Miller (mlr) is a command-line data processing tool for structured text formats including CSV, TSV, JSON, JSON Lines, YAML, DKVP, and others. It operates as a streaming pipeline: input records are read from files or stdin, processed through a chain of transformer verbs (e.g., cut, sort, join, stats1, put, filter), and written to output in any supported format. The tool includes a domain-specific language (DSL) for record-level transformations, with support for user-defined functions, subroutines, loops, and higher-order functions. Miller is designed for performance, using Go channels for concurrent streaming, batch processing, and a slab allocator for efficient memory use. The architecture is a monolithic CLI application written in Go with no external runtime dependencies. Key entry points include the main binary (cmd/mlr/main.go), the entrypoint dispatcher (pkg/entrypoint), and the terminal subcommands (help, repl, script, regtest, mcp). The codebase is organized into packages for input readers, output writers, transformers (verbs), DSL parsing and execution, CLI flag handling, and a core value type system (Mlrval). Miller also provides an MCP (Model Context Protocol) server for AI agent integration, shell completion scripts, and a REPL for interactive use. Miller is not a research project; it is a production-grade data munging tool comparable to awk, sed, cut, join, and sort but for name-indexed data. Its primary data flows involve reading heterogeneous records, applying transformations via verbs or DSL expressions, and writing results. The tool handles compressed files, in-place editing, and output redirection to files or pipes.
This repository is a comprehensive data dump of Grand Theft Auto V (GTA V) game assets, structured as a reference library for modding, scripting, and reverse engineering. It contains no executable application logic; instead, it provides thousands of JSON, INI, and TXT files detailing internal game constants, entity flags, vehicle definitions, pedestrian data, weapon configurations, and precise spatial coordinates for world objects. The data is extracted from the RAGE engine binaries and enhanced with community research, specifically targeting version v3717.0 (Online 1.72). It serves as a critical resource for FiveM server administrators, mod developers, and game analysts who need to understand the game's internal data structures without access to the source code. The repository is organized into logical categories: core entity lists (vehicles, peds, weapons), world object placement data (objectslocations/), audio event mappings, and rendering/animation flags. While the data itself is static and version-locked, the structure is highly consistent, using standard JSON schemas for spatial data (Position/Rotation) and flat arrays for enumerations. The primary value lies in the 'Data Truth Layer'โthe ground truth of internal game names and hashes that are otherwise undocumented. However, the data is inherently volatile; any official game patch that alters internal hashes or map geometry will invalidate specific entries, requiring regeneration of the dumps. For AI agents and engineers, this repository is a high-fidelity knowledge base for GTA V modding. It is not a tool to be run, but a dataset to be consumed. The quality is exceptional for a community-maintained data dump, with consistent naming conventions and comprehensive coverage of game systems. The lack of code means there is no technical debt in terms of software engineering, but there is significant drift risk regarding game version compatibility.
This repository provides a JSON dataset of world cities and their administrative divisions (levels 1 and 2) sourced from the GeoNames Gazetteer. The data includes city names, coordinates, country codes, and administrative codes for over 169,000 cities. The repository features automated monthly updates via GitHub Actions that download raw GeoNames data, convert it to JSON format, and validate the output. The data structure enables direct use in geolocation applications, administrative boundary mapping, and demographic analysis without additional processing.
This is the largest structured Chinese classical poetry dataset on GitHub, containing ~55,000 Tang poems, ~260,000 Song poems, ~21,000 Song ci (lyric poetry), and additional classical texts (Four Books and Five Classics, Book of Songs, Chu Ci, Yuan opera, etc.). The data is distributed as JSON files organized by dynasty and genre, with each poem containing author, title, and paragraph fields. The repository serves as a foundational dataset for Chinese NLP, digital humanities, and cultural heritage projects. The codebase is minimal โ essentially a data repository with a JSON validation test (test_poetry.py), a simple data loader (loader/data_loader.py), and a web scraper for Song ci (ๅฎ่ฏ/UpdateCi.py). The data itself is the product of web scraping from sources like ctext.org and other online repositories, with manual curation and error correction over time. The repository includes an 'error' directory containing records with data quality issues (corrupted characters, missing content, etc.), which demonstrates transparency about data limitations. The data loader provides a basic abstraction for extracting poem bodies across different datasets, but the overall engineering is straightforward. The value lies entirely in the data breadth and accessibility โ no complex transformations, feature engineering, or analytical pipelines are present. The repository is widely used (52K+ stars) as a reference dataset for Chinese poetry generation, analysis, and educational applications.