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 is an automated trading bot for Polymarket prediction markets that buys NO outcomes in standalone yes/no markets below a price threshold. The application features a modular architecture with separate components for strategy execution, exchange interaction, risk management, state persistence, and web-based monitoring. It continuously scans for eligible markets, places orders when conditions are met, manages positions, and implements recovery mechanisms for handling failed orders. The system can operate in paper trading mode or live trading mode with appropriate configuration.
PMXT is a unified prediction market SDK that aggregates data and trading across 15+ venues (Polymarket, Kalshi, Limitless, etc.) into a single, consistent interface. The system provides a core TypeScript library with a local sidecar server (Express), plus TypeScript, Python, and CLI SDKs that support both self-hosted and hosted (trade.pmxt.dev) modes. It offers market data (events, order books, trades, OHLCV), WebSocket streaming, order management (create, cancel, fetch), account balance and positions, cross-venue router for event matching and arbitrage, and data feeds (Binance, Chainlink). The architecture is a monorepo with workspaces core, sdks/typescript, sdks/python, sdks/cli, and tools. Each exchange follows a clean fetcher → normalizer → error mapper pattern, and the SDK surfaces are partially generated from the core's method signatures. While primarily an application SDK, the project includes significant data normalization, cross-venue data aggregation, and a router for analytical queries, giving it a hybrid character.
This repository implements a comprehensive backtesting and live-trading framework for prediction markets (specifically Polymarket and Kalshi), built on a heavily customized NautilusTrader engine. It provides historical replay of L2 order book data from multiple vendors (Telonex, PMXT, Polymarket native), realistic execution modeling (fill models, fee models with maker rebate inference), and a library of trading strategies (e.g., EMA crossover, microprice imbalance, late favorite hold). The framework supports parameter search optimization (random grid, TPE via optuna), joint-portfolio multi-market backtests, and generates detailed HTML/CSV/JSON reports with equity curves, drawdown, and Brier advantage analysis. A Rust native extension accelerates data conversion (book diffing, payload sorting), and a CLI TUI (main.py) discovers and runs backtest entrypoints. While primarily an application framework, it also includes numerous one-off research scripts and Jupyter notebooks (e.g., BTC snapshot model walkforward), giving it a hybrid character: a production-quality backtesting infrastructure with a heavy research emphasis. The codebase is well-tested (50+ test files), documented, and follows clean engineering practices (config validation, structured event logging, concurrency patterns). However, it is tightly coupled to NautilusTrader internals, relies on several environment variables, and contains large monolithic loader classes (e.g., Telonex loader at ~3600 lines) that limit reusability outside its specific domain. Maintenance appears active (last commit 2026-05-16) with low technical drift, though the heavy dependency on NautilusTrader and optional Rust build are potential friction points.
Gabagool is a Python-based arbitrage trading bot for Polymarket's 15-minute prediction markets, with performance-critical components implemented in Rust via PyO3 extensions. The bot implements the 'Gabagool arbitrage' strategy: buying both YES and NO shares when their combined cost is less than $1.00 to guarantee profit regardless of market outcome. The architecture features a modular design separating concerns into API clients (Gamma and CLOB), trading strategy, position tracking, risk management, and persistence layers. Key components include a thread-local HTTP session manager to prevent connection pool issues, a SQLite-based persistence layer for trade and position data, and a thread-safe position tracker with expiration mechanisms. The system supports multiple operational modes including live trading, paper trading simulation, and backtesting against historical data. Recent development activity shows active maintenance with ongoing Rust integration for performance optimization.