by pseudo-longinus · Agent Tool · ★ 199
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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.
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.
| Stars | 199 |
| Forks | 26 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 62/100 |
| Last Updated | 2026-11-05 |
| Created | 2026-04-21 |
| Platforms | claude-code, python |
| Est. Tokens | ~17k |
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...
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A股数据分析、量化选股与模拟交易 Skill 集合,支持实时行情、历史K线、技术指标、自定义交易策略与模拟买入、卖出交易账户
A collection of AI agent skills for Clawdbot, Claude Code, Codex
103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf,
Install, update and manage AI agent skills across 45 coding agents from one CLI. Sources: marketplace, GitHub,
迅投 QMT 量化 AI 技能集(Agent Skills):研报因子回测脚本生成等,适用于 Claude Code / Cursor / Codex / Kimi 等 70+ AI 编程工具
AI agent skill for generating quantitative strategy code on JoinQuant platform - Cursor / Claude Code compatib
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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: