---

name: wechat-viral-topic description: Find WeChat public-account articles that break out above the account's average reads for account-growth topic selection. Use when the user asks to discover 公众号爆款, 微信低粉爆款, 平均阅读爆款, 起号选题, category-based WeChat hot articles, or article references filtered by month_read_avg rather than unstable follower_count.

WeChat Viral Topic

Use this skill to find WeChat public-account articles that are hot relative to the account's average reads. Do not use follower_count as the default low-fan signal because the account-detail follower value is unstable.

Quick Start

Run the bundled script from this skill directory:

python3 scripts/search_wechat_viral_topic.py --category ai --days 7 --min-read 10000 --min-read-month-avg-ratio 2 --limit 50 --format markdown

The script excludes large institution/media accounts by default because they are weak references for new-account growth. The built-in list is: 新智元, 机器之心, 差评, 智东西, 极客公园, 量子位, CSDN.

Useful exclusion options:

python3 scripts/search_wechat_viral_topic.py --category ai --exclude-account "某机构号"
python3 scripts/search_wechat_viral_topic.py --category ai --exclude-accounts "账号A,账号B"
python3 scripts/search_wechat_viral_topic.py --category ai --no-default-excluded-accounts

Required environment for live API calls:

export WECHAT_HOT_API_BASE="https://example.com"
export WECHAT_HOT_APP_ID="..."
export WECHAT_HOT_APP_SECRET="..."

If WECHAT_HOT_ACCESS_TOKEN is set, the script uses it and skips token creation. Never write app ids, secrets, or access tokens into skill files.

Workflow

1. Select a platform category from references/categories.md.
2. Fetch hot articles from /api/v2/hot/articles with category, read_num, published_at, and pagination.
   • Send POST bodies as JSON with Content-Type: application/json.
   • Use datetime format for published_at, such as 2026-06-07T00:00:00; the live API rejects plain YYYY-MM-DD.
3. Extract __biz from each content_url.
4. Enrich each account through /api/v2/accounts/detail, preferring biz over nickname.
5. Filter for average-read breakout candidates:
   • read_num >= --min-read
   • read_num / month_read_avg >= --min-read-month-avg-ratio
6. Exclude institution/media accounts that are not useful new-account references, unless the user disables the default exclusion list.
7. Rank by breakout_score, then by read_month_avg_ratio, then by read_num.
8. Return JSON for automation or Markdown for human topic review.

Output Contract

Each result must preserve the source URL and average-read evidence:

{
  "platform": "wechat",
  "content_id": "",
  "url": "",
  "title": "",
  "author_id": "",
  "author_name": "",
  "follower_count": 0,
  "month_read_avg": 0,
  "published_at": "",
  "view_count": 0,
  "like_count": 0,
  "share_count": 0,
  "hot_score": 0,
  "breakout_score": 0,
  "evidence": []
}

Read references/scoring.md before changing thresholds or comparing this output with other platforms.

Guardrails

• Treat follower_count as an unstable reference field, not a filter.
• Exclude obvious institution/media accounts by default. Add more with --exclude-account or --exclude-accounts when a niche has dominant official/media accounts.
• If account detail fails because the account is not yet collected, retry only when the user accepts the delay or --account-retry-seconds is set.
• If month_read_avg is missing, do not label the article as a confirmed average-read breakout.
• Use article URLs only for research and topic reference. Do not copy article content wholesale.