Search Results for "linux file parser"

a-stock-data is a self-contained AI agent skill for accessing Chinese A-share market data through direct HTTP data sources. It packages data access logic into a structured skill file so coding assistants can retrieve and analyze stock information without relying on separate third-party wrapper libraries. The project organizes data across multiple layers, including market quotes, research reports, signals, capital flows, news, announcements, macro data, and financial statements. It is...

design.md is an open specification created by Google Labs that defines a standardized way to describe design systems for AI coding agents. It allows developers to encode visual identity elements such as colors, typography, spacing, and components in a structured format. The file combines machine-readable design tokens with human-readable explanations, enabling agents to generate consistent user interfaces aligned with a brand. By providing persistent design context, it eliminates the need to...

Context Engineering Template is a comprehensive template and workflow repository designed to teach and implement context engineering, a structured approach to preparing and organizing the information necessary for AI coding assistants to complete complex tasks reliably. Instead of relying solely on short prompts, this project encourages developers to create rich, structured context files that include project rules, examples, and validation criteria so that AI systems can act more like...

MiniMax-M2 is an open-weight large language model designed specifically for high-end coding and agentic workflows while staying compact and efficient. It uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only 10 billion activated per token, giving it the behavior of a very large model at a fraction of the runtime cost. The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real...

Claude Code Architecture Study is an educational and experimental repository designed to teach developers how to build, configure, and understand AI coding agents from first principles. The project focuses on breaking down the architecture of agentic systems, including how models perceive context, make decisions, and execute actions in a coding environment. It likely provides step-by-step examples, conceptual explanations, and practical implementations that guide users through creating their...