Skill: 0xf69/viralevo (version 0.1.0)

--- name: viralevo description: Self-evolving viral content trend advisor. Monitors 11 platforms, predicts what to post and when, and improves its own accuracy every week automatically. version: 0.6.4 homepage: https://github.com/0xF69/viralevo user-invocable: true metadata: {"openclaw":{"emoji":"📈","requires":{"bins":["node","python3"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY","install":[{"id":"node","kind":"node","label":"Install npm dependencies (better-sqlite3, axios, dotenv)"}]}} --- # 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 user says: - "Run ViralEvo" / "运行趋势雷达" → run collect then report - "Collect trends" / "采集趋势" → run collect only - "Generate report" / "生成报告" → run report only - "Weekly review" / "周度复盘" → run weekly_review - "Show keywords" / "查看关键词" → run keywords --show --- ## Feedback Intake When the user describes post results, **always match to a recent topic, confirm before logging**: - "The hair clips video got 80k views on TikTok" → match topic, log: views=80000, platform=tiktok - "那个AI文章效果很好，小红书5000收藏" → 匹配话题，记录：saves=5000, platform=xiaohongshu Use the `/trend feedback` command or natural language — both are accepted. --- ## Available Commands | Command | Action | |---|---| | `node {baseDir}/scripts/onboarding.js` | First-time setup wizard | | `node {baseDir}/scripts/collect.js` | Fetch trend signals from all sources | | `python3 {baseDir}/scripts/report.py` | Generate and output today's report | | `python3 {baseDir}/scripts/verify.py --hours 24` | Verify yesterday's predictions | | `python3 {baseDir}/scripts/verify.py --hours 72` | Verify 72h-old predictions | | `python3 {baseDir}/scripts/weekly_review.py` | Run self-evolution (Mondays recommended) | | `python3 {baseDir}/scripts/keywords.py --show` | View your keyword index | | `python3 {baseDir}/scripts/keywords.py --add "term"` | Add a keyword manually | | `python3 {baseDir}/scripts/keywords.py --remove "term"` | Remove a keyword | | `python3 {baseDir}/setup.py` | Check all system requirements | | `python3 {baseDir}/scripts/feedback.py --list` | List recent topics to log feedback for | | `python3 {baseDir}/scripts/feedback.py --search "keyword"` | Find a topic by keyword | | `python3 {baseDir}/scripts/feedback.py --topic-id <id> --platform tiktok --views 80000` | Log post performance | | `python3 {baseDir}/db/init_db.py` | Re-initialize database (use if DB is corrupted) | | `python3 {baseDir}/scripts/status.py` | Quick health check — config, API key, DB, recent data | --- ## System Requirements | Requirement | Minimum | Role | |---|---|---| | Node.js | v18+ | Data collection, onboarding | | Python | 3.10+ | Scoring, reports, self-evolution | | OpenClaw | v2026.1+ | Agent runtime, scheduling | | Tavily API Key | Free tier | Indirect platform search | Tavily free tier = 1,000 calls/month. Single niche daily usage ≈ 60–120/month. --- ## Supported Platforms | Platform | Method | Confidence Cap | |---|---|---| | HackerNews | Official Algolia API | 1.00 | | Dev.to | Official API | 1.00 | | Product Hunt | RSS | 1.00 | | Reddit | JSON API (public) | 0.90 | | YouTube | Tavily search | 0.70 | | Twitter / X | Tavily search | 0.70 | | Pinterest | Tavily search | 0.70 | | LinkedIn | Tavily search | 0.70 | | TikTok | Tavily search | 0.65 | | Instagram | Tavily search | 0.65 | --- ## Supported Niches AI/Tech · E-commerce · Beauty/Skincare · Fitness/Health · Finance · Gaming · Fashion/Lifestyle · Education · Real Estate · Pets · Custom (11 niches) --- ## Scoring Formula ``` Total Score = (Platform Signal Strength) × W1 [default 0.25] + (Engagement Velocity) × W2 [default 0.25] + (Cross-Platform Spread) × W3 [default 0.20] + (Niche Relevance Score) × W4 [default 0.15] + (Goal Alignment Score) × W5 [default 0.15] Constraints: W1+W2+W3+W4+W5 = 1.0 exactly Each weight: floor=0.08, ceiling=0.45 Max change per weekly review: ±0.05 (±0.10 after algorithm change detection) ``` --- ## Report Output Format ``` ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔥 ViralEvo | AI/Tech | 2026-03-09 08:15 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔴 ACT NOW (Score > 80) 1. OpenClaw Security Issue — 135k instances exposed ████████████████████ 93% | Confidence: 0.85 📅 Detected 14h ago | Source: hackernews ⏰ Estimated window: ~42h remaining 🎯 Post: TODAY 🟡 PREPARE (Score 60–80) 2. OpenAI Government Surveillance Controversy ████████████████░░░░ 78% | Confidence: 0.74 📅 Detected 6h ago | Source: dev.to ⏰ Estimated window: ~68h remaining 🎯 Post: Tomorrow morning 🟢 EVERGREEN (Score < 60) 3. MCP Protocol Enterprise Adoption ████████░░░░░░░░░░░░ 44% | Confidence: 0.79 📅 Steady growth — no spike ⏰ Relevant: 30d+ 🎯 Post: Any time this week ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 Model Health Accuracy : 58% (44 predictions) Sources : 6/6 ✅ Tavily usage : 112 / 1,000 this month Keyword index: 1,203 terms ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ``` --- ## Scheduling (Cron Setup) ViralEvo runs automatically via OpenClaw's cron system. After onboarding, add these four jobs to your OpenClaw cron config. **How to add cron jobs in OpenClaw:** Tell your agent: ``` "Add a cron job to run ViralEvo daily at 8am" ``` Or add manually to `~/.openclaw/openclaw.json`: ```json { "cron": { "jobs": [ { "id": "viralevo-collect-report", "schedule": "0 8 * * *", "commands": [ "node ~/.openclaw/workspace/viralevo/scripts/collect.js", "python3 ~/.openclaw/workspace/viralevo/scripts/report.py" ] }, { "id": "viralevo-verify-24h", "schedule": "5 8 * * *", "commands": ["python3 ~/.openclaw/workspace/viralevo/scripts/verify.py --hours 24"] }, { "id": "viralevo-verify-72h", "schedule": "10 8 * * *", "commands": ["python3 ~/.openclaw/workspace/viralevo/scripts/verify.py --hours 72"] }, { "id": "viralevo-weekly-review", "schedule": "0 8 * * 1", "commands": ["python3 ~/.openclaw/workspace/viralevo/scripts/weekly_review.py"] } ] } } ``` > See OpenClaw docs: https://docs.openclaw.ai/automation/cron-jobs --- ## OpenClaw Config (Alternative API Key Setup) Instead of using `.env`, you can configure your Tavily key via `~/.openclaw/openclaw.json`: ```json { "skills": { "entries": { "viralevo": { "enabled": true, "apiKey": "tvly-your-key-here" } } } } ``` --- ## Self-Evolution Loop **Daily verification** (5 min and 65 min after your report time): re-fetches topics predicted 24h ago, compares predicted lifecycle vs actual activity, records error. **Weekly review** (every Monday at your report time): 1. Aggregates all predictions from past 7 days 2. Calculates accuracy per platform, per topic type 3. Identifies top 3 sources of systematic error 4. Proposes weight adjustments (max ±0.05 per weight) 5. Applies new weights to config.json 6. Writes report to `reports/YYYY-MM-DD_weekly.md` 7. Auto-rolls back if accuracy drops for 2 consecutive weeks --- ## Accuracy Expectations | Period | Expected Accuracy | |---|---| | Week 1–2 | 30–40% (cold start) | | Month 2 | 55–65% | | Month 3+ | 65–75% | | Month 6+ | 75%+ | Accuracy = prediction within ±20% of actual topic lifecycle. --- ## Data Location ``` ~/.openclaw/workspace/viralevo/ ├── config.json ← niche, weights, schedule ├── user_profile.json ← onboarding answers, language ├── data/ │ ├── trends.db ← SQLite database │ └── backups/ ← daily snapshots, 7-day retention ├── reports/ ← daily + weekly markdown reports └── logs/ └── execution.log ``` --- ## Privacy All data is stored locally on your machine. The skill makes outbound network requests only to fetch public trend signals: - HackerNews, Dev.to, Product Hunt, Reddit: public APIs, no auth required - Tavily API: receives only search query strings — no personal data transmitted --- ## ⚠️ Disclaimers ViralEvo provides probabilistic estimates based on publicly available signals. It does not guarantee specific outcomes in views, impressions, followers, or revenue. All predictions are directional guidance — not the sole basis for business decisions. Platform APIs change without notice. --- ## Uninstall ```bash # Step 1: Remove skill from OpenClaw openclaw skills remove viralevo # Step 2: Delete local data (optional) rm -rf ~/.openclaw/workspace/viralevo/ # Step 3: Verify openclaw skills list ``` > If you reinstall later without deleting Step 2, ViralEvo will resume from your existing data. --- ## Troubleshooting | Symptom | Fix | |---|---| | "Not configured" | Run `node {baseDir}/scripts/onboarding.js` | | "TAVILY_API_KEY not set" | Add key to `~/.openclaw/workspace/.env` | | No topics in report | Run `node {baseDir}/scripts/collect.js` first | | System check | Run `python3 {baseDir}/setup.py` | | Accuracy dropping | Run `python3 {baseDir}/scripts/weekly_review.py` manually | | Quick diagnosis | Run `python3 {baseDir}/scripts/status.py` |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)