Social Media Data Collector

SKILL.md

---
name: social-media-data-collector
description: |
  Multi-platform social media data collection and aggregation for content performance tracking.
  Use when: (1) collecting engagement metrics (views/likes/comments/shares) across multiple platforms,
  (2) filling bitable/spreadsheet with social media performance data, (3) tracking content distribution
  results across 10+ platforms, (4) need to scrape platforms without APIs.
  Covers: Douyin, Weibo, Kuaishou, Bilibili, Toutiao, Xiaohongshu, WeChat Video (视频号),
  Autohome (汽车之家), Yiche (易车), Baijiahao (百家号), Douyu (斗鱼), Pipixia (皮皮虾),
  Dongchedi (懂车帝), TikTok, YouTube.
  NOT for: posting content, account management, or social listening/monitoring.
---

# Social Media Data Collector

## Overview

Collect engagement metrics from 13+ platforms, aggregate into structured format (飞书多维表格/CSV).
Three-tier approach: API first → browser scrape fallback → manual flag.

## Execution Flow

1. **Classify platforms** by data access method (see references/platform-guide.md)
2. **API tier** — call APIs for platforms with programmatic access
3. **Browser tier** — Playwright render + text extraction for remaining
4. **Aggregate** — normalize data, write to target (bitable/CSV)
5. **Cleanup** — remove screenshots, temp files, browser cache

## Platform Tiers

| Tier | Platforms | Method |
|------|-----------|--------|
| API-first | 抖音, 微博, 快手, B站, 今日头条, 小红书 | TikHub API / BlueAI Crawler |
| Browser-scrape | 百家号, 汽车之家, 易车, 视频号, 斗鱼, 皮皮虾 | Playwright headless |
| API+scrape | 懂车帝 | TikHub (limited) + scrape |

## Model Strategy (Token Optimization)

### Problem
Using opus/sonnet for the entire pipeline wastes tokens on mechanical tasks.

### Recommended Model Split

| Phase | Model | Why |
|-------|-------|-----|
| Planning & classification | opus/sonnet | Needs reasoning |
| API calls & JSON parsing | haiku/flash | Mechanical, no reasoning needed |
| Browser text extraction | Code (no LLM) | Pure Python, no model call |
| Data normalization | haiku/flash | Simple mapping |
| Report/summary | sonnet | Needs synthesis |

### Implementation
- Use `scripts/collect_api.py` for API tier — **zero LLM tokens** (pure code)
- Use `scripts/collect_browser.py` for browser tier — **zero LLM tokens** (pure code)
- Only invoke LLM for: planning which platforms to hit, handling errors, writing summaries

### Token Budget Estimate (per 13-platform run)
- With current approach (all-opus): ~80k tokens
- With optimized approach (code scripts + haiku routing): ~5k tokens
- **Savings: 94%**

## Key Commands

```bash
# Full collection run
python3 scripts/collect_api.py --config /tmp/sm-collect/config.json

# Browser scrape specific platforms  
python3 scripts/collect_browser.py --platforms "百家号,汽车之家,视频号"

# Write to bitable
python3 scripts/write_bitable.py --app-token XXX --table-id YYY --data /tmp/sm-collect/results.json

# Cleanup
rm -rf /tmp/sm-collect/ /tmp/screenshots/
```

## Bitable Field Mapping

| 多维表格字段 | 类型 | 说明 |
|-------------|------|------|
| 播放量 | text | 带"万"后缀的文本 |
| 点赞 | number | 纯数字 |
| 评论 | number | 纯数字 |
| 分享 | number | 纯数字 |
| 收藏 | number | 纯数字 |
| 互动量合计 | text | 带"万"后缀的文本 |
| 数据统计日期 | text | 格式 "2026.5.15" |

⚠️ 注意 `播放量` 和 `互动量合计` 是 text 类型，不是 number！传数字会报 TextFieldConvFail。

## Cleanup Protocol

After each collection run, delete:
- `/tmp/sm-collect/` (intermediate JSON)
- `/tmp/screenshots/` (browser screenshots)
- `/tmp/subagent-out/` (if spawned sub-agents)
- Any `.json` temp files in workspace

## Error Handling

- API 403/401 → token expired, refresh and retry once
- Browser timeout → increase to 25s, retry with `wait_until="domcontentloaded"` 
- Platform redirects → check URL is correct (易车 hao vs sv domain!)
- Empty data → flag for manual check, don't guess

## Platform-Specific Notes

See `references/platform-guide.md` for detailed per-platform experience including:
- Authentication requirements
- URL patterns and gotchas  
- Data extraction selectors
- Known limitations