Skill flagged — suspicious patterns detected

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Quant Orchestrator

v1.1.0

Multi-Agent AI Quant System with multi-coin prediction, strategy templates, and automated backtesting

0· 393·1 current·1 all-time

Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for pikachu022700/quant-orchestrator.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Quant Orchestrator" (pikachu022700/quant-orchestrator) from ClawHub.
Skill page: https://clawhub.ai/pikachu022700/quant-orchestrator
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Canonical install target

openclaw skills install pikachu022700/quant-orchestrator

ClawHub CLI

Package manager switcher

npx clawhub@latest install quant-orchestrator
Security Scan
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high confidence
!
Purpose & Capability
The skill claims to be a multi‑agent quant orchestrator, which explains the prediction, backtest and strategy code. However, there are surprising elements that don't belong to that stated purpose: a standalone billing module (billing.py) with a hardcoded API key and skillpay API URL, and multiple files referencing a local absolute model path (/Users/a/.openclaw/...), while the skill metadata declares no credentials or config requirements. The code also imports heavy dependencies (lightgbm, numpy, requests) though the SKILL.md and registry declare no required packages. These are disproportionate or undeclared relative to the simple description.
Instruction Scope
SKILL.md shows normal usage examples (instantiating MultiCoinPredictor and calling run_all) and lists pricing, but it does not document when or how billing is invoked, nor how model files are provided. The code will make outbound POSTs to https://api.hyperliquid.xyz/info to fetch prices and billing.py calls https://skillpay.me endpoints. The CLI sections hardcode a local model path and may attempt to read local files if executed. The runtime instructions are not explicit about network calls, local file access, or charging behavior, giving the agent broad ability to call external endpoints and access local model files if run.
Install Mechanism
There is no install spec (no external downloads or archive extraction), so nothing is fetched during install. The risk comes from the included source files themselves (they will be present in the skill), but there is no installer that pulls arbitrary code from untrusted URLs.
!
Credentials
The registry declares no required environment variables or credentials, yet billing.py contains a hardcoded API key and contacts an external billing service. That embedded credential is sensitive and not declared. The skill also performs network requests to third‑party endpoints (market data and billing) without declaring those endpoints or requiring explicit authorization. The code references a user home path for model files, which implies filesystem access to potentially sensitive local files.
Persistence & Privilege
The skill is not marked always:true and does not attempt to modify other skills or system config. Autonomous invocation (default) remains possible but there is no evidence the skill self‑installs persistent agents or changes global settings.
What to consider before installing
This skill contains plausible quant code but also several red flags you should resolve before installing or using it with real data or funds: 1) billing.py embeds a hardcoded API key and calls a third‑party billing API — ask the author why a secret is in the repository and request that billing keys be provided via environment variables or handled by the platform (and rotate the embedded key immediately). 2) The SKILL.md and registry declare no credentials or dependencies, yet the code uses requests, numpy, and lightgbm and will make outbound network calls to api.hyperliquid.xyz and skillpay.me — verify these endpoints are expected and safe. 3) Several files hardcode a local model path (/Users/a/...), so running CLI entrypoints might read local files — run the skill in a sandbox and inspect what files it opens. 4) Ask the publisher for provenance (homepage, source repo, author identity) and why billing is implemented inline. 5) If you test it, do so in an isolated environment with no access to your production secrets or wallets, and monitor outbound network traffic. If the author cannot justify the embedded billing key or the undeclared dependencies/endpoints, do not install or run the skill.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

📊 Clawdis
OSmacOS · Linux · Windows
aivk975wjd2nx89ehphftg4z68dnd82g52ylatestvk975wjd2nx89ehphftg4z68dnd82g52ymulticoinvk975wjd2nx89ehphftg4z68dnd82g52yquantvk975wjd2nx89ehphftg4z68dnd82g52ytradingvk975wjd2nx89ehphftg4z68dnd82g52y
393downloads
0stars
2versions
Updated 3m ago
v1.1.0
MIT-0
macOS, Linux, Windows

Quant Orchestrator AI

📊 Description

多Agent量化系统,支持多币种预测和策略模板

💰 Pricing

  • 0.1 USDC per call

🚀 Features

1. 多币种预测 (8 coins)

  • BTC, ETH, SOL, XRP, DOGE, LINK, ADA, AVAX, DOT

2. 多模型投票

  • 3个模型投票,更稳定

3. 策略模板 (10个)

  • momentum
  • mean_reversion
  • breakout
  • rsi_extreme
  • macd_cross
  • bollinger_bounce
  • volume_spike
  • trend_following
  • support_resistance
  • volatility_expansion

4. AI功能

  • AI因子挖掘
  • AI策略生成
  • 自动回测

Usage

from skill_v2 import MultiCoinPredictor, get_strategy_templates

# Get predictions for all coins
predictor = MultiCoinPredictor()
results = predictor.run_all(model_paths)

# Get strategy templates
templates = get_strategy_templates()

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