feat(workflow): Consolidated multi-platform research pipeline — expand XResearch into SourceDiscovery

Feature Description

Expand the current XResearch workflow into a unified SourceDiscovery pipeline that collects trending content from multiple platforms (X/Twitter, TikTok, YouTube Shorts, Instagram Reels, Reddit) and consolidates it into a single research feed before feeding downstream workflows.

Motivation

Currently XResearch only scrapes X/Twitter trending topics → AI-analyzed posts → Telegram reports. The Video Sourcing + Retouch workflow (see companion issue) needs broader source discovery — not just text posts but also video content — to find viral clips across platforms, extract key themes, and feed them as source material for retouch/re-upload.

A consolidated research pipeline would:

• Find video content (not just text posts) across TikTok, YouTube Shorts, Instagram Reels
• Cross-reference trends across platforms — see the same trend on X, TikTok, and Reddit
• Deduplicate and rank source material by engagement metrics across all platforms
• Store source metadata (URL, platform, views, engagement, thumbnail, duration) in a structured format that downstream workflows can consume
• Send consolidated research reports to Telegram (like current XResearch)

Proposed Solution

New class: src/classes/SourceDiscovery.py

SourceDiscovery(fp_profile_path)
  ├── discover_x_trends()        # existing XResearch logic, refactored
  ├── discover_tiktok_trends()   # scrape TikTok trending/creative center
  ├── discover_youtube_trends()  # scrape YouTube Shorts trending
  ├── discover_reddit_trends()   # scrape Reddit popular/rising posts
  ├── consolidate_sources()      # merge + dedupe + rank across platforms
  └── run() → List[SourceItem]   # unified pipeline

SourceItem schema (replaces current XResearch post dict)

{
  "id": "src-<platform>-<hash>",
  "platform": "tiktok|x|youtube|reddit|instagram",
  "url": "https://...",
  "title": "...",
  "content": "...",
  "views": 0,
  "engagement": { "likes": 0, "shares": 0, "comments": 0 },
  "has_video": true,
  "video_url": "https://...",
  "thumbnail_url": "https://...",
  "duration_seconds": 30,
  "trend_name": "...",
  "ai_analysis": { "main_topic": "...", "keywords": [], "angle": "...", "summary": "..." },
  "discovered_at": "2026-04-25T..."
}

New script: scripts/run_source_discovery.py

CLI entry point mirroring scripts/xresearch.py pattern, with flags:

• --platforms x,tiktok,youtube,reddit
• --min-engagement 1000
• --save-json (persist SourceItems to .mp/sources/)
• --send-telegram

Storage: .mp/sources/ directory

Each run saves sources_YYYY-MM-DD.json so downstream workflows can pick up discovered sources.

Current XResearch → Migration

• XResearch.py stays functional but internal methods get refactored into SourceDiscovery
• xresearch.py script stays as shortcut: --platforms x
• Full migration path: old script works, new script is the supercharged version

Alternatives Considered

• Per-platform separate scripts → rejected (cross-platform dedup/ranking is core value)
• API-based discovery → rejected (most platforms require auth; Selenium matches existing pattern)
• Just extend XResearch → rejected (scope too different; new class keeps things clean)

Dependencies

• Companion issue: Video Sourcing + Retouch workflow
• Requires yt-dlp for video metadata extraction