This guide covers the full lifecycle of building, running, and serving a custom OpenBB Workspace application from an extension project scaffolded by openbb-cookiecutter. It assumes the project shell already exists (see the developextension skill for scaffolding instructions).
This is a complete guide for creating a new OpenBB Platform extension from scratch. Follow every phase in order. When the user says "build me an application that does X", use this guide to scaffold, implement, install, and verify the extension.
This guide explains how to call tools, interpret responses, discover capabilities, use prompts, and handle errors when interacting with an OpenBB MCP server.
An A-share China stock risk checker that forecasts whether a company may receive an ST or *ST warning in the next financial year. ST labels are Chinese exchange warnings for companies facing specified financial or regulatory problems.
A guide to analysing bonds and other fixed-income investments, including issuer credit quality, interest payments, default risk, credit spreads, and convertible bonds. It also covers Chinese fixed-income markets and local-government financing bonds.
Bulk PR reviewer for awesome-quant. Use when the user asks to review all open PRs, review unreviewed PRs, bulk review, or mentions "bprr". Reviews open PRs lacking the reviewed label and presents a summary before any merge/comment/label action.
Single PR reviewer for awesome-quant. Use when the user asks to review, validate, comment on, label, close, or merge one specific pull request that adds README.md entries. Triggers include "sprr", "review PR", "check PR", and "validate contribution".
Refresh tracked PyPI last-updated dates in awesome-quant README.md. Use when the user asks to update PyPI dates, refresh PyPI metadata, or run update-pypi-dates.
QuantDinger repo workflow for coding agents: layered contracts, safety boundaries, and where backend, strategies, and Docker live. Use when editing Python API, strategies, deployment, or docs/agent.
Contributor workflow for the QuantMind codebase. Covers contributor setup (environment + hooks), filing issues, commit format, pull request format, and component development across quantmind/ modules (etl, knowledge, configs, preprocess, rag, flows, mind, utils) with tests, examples, and verification. Use when setting…
A collection of tools for retrieving financial and economic data from Chinese, Hong Kong, and United States markets. It covers items such as stock lists, prices, charts, company financial statements, funds, indexes, bonds, news, and macroeconomic data.
A tool for retrieving daily valuation data for major Chinese A-share market indexes, such as price-to-earnings ratios, market value, and closing levels.
A lookup for daily valuation figures on Chinese A-share stocks, such as price-to-earnings, price-to-book, market value and closing price. It can return one stock, a date range, one trading day, or the whole market.
Run Pine Script indicators from the command line using pinets-cli. Use when the user asks to run, execute, test, or analyze a Pine Script indicator, calculate technical analysis values, fetch market data for crypto symbols, or work with .pine files. Also use when the user wants RSI, SMA, EMA, MACD, Bollinger Bands, or…
Run Pine Script indicators from the command line using pinets-cli. Use when asked to execute, test, or analyze Pine Script indicators, calculate technical analysis values (RSI, SMA, EMA, MACD, Bollinger Bands, etc.), fetch market data for crypto trading pairs. This tool can run PineScript indicators from .pine files…
Run Pine Script indicators from the command line using pinets-cli. Use when asked to execute, test, or analyze Pine Script indicators, calculate technical analysis values (RSI, SMA, EMA, MACD, etc.), or fetch market data for crypto trading pairs. This tool can run PineScript indicators from .pine files or stdin and…
A tool for turning quantitative trading requirements into strategy code, testing that code against historical data, and refining it. Quantitative trading uses rules and data to make investment decisions; backtesting checks those rules against past market data.
A Chinese-language AI investment research workflow covering industry-chain research, company analysis, valuation, multi-agent debate, and quantitative backtesting for Chinese, Hong Kong, and US stocks.
Full-stack equity research report generator built on the Anamnesis Pattern (cross-session institutional memory + scheduled adversarial review). Trigger when the user wants to analyze a company, generate an equity research report, fundamental analysis, or stock investment research. Works with a company name (web…
Build, smoke-test, publish, and verify the Q-agent Docker image (LEAN CLI + infrastructure pipelines + marimo). Handles LEANVERSION bumps, local iteration, GHCR pull verification, and CI debugging for the docker workflow.
Run a local QuantConnect research notebook in the correct LEAN Docker environment, inspect executed outputs, extract rendered charts, and interpret the results. Use when the user asks to run, debug, validate, or explain a notebook under MyProjects//research/.ipynb.