text, products, and scenes — it provides visual context that Whisper cannot translate. Enabling it requires a free Gemini API key (https://aistudio.google.com/apikey). Enable it? (default: yes)"

If yes: ask "Paste your Gemini API key:". Write the key into ~/.social-media-scraper.env but do not print to terminal, do not commit, do not log. Protect the file with chmod 600.

If no: mark GEMINI_ENABLED=false, do not install google-genai.

Question 3 — Transcription language

> "What should the default transcription language be? Auto-detect (recommended — 99 languages), Turkish, English, or another ISO 639-1 language code (e.g. fr, de, es). (default: auto)"

Store the value as auto, tr, en or an ISO code.

Configuration File Format

~/.social-media-scraper.env:

# Active platforms (comma-separated)
PLATFORMS=instagram,tiktok,twitter,youtube

# Gemini video analysis
GEMINI_ENABLED=true
GEMINI_API_KEY=AIza...

# Transcription
TRANSCRIPTION_LANG=auto
WHISPER_MODEL=medium

Installation Steps (based on answers)

Install only the tools for the selected platforms:

# Always required
pip install faster-whisper --break-system-packages
# ffmpeg: macOS → brew install ffmpeg | Linux → apt install ffmpeg

# Only if selected
# instagram:
pip install instaloader --break-system-packages
# tiktok or youtube:
pip install yt-dlp --break-system-packages
# twitter:
npm install -g @steipete/bird
# if gemini enabled:
pip install google-genai --break-system-packages

After Setup

When setup is complete, give the user a short summary (which platforms are active, whether Gemini is enabled, what language) and ask for a sample link to test.

Reading the Current Configuration

When the skill is running, read the ~/.social-media-scraper.env file and behave according to the selections. E.g. if GEMINI_ENABLED=false, skip the visual analysis step. If TRANSCRIPTION_LANG=tr, pass language="tr" to Whisper.

set -a
source ~/.social-media-scraper.env 2>/dev/null
set +a

If a link from a platform not in the PLATFORMS list arrives, ask the user "This platform is not installed, would you like to add it?".

General Flow

1. The user shares a social media link
2. The platform is auto-detected (from the URL)
3. All data is fetched with the appropriate tool for the platform
4. If video/audio content exists: download → extract audio with ffmpeg → transcribe with faster-whisper → analyze visuals/screen with Gemini → delete temporary files
5. Transcription + visual analysis are merged into a full understanding
6. Present results to the user in a clean and readable way

Platform Detection

Detect the platform by looking at the URL:

• instagram.com or instagr.am → Instagram
• tiktok.com → TikTok
• x.com or twitter.com → Twitter/X
• youtube.com or youtu.be → YouTube

Per-Platform Tools

Twitter/X

Priority order:

1. bird CLI (npm package: @steipete/bird) — most comprehensive, tweet + reply thread + media info
2. Jina Reader (curl -s "https://r.jina.ai/TWEET_URL") — fallback method
3. Reading via browser — last resort

bird CLI usage:

bird --urls "TWEET_URL"

If bird is not installed: npm install -g @steipete/bird bird may need Chrome cookies to work, it auto-detects them.

Instagram

Priority order:

1. instaloader (pip package) — reel, post, story download and metadata
2. instagrapi (pip package) — more comprehensive API, may require login
3. yt-dlp — fallback video download

instaloader usage:

pip install instaloader --break-system-packages
instaloader -- -SHORTCODE

The shortcode is extracted from the URL: instagram.com/reel/SHORTCODE/ or instagram.com/p/SHORTCODE/

Fetching metadata (Python):

import instaloader
L = instaloader.Instaloader()
post = instaloader.Post.from_shortcode(L.context, "SHORTCODE")
print(f"Caption: {post.caption}")
print(f"Likes: {post.likes}")
print(f"Comment count: {post.comments}")
print(f"Date: {post.date}")
print(f"Hashtags: {post.caption_hashtags}")

TikTok

Priority order:

1. Download video + metadata with yt-dlp
2. Fetch page content with Jina Reader

yt-dlp usage:

yt-dlp --write-info-json --write-comments -o "downloads/%(id)s.%(ext)s" "TIKTOK_URL"

If yt-dlp is not installed: pip install yt-dlp --break-system-packages Cookies may be required for TikTok: yt-dlp --cookies-from-browser chrome "URL"

YouTube

Video + metadata + comments with yt-dlp:

yt-dlp --write-info-json --write-comments --skip-download -o "downloads/%(id)s.%(ext)s" "YOUTUBE_URL"

If video transcription is needed (when no captions are available):

yt-dlp -f "bestaudio" -o "downloads/audio.%(ext)s" "YOUTUBE_URL"

Transcription (For Video/Audio Content)

Automatically transcribe every post containing video or audio. The user does not need to ask separately.

