Hype Scanner

Real-time crypto and stock hype detection using Reddit, CoinGecko, DEXScreener, and StockTwits. AI-powered signal validation with local Ollama model. Only re...

Security Scan

The skill's code and instructions match its stated purpose (a local Node.js hype scanner that uses public APIs and a local Ollama model); nothing obvious is requesting unrelated credentials or installing external binaries, but a few operational assumptions/omissions are worth noting before you run it.

Purpose & Capability

The name/description (crypto/stock hype scanner) align with the included Node.js scanner and SKILL.md. The scanner queries Reddit, CoinGecko, DEXScreener, and StockTwits and calls a local Ollama instance for analysis — these are coherent with the stated purpose. It writes alerts.json/state/log files locally (expected for this task).

Instruction Scope

SKILL.md and the code restrict actions to scanning public APIs, local Ollama (http://localhost:11434), and writing alerts/state/logs to the scanner directory. The OpenClaw cron example instructs the agent to read alerts.json and send Telegram messages; the skill itself does not include a Telegram integration or declare Telegram credentials, so the alert-transport step depends on other agent configuration. The provided Windows Task Scheduler instructions run the scanner under the current user and ask to 'Run whether logged in or not' — this implies stored credentials for the scheduler and elevated persistence that users should be aware of.

Install Mechanism

No install spec or external downloads are used — the skill is instruction-only plus a Node.js script that uses built-in Node modules (fs/http/https). That is low-risk from an install mechanism perspective (nothing arbitrary is downloaded or executed beyond Node itself).

Credentials

The skill declares no required environment variables or credentials, and its network calls go to public APIs and localhost Ollama. One mismatch to note: SKILL.md expects alerts to be delivered via Telegram, but the skill does not declare or request Telegram credentials — responsibility for messaging is delegated to the agent/OpenClaw environment. Ensure the Telegram (or other) integration used to forward alerts is configured elsewhere and only accessible with appropriate credentials.

Persistence & Privilege

always:false and no system-wide configuration changes are requested. The scanner writes files (alerts.json, scanner-state.json, scanner-ai.log) in its own directory and relies on a scheduler for periodic execution. It does not modify other skills or agent config in the code shown.

Assessment

This skill appears to do what it claims: polling public market/social APIs, scoring candidates, and using a local Ollama instance for final validation. Before installing: 1) Ensure you run it on a machine with Node.js and a local Ollama instance (the code expects http://localhost:11434 and a specified model); if Ollama is missing the scanner will fall back to rules. 2) Be aware it writes alerts.json, scanner-state.json, and logs to its directory — run it under a limited user and monitor those files. 3) The SKILL.md's alert delivery (Telegram) is an external step — configure your Telegram token or other notifier securely in your agent/system; the skill does not store or request that token. 4) The Task Scheduler / cron guidance may require stored OS credentials for 'Run whether logged in or not' — consider using a less-privileged scheduled account. 5) If you need higher assurance, provide the full (non-truncated) scanner-ai.js for review and consider running it in an isolated environment (VM/container) while you validate behavior and network calls.

Hype Scanner 🦁 (Ari)

Detect real hype before it hits the charts. Built for autonomous 24/7 operation.

What It Does

Scans 4 sources every 15 minutes:

• Reddit — 5 subreddits (wallstreetbets, CryptoCurrency, SatoshiStreetBets, memecoins, pennystocks)
• CoinGecko — trending + gainers
• DEXScreener — top token boosts (new launches)
• StockTwits — trending tickers

AI validation layer (local Ollama, qwen3:32b):

• Analyzes every candidate for real signal vs noise
• Confidence score 1-10 — only ≥6 becomes an alert
• Zero API costs for the AI part

Architecture

Scanner (Node.js, every 15 min)
  ↓ Rule-based pre-filter (fast)
  ↓ Ollama validation per candidate (smart)
  → alerts.json (only real signals)

OpenClaw Cron (every 20 min)
  → Read alerts.json
  → If pending → alert Yuri via Telegram

Setup

Prerequisites

• Node.js 18+
• Ollama running locally with qwen3:32b (or any model)
• Windows Task Scheduler (or cron) for scanner loop

Files

hype-scanner/
├── scanner-ai.js        ← main scanner (Node.js)
├── alerts.json          ← output (pending alerts)
├── scanner-state.json   ← cooldown + seen tokens
└── scanner-ai.log       ← debug log

Step 1: Install Scanner

Clone or copy scanner-ai.js to your workspace:

# No npm install needed — uses built-in https/http/fs
node scanner-ai.js

Step 2: Schedule with Windows Task Scheduler

Create a VBS wrapper for zero-flash execution:

' ari-scanner.vbs
Set oShell = CreateObject("WScript.Shell")
oShell.Run "cmd /c node C:\path\to\hype-scanner\scanner-ai.js >> C:\path\to\hype-scanner\scanner-ai.log 2>&1", 0, False

Register in Task Scheduler:

• Trigger: Every 15 minutes
• Action: wscript.exe ari-scanner.vbs
• Run As: current user
• Run whether logged in or not

Step 3: Add OpenClaw Cron Alert Checker

Add this cron to OpenClaw (every 20 minutes):

{
  "name": "Ari Alert Checker",
  "schedule": { "kind": "every", "everyMs": 1200000 },
  "payload": {
    "kind": "agentTurn",
    "message": "Check C:\\path\\to\\hype-scanner\\alerts.json. If pending alerts exist, send them to Telegram, then mark as seen (set seen: true on each). Format: 🦁 HYPE ALERT: [token] [source] confidence: [X]/10. If none → HEARTBEAT_OK.",
    "timeoutSeconds": 60
  }
}

Configuration

Edit scanner-ai.js top-level config:

const CONFIG = {
  minHypeScore: 3,          // pre-filter threshold (Ollama does the real work)
  volumeSpikeThreshold: 200, // volume spike % to flag
  subreddits: ['wallstreetbets', 'CryptoCurrency', 'SatoshiStreetBets', 'memecoins', 'pennystocks'],
  redditMinScore: 50,        // min Reddit post score
  alertCooldownHours: 3,     // don't re-alert same token
};

Alert Format (alerts.json)

[
  {
    "id": "BTC-1706...",
    "token": "BTC",
    "sources": ["reddit", "coingecko"],
    "hypeScore": 8.5,
    "ollamaConfidence": 7,
    "ollamaSummary": "Strong momentum across Reddit and CoinGecko trending. Institutional buying signals.",
    "timestamp": "2026-02-24T04:30:00Z",
    "seen": false
  }
]

Ollama Model Options

Model	Speed	Accuracy	Use When
qwen3:32b	Slow	⭐⭐⭐⭐⭐	Main analysis
qwen2.5:7b	Fast	⭐⭐⭐	Heavy load
llama3.2:3b	Very fast	⭐⭐	Fallback

If Ollama is overloaded (timeout), scanner falls back to rule-based scoring only.

Integration with OpenClaw Morning/Evening Brief

Add to your Morning Brief cron:

Read hype-scanner/alerts.json — pending alerts?
If yes → include in brief + mark as seen

Production Results

Running 24/7 on a trading system with:

• ~96 scans/day
• Average 0-3 real alerts/day (low noise)
• Caught BONK, WIF, and PENGU early in their runs
• Zero false positives that triggered a bad trade

Philosophy

Quality over quantity. Most scanners spam you with noise. Ari is trained to stay quiet unless it's real.

Local AI, no API cost. Ollama runs on your GPU. 10,000 analyses = $0.

Autonomous. Silent. Alert only when it matters.