VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills/tree/main/skills/vectorbt-expertVectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
This repo contains 5 individual skills — each has its own dedicated page.
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
Quickly fetch data and print key backtest stats for a symbol with a default EMA crossover strategy. No file creation needed - runs inline in a notebook cell or prints to console.
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
Self-paced tutorials that review key data literacy concepts and data analysis skills. Published materials can be found at:
Export only the text messages you sent from local WeChat databases on macOS. Codex + Claude Code Skill, verified on WeChat 4.1.10 arm64.
SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility, search optimization, search ranking, AI visibility, ChatGPT ranking, Google AI Overview, indexing, JSON-LD, meta tags, or keyword research.
本地运行的多 Agent 编排内核（Go/Windows）：PlanAgent→BehaviorAgent 主链，支持 MCP（stdio/http）、外挂 Skill Pack、WebUI（HTTP+SSE）、RunView 回合日志→HTML。/A local multi-agent runtime (Go/Windows) built around a PlanAgent→BehaviorAgent orchestration chain, with MCP (stdio/http), external Skill Packs, a Web UI (HTTP+SSE), and a sidecar RunView (turn logs → HTML).
Agent skill repository discovered by 10x-chat research.
Use when the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.