Steps:

1. Download the video (with the appropriate tool per platform)
2. Extract audio with ffmpeg:

ffmpeg -i video.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 audio.wav

1. Transcribe with faster-whisper:

from faster_whisper import WhisperModel
model = WhisperModel("medium", compute_type="int8")
segments, info = model.transcribe("audio.wav")
print(f"Detected language: {info.language} ({info.language_probability:.0%})")
for segment in segments:
    print(f"[{segment.start:.1f}s → {segment.end:.1f}s] {segment.text}")

1. Delete temporary video and audio files (to save space)

If faster-whisper is not installed: pip install faster-whisper --break-system-packages If ffmpeg is not installed: brew install ffmpeg (macOS) or apt install ffmpeg (Linux)

Video Analysis (Gemini Vision)

For posts containing video, transcribing the audio alone is not enough. On-screen text, displayed products, interfaces, logos, gestures, scene transitions — all of these are part of the meaning. Whisper only translates speech; use Gemini to analyze what appears on screen.

Flow

1. Download the video (yt-dlp / instaloader / etc.)
2. Transcribe audio with Whisper (section above)
3. Upload the video with Gemini File API, analyze it
4. Merge the two sources and present to the user
5. Delete temporary files

API Key

The Gemini API key is read from the GEMINI_API_KEY environment variable. You can get an API key for free from Google AI Studio.

Setup:

export GEMINI_API_KEY="your_api_key_here"

To add it permanently to your shell config:

echo 'export GEMINI_API_KEY="your_api_key_here"' >> ~/.zshrc   # macOS / zsh
echo 'export GEMINI_API_KEY="your_api_key_here"' >> ~/.bashrc  # Linux / bash

If you use a .env file:

export GEMINI_API_KEY=$(grep "^GEMINI_API_KEY=" .env | cut -d= -f2- | tr -d '"' | tr -d "'" | tr -d ' ')

Important: ASCII file path

In Gemini SDK upload, httpx cannot convert non-ASCII characters (ı, ş, ç, ö, ğ) in the filename and crashes. Before uploading, copy the video to an ASCII path like /tmp/video.mp4.

Code (new SDK: google-genai)

The old google.generativeai package is deprecated. Use the new google-genai package:

import os, time, shutil
from google import genai

src = "downloads/instagram_reel.mp4"
tmp = "/tmp/scraper_video.mp4"
shutil.copy(src, tmp)

client = genai.Client()  # read from GEMINI_API_KEY env var
f = client.files.upload(file=tmp)

while f.state.name == "PROCESSING":
    time.sleep(3)
    f = client.files.get(name=f.name)

if f.state.name != "ACTIVE":
    raise RuntimeError(f"Gemini upload failed: {f.state.name}")

prompt = (
    "What is happening in this video? Describe in detail: "
    "on-screen text/titles, logos, product names, "
    "interfaces (app/website), locations or people shown, "
    "the narrative order and key scenes. "
    "Respond in English."
)
resp = client.models.generate_content(model="gemini-2.5-flash", contents=[f, prompt])
visual_analysis = resp.text

client.files.delete(name=f.name)
os.remove(tmp)

Install:

pip install google-genai --break-system-packages

Merging

Combine the Whisper transcript and Gemini visual analysis into a single narrative. Example structure:

• Speech (Whisper): "Today I'm going to show you a new product..."
• Screen/Visual (Gemini): "The video shows a product detail page on an e-commerce site, the user is focused on price and features, browsing interior/exterior images..."

Present it to the user in fluent paragraphs, merging "what they said" with "what they showed on screen".

Output Format

Present results to the user as follows (simple, readable, no lists):

Post info: Who posted, when, on which platform Content: The post's text or description Engagement: Like, comment, share, view counts Comments: Summary of standout comments (if there are many, pick the most notable ones) Transcription: Full text with timestamps if there is video/audio

The user may not be a technical person. Avoid jargon, explain in simple, fluent paragraphs. Summarize in natural language rather than long lists and tables.

Dependencies

These tools will be installed automatically when needed:

• bird CLI: npm install -g @steipete/bird
• instaloader: pip install instaloader --break-system-packages
• yt-dlp: pip install yt-dlp --break-system-packages
• faster-whisper: pip install faster-whisper --break-system-packages
• ffmpeg: brew install ffmpeg (macOS) or apt install ffmpeg (Linux)
• google-genai (Gemini Vision): pip install google-genai --break-system-packages — requires GEMINI_API_KEY env var to use

Check whether each tool is installed before using it. Install if not.

Important Notes

• Video/audio files are temporary, deleted after transcription is taken
• Text data (transcript, comments, metadata) can be stored
• Some platforms may require login, in that case inform the user
• If you get an error, try the next method, present only the result to the user
• Use --cookies-from-browser chrome when cookies are needed

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: elbis330
• Source: elbis330/social-media-scraper-skill
• License: MIT

Install and usage instructions live in the source repository linked above.