global-stock-data is a comprehensive financial data pipeline designed for AI coding assistants to access US and Hong Kong market data. It implements a 13-layer architecture that fetches data from 11 diverse sources including exchanges (CBOE), regulatory bodies (SEC, FINRA), financial data providers (Eastmoney, Yahoo Finance), and macroeconomic sources (Treasury, CFTC). The system normalizes data from various formats (JSON, CSV, text) into consistent schemas for real-time quotes, historical prices, technical indicators, fundamentals, options data, SEC filings, short volume, and macroeconomic indicators. Unlike a traditional application, it functions as a reusable data toolkit where each layer can be independently accessed to solve specific financial data acquisition problems.
This is a mature, actively maintained AI agent skill called 'last30days' that enables agents (Claude Code, Codex, Grok, Gemini CLI, MCP hosts) to research any topic by aggregating content from 20+ social and news platforms (Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, arXiv, Techmeme, Digg, and more) over a rolling 30-day window. The system is primarily a CLI engine (Python) with a companion Go MCP server for Claude Desktop. It employs a sophisticated pipeline: query planning by intent, parallel retrieval from multiple backends (keyless APIs, paid scrapers, CLI tools, LLM-based search), source-specific normalization, engagement-weighted relevance scoring, cross-source clustering, and deterministic fusion (weighted RRF) with per-author caps and diversity floors. Output is rendered as structured JSON, compact text, or self-contained HTML with optional publishing and Atom feeds. The codebase emphasizes security (credential hygiene, no browser cookie reads in unattended mode), determinism (fixture-based evaluation harness with grounding/recency/coherence scores), and graceful degradation (per-source fallback chains, never-raise error envelopes). While classified as an application (it's a skill/plugin for agent hosts), it exhibits strong data engineering traits: source adapters, normalization pipelines, scoring models, and an offline evaluation framework.
This repository implements a complete quantitative trading framework for A-shares, known as 'zettaranc-skill'. It is a dual-track project: (1) a Python/Rust software application providing CLI tools, a FastAPI REST API, and a React frontend for stock analysis, backtesting, simulation, screening, and strategy optimization; (2) an AI character-play skill pack that encodes a specific trading methodology (Zettaranc/Shaofu strategies) into a SKILL.md for LLM role-play. The application side dominates: it has 15+ CLI subcommands, a modular architecture with 30+ technical indicators, 20+ strategy detectors, a multi-source data layer (Tushare, Indevs, A-Stock-Data, Hithink, SQLite), a Rust-accelerated backtesting engine (via PyO3), a walk-forward simulation framework, and a self-optimization pipeline. The codebase is well-structured with clean abstractions (DataSource protocol, unified error codes, parameter registration), extensive test coverage, and CI/CD. However, it suffers from some technical drift: dual Python/Rust implementations must stay synchronized, Chinese vernacular naming reduces readability, hardcoded constants persist, and the self-optimizer's LLM judge is a stub. Overall, it is a production-grade personal quant framework with high craftsmanship in its core components.
This repository is an AI skill/prompt library for coding assistants (Claude, Cursor, GitHub Copilot, etc.). It contains a comprehensive security-focused markdown guide (SKILL.md) that AI assistants reference when generating code to ensure secure coding practices. The skill covers 60+ security vulnerabilities including IDOR, XSS, CSRF, SSRF, SQL injection, JWT misconfigurations, and more. It's designed to be installed in AI assistant skill directories to provide real-time security guidance during code generation. The repository has minimal structure - just three files (LICENSE, README.md, SKILL.md) with no traditional software components like tests, CI/CD, or package management. This is appropriate for its purpose as a static documentation resource for AI assistants. The security guidance is comprehensive and well-organized, covering both prevention strategies and common bypass techniques attackers use. Note: The last commit date (2026-02-17) appears to be in the future, which is unusual. The repository has 0 open issues and 94 forks, suggesting active adoption despite the odd timestamp.