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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.

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安装方式

直接复制以下提示词,发送给你的 AI 助手即可完成安装。

请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 viralevo 技能。 若已安装,则直接安装 viralevo 技能。

Overview

Skill Key
0xf69/viralevo
Author
0xf69
Source Repo
openclaw/skills
Version
0.6.4
Source Path
skills/0xf69/viralevo
Latest Commit SHA
5f2d1d376d58581e6dd697d24720e86590b06a2e

Extracted Content

SKILL.md excerpt

# 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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