What does finance-quant-skills do?
一个面向金融量化交易领域的 Agent Skills 技能维护仓库，主要聚焦A股量化交易。
A repository of agent skills for financial quantitative trading, focused on A-share (Chinese stock market) quantitative analysis.
The lzwme/finance-quant-skills repository provides a collection of specialized agent skills tailored for financial quantitative trading, with a primary focus on the Chinese A-share stock market. It enables AI agents like Claude Code, Cursor, or Codex to access and execute trading-related tasks such as data retrieval, strategy backtesting, signal generation, and market analysis. By leveraging these pre-built skills, developers and quantitative analysts can automate complex workflows, integrate with popular trading APIs, and accelerate research in algorithmic trading. The repository is designed to be modular and extensible, making it a valuable resource for both beginners and experienced quants working in the China equity markets.
npx skills add lzwme/finance-quant-skillsInstalled? Explore more Research & Data Analysis skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
一个面向金融量化交易领域的 Agent Skills 技能维护仓库，主要聚焦A股量化交易。
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.