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viralevo

Self-evolving viral content trend advisor. Monitors 11 platforms, predicts what to post and when, and improves its own accuracy every week automatically.

0xf690 source installs☆ 0 Repository starsv0.6.4

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install 0xf69/viralevo

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

ViralEvo — Viral Content Trend Advisor

Version: v0.6.4 | Languages: English / 中文


语言说明 / Language Support

本 Skill 完整支持中文操作。安装完成后,你可以:

  • 用中文与 Agent 对话("今天该发什么内容?")
  • 接收中文版每日报告
  • 用中文关键词监控中文平台内容
  • 在引导设置时选择界面语言

This skill fully supports both English and Chinese. During onboarding, the agent will ask which language you prefer.


What This Skill Does

ViralEvo monitors content platforms, scores trending topics using a weighted formula, predicts lifecycle windows, and automatically adjusts its own prediction weights every week based on how accurate it was.

Three core advantages over manual research:

  1. Catches trends 12–48h early — monitors signal velocity across 11 platforms simultaneously
  2. Learns from your results — when you report your post outcomes, those signals feed back into the model
  3. Self-corrects weekly — every Monday the system reviews its prediction errors and updates its weights automatically

Quick Start

After installation, add your Tavily API key:

echo "TAVILY_API_KEY=tvly-xxxx" >> ~/.openclaw/workspace/.env

Then tell your agent:

"Start ViralEvo setup"
— or in Chinese —
"开始趋势雷达设置"

Or run onboarding directly:

node {baseDir}/scripts/onboarding.js

Natural Language Triggers

When the user reports post results (e.g. "got 80k views", "效果很好"), the agent should:

  1. Search for the matching topic: python3 {baseDir}/scripts/feedback.py --search "<keyword>"
  2. Confirm the match with the user
  3. Log the result: python3 {baseDir}/scripts/feedback.py --topic-id <id> --platform <platform> --views <n>

When the user says any of the following, the agent should run collect → report:

  • "What should I post today?" / "今天该发什么?"
  • "Any trends?" / "有什么趋势?"
  • "Show me the trend report" / "给我看趋势报告"
  • "What's trending in my niche?" / "我的赛道有什么热点?"

When the...

Source information

Source
github
Repository
openclaw/skills
Path
skills/0xf69/viralevo
Version
0.6.4
Commit
5f2d1d376d58581e6dd697d24720e86590b06a2e
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# ViralEvo — Viral Content Trend Advisor

**Version:** v0.6.4 | **Languages:** English / 中文

---

## 语言说明 / Language Support

本 Skill 完整支持中文操作。安装完成后,你可以:
- 用中文与 Agent 对话("今天该发什么内容?")
- 接收中文版每日报告
- 用中文关键词监控中文平台内容
- 在引导设置时选择界面语言

This skill fully supports both English and Chinese. During onboarding, the agent will ask which language you prefer.

---

## What This Skill Does

ViralEvo monitors content platforms, scores trending topics using a weighted formula, predicts lifecycle windows, and automatically adjusts its own prediction weights every week based on how accurate it was.

**Three core advantages over manual research:**

1. **Catches trends 12–48h early** — monitors signal velocity across 11 platforms simultaneously
2. **Learns from your results** — when you report your post outcomes, those signals feed back into the model
3. **Self-corrects weekly** — every Monday the system reviews its prediction errors and updates its weights automatically

---

## Quick Start

After installation, add your Tavily API key:

```bash
echo "TAVILY_API_KEY=tvly-xxxx" >> ~/.openclaw/workspace/.env
```

Then tell your agent:

```
"Start ViralEvo setup"
— or in Chinese —
"开始趋势雷达设置"
```

Or run onboarding directly:

```bash
node {baseDir}/scripts/onboarding.js
```

---

## Natural Language Triggers

When the user reports post results (e.g. "got 80k views", "效果很好"), the agent should:
1. Search for the matching topic: `python3 {baseDir}/scripts/feedback.py --search "<keyword>"`
2. Confirm the match with the user
3. Log the result: `python3 {baseDir}/scripts/feedback.py --topic-id <id> --platform <platform> --views <n>`

When the user says any of the following, the agent should run **collect → report**:

- "What should I post today?" / "今天该发什么?"
- "Any trends?" / "有什么趋势?"
- "Show me the trend report" / "给我看趋势报告"
- "What's trending in my niche?" / "我的赛道有什么热点?"

When the...

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