quant-buddy-skills: 宽宝 QuantBuddy: 一整套面向Agent Invesment投资研究与决策的底层设施与能力的调用skill

by pseudo-longinus · Agent Tool · ★ 199

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install quant-buddy-skills

npx skills add pseudo-longinus/quant-buddy-skills -g -a claude-code -s quant-buddy-skill -y
npx skills add pseudo-longinus/quant-buddy-skills -g -s quant-buddy-skill -a claude-code -a cursor -y

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

a-shareagent-skillsbacktestingclaude-codecursorfactor-analysisgithub-copilotmarket-dataquantquantbuddy

Quick Facts

Stars199
Forks26
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score62/100
Last Updated2026-11-05
Created2026-04-21
Platformsclaude-code, python
Est. Tokens~17k

About quant-buddy-skills

quant-buddy-skills 中文 · English · 官网 · 新手教程 🔥 3 秒快速安装 如果你熟悉 Agent 工具(Claude Code、Cursor、OpenClaw 等),可以直接对 AI Agent 说: 帮我安装这个 skill: 如果你不懂如何使用 Agent 和 skill,可以按照小白图文教程一步步展开。 让 AI Agent(智能代理)直接在全 A 股上跑公式、选股、因子和回测。 A-share quant execution layer for Claude Code、Cursor、Codex、GitHub Copilot、Windsurf 等 AI Agent。 quant-buddy-skills 不是普通股票数据 API(应用程序接口)。它把行情、估值、财务、公式引擎、全市场筛选、因子计算、策略回测、净值对比和图表渲染封装成 AI...

Compatible Skills

These tools work well together with quant-buddy-skills for enhanced workflows:

  • hhxg-top-hhxg-python — semantic(0.43)+complementary+rare_topics+same_lang+similar_pop+shared_platform (74%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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