viral-topic

name	viral-topic
description	Route viral topic discovery across platform-specific account-growth skills. Use when the user asks for 起号选题, low-follower viral references, recent viral content, cross-platform topic mining, or does not specify whether to search WeChat, X, Bilibili, or YouTube.

Viral Topic

Use this skill as the control layer for finding topic references across platforms. It does not fetch data itself; it routes the request to the platform skill whose collection logic matches the platform.

Package Layout

This repository publishes viral-topic/ as one skill package:

viral-topic/
├── SKILL.md
├── references/platforms.md
├── wechat-viral-topic/
├── x-viral-topic/
├── bilibili-viral-topic/
└── youtube-viral-topic/

The root SKILL.md is the controller. The platform directories are child skills/adapters with their own SKILL.md, references/, agents/, and scripts.

Platform Routing

Read references/platforms.md before deciding which child skills to run.

Implemented child skills:

• $wechat-viral-topic: WeChat public-account articles that exceed the account's average reads.
• $x-viral-topic: X/Twitter posts from low-follower authors that outperform their audience size.
• $bilibili-viral-topic: B站 videos from low-follower UP主 that outperform their follower base.
• $youtube-viral-topic: recent high-performing YouTube videos without low-subscriber filtering.

If the user gives no platform, default to the implemented set: WeChat, X, Bilibili, and YouTube. If the user asks for 小红书 or 抖音, explain that those adapters are not implemented yet and propose the available API/browser-automation path before building it.

Workflow

1. Normalize the user's niche into 3-8 focused search terms.
2. Choose platforms from the request. For "全平台" or "各个平台", run all implemented child skills.
3. Run each platform skill independently with its own defaults:
   • WeChat: --days 7, --min-read 10000, --min-read-month-avg-ratio 2
   • X: --days 7, --max-followers 50000, --min-engagement 100
   • Bilibili: --days 30, --max-followers 100000, --min-play 10000
   • YouTube: --days 30, --min-views 10000
4. Preserve source URLs, author/account metrics, timing, and evidence labels.
5. Merge outputs into a cross-platform topic table only after preserving each platform's original evidence.
6. Label confidence carefully:
   • WeChat confirms average-read breakout, not low-follower breakout.
   • X and Bilibili can confirm low-follower viral only when follower counts are present.
   • YouTube confirms viral strength, not low-subscriber breakout.

Output Shape

When combining platforms, use this compact table first:

platform | title | author | published_at | main_metric | scale_metric | breakout_reason | url

Then add a short synthesis:

• reusable topic angle
• title/hook pattern
• format pattern
• why it likely worked
• whether the evidence is confirmed or proxy-only

Guardrails

• Do not collapse all platforms into one scoring formula without keeping platform-specific evidence.
• Do not call YouTube results low-fan by default.
• Do not use WeChat follower_count as the default low-fan signal; compare article reads with month_read_avg.
• Never write API keys, cookies, app secrets, or access tokens into skill files or outputs.
• Use results as topic references and structure inspiration. Do not copy posts, articles, video scripts, captions, or thumbnails wholesale.