wechat-viral-topic

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 Select a platform category from references/categories.md . 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 . Extract __biz from each content_url . Enrich each account through /api/v2/accounts/detail , preferring biz over nickname . Filter for average-read breakout candidates: read_num >= --min-read read_num / month_read_avg >= --min-read-month-avg-ratio Exclude institution/media accounts that are not useful new-account references, unless the user disables the default exclusion list. Rank by breakout_score , then by read_month_avg_ratio , then by read_num . 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.