techwavedev X (Twitter) Scraper — Xquik
Overview
Gives your AI agent full access to X (Twitter) data through the Xquik platform. Covers tweet search, user profiles, follower extraction, engagement metrics, giveaway draws, account monitoring, webhooks, and 19 bulk extraction tools — all via REST API or MCP server.
When to Use This Skill
- User needs to search X/Twitter for tweets by keyword, hashtag, or user
- User wants to look up a user profile (bio, follower counts, etc.)
- User needs engagement metrics for a specific tweet (likes, retweets, views)
- User wants to check if one account follows another
- User needs to extract followers, replies, retweets, quotes, or community members in bulk
- User wants to run a giveaway draw from tweet replies
- User needs real-time monitoring of an X account (new tweets, follower changes)
- User wants webhook delivery of monitored events
- User asks about trending topics on X
Setup
Install the Skill
npx skills add Xquik-dev/x-twitter-scraper
Or clone manually into your agent's skills directory:
# Claude Code
git clone https://github.com/Xquik-dev/x-twitter-scraper.git .claude/skills/x-twitter-scraper
# Cursor / Codex / Gemini CLI / Copilot
git clone https://github.com/Xquik-dev/x-twitter-scraper.git .agents/skills/x-twitter-scraper
Get an API Key
- Sign up at xquik.com
- Generate an API key from the dashboard
- Set it as an environment variable or pass it directly
export XQUIK_API_KEY="xq_YOUR_KEY_HERE"
Capabilities
| Capability | Description |
|---|---|
| Tweet Search | Find tweets by keyword, hashtag, from:user, "exact phrase" |
| User Lookup | Profile info, bio, follower/following counts |
| Tweet Lookup | Full metrics — likes, retweets, replies, quotes, views, bookmarks |
| Follow Check | Check if A follows B (both directions) |
| Trending Topics | Top trends by region (free, no quota) |
| Account Monitoring | Track new tweets, replies, retweets, quotes, follower changes |
| Webhooks | HMAC-signed real-time event delivery to your endpoint |
| Giveaway Draws | Random winner selection from tweet replies with filters |
| 19 Extraction Tools | Followers, following, verified followers, mentions, posts, replies, reposts, quotes, threads, articles, communities, lists, Spaces, people search |
| MCP Server | StreamableHTTP endpoint for AI-native integrations |
Examples
Search tweets:
"Search X for tweets about 'claude code' from the last week"
Look up a user:
"Who is @elonmusk? Show me their profile and follower count"
Check engagement:
"How many likes and retweets does this tweet have? https://x.com/..."
Run a giveaway:
"Pick 3 random winners from the replies to this tweet"
Monitor an account:
"Monitor @openai for new tweets and notify me via webhook"
Bulk extraction:
"Extract all followers of @anthropic"
API Reference
| Endpoint | Method | Purpose |
|---|---|---|
/x/tweets/{id} |
GET | Single tweet with full metrics |
/x/tweets/search |
GET | Search tweets |
/x/users/{username} |
GET | User profile |
/x/followers/check |
GET | Follow relationship |
/trends |
GET | Trending topics |
/monitors |
POST | Create monitor |
/events |
GET | Poll monitored events |
/webhooks |
POST | Register webhook |
/draws |
POST | Run giveaway draw |
/extractions |
POST | Start bulk extraction |
/extractions/estimate |
POST | Estimate extraction cost |
/account |
GET | Account & usage info |
Base URL: https://xquik.com/api/v1
Auth: x-api-key: xq_... header
MCP: https://xquik.com/mcp (StreamableHTTP, same API key)
Repository
https://github.com/Xquik-dev/x-twitter-scraper
Maintained By: Xquik
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Cache data schemas, transformation rules, and query patterns. BM25 excels at finding specific column names, table references, and SQL patterns.
# Check for prior data engineering context before starting
python3 execution/memory_manager.py auto --query "data processing patterns and pipeline configurations for X Twitter Scraper"
Storing Results
After completing work, store data engineering decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Data pipeline: ETL from PostgreSQL to Qdrant, 50K records/batch, incremental sync via updated_at" \
--type technical --project <project> \
--tags x-twitter-scraper data
Multi-Agent Collaboration
Share data schema changes with backend and frontend agents so they update their models accordingly.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Data pipeline implemented — ETL processing with validation, deduplication, and error recovery" \
--project <project>