61项独立技能，教AI编码助手掌握《机器学习交易》工作流程，摘自《算法交易机器学习》第三版。

该库将本书的定量研究学科转化为编码代理的运行时指导。每一项技能都为代理提供了避免常见ML4T失败所需的模式：泄漏、前瞻偏差、过拟合回测、陈旧的数据连接、破损的交叉验证、不切实际的交易成本以及不安全的自主工作流。

每个技能都是带有 YAML 前置和简明过程指引的普通 SKILL.md 文件。安装它们意味着要将每个技能目录放在代理寻找技能的位置，scripts/install.sh 可以从克隆或解包的发布档案中查找技能。

下一场免费课程： 如何高效使用编码代理，超越代码，2026年9月30日星期三，上午11：00 ET / 15：00 UTC**的30分钟直播课程。所有课程、工作坊和免费课程。

为什么要用这些技能

编码代理擅长编写代码，但金融机器学习存在一些容易被忽视且后期发现成本高昂的失败模式。

ML4T Agent Skills

61 standalone skills that teach AI coding assistants the Machine Learning for Trading workflow from Machine Learning for Algorithmic Trading, 3rd Edition.

This repository turns the book’s quant research discipline into runtime guidance for coding agents. Each skill gives an agent the pattern it needs to avoid common ML4T failures: leakage, lookahead bias, overfit backtests, stale data joins, broken cross-validation, unrealistic transaction costs, and unsafe autonomous workflows.

Each skill is a plain SKILL.md file with YAML frontmatter and concise procedural guidance. Installing them means putting each individual skill directory where your agent looks for skills, which scripts/install.sh does from a clone or from an unpacked release archive.

Next free session: How to use AI Agents for Trading Strategy Research, a 30-minute live session on Wednesday, October 28, 2026, 11:00 AM ET / 15:00 UTC. All courses, workshops, and free lessons.

Why Use These Skills

Coding agents are strong at writing code, but financial ML has failure modes that are easy to miss and expensive to discover late. These skills package the reviewer-grade checks that should happen before a strategy result is trusted:

• Method discipline: every skill teaches a concept-first WRONG/CORRECT pattern.
• Book alignment: skills map to the chapters and case-study workflow in Machine Learning for Algorithmic Trading.
• Production handoff: stable patterns point to the corresponding ml4t-data, ml4t-engineer, ml4t-backtest, ml4t-diagnostic, or ml4t-live API.
• Agent portability: the files are standard Markdown and can be used by Claude Code, Codex/OpenAI agents, or any tool that can discover and read local skill files.

Installation

The canonical distribution is this Git repository. There is no npm package, Python package, or build step, because the runtime artifact is the Markdown file itself.

Agents discover skills one directory level deep, at //SKILL.md. This repository groups skills into category directories so it can be read, so cloning it into a skills directory as a single folder will not work: the SKILL.md files end up three levels down and nothing finds them. scripts/install.sh flattens the categories for you.

git clone https://github.com/ml4t/skills.git ~/.ml4t-skills
cd ~/.ml4t-skills

./scripts/install.sh                    # ~/.claude/skills, the default
./scripts/install.sh .agents/skills     # Codex, project-local
./scripts/install.sh ~/.claude/skills --copy   # copies instead of symlinking