Skills 导航

聚焦生产级数据工程，覆盖 ETL/ELT、批处理与流式管道、数据建模、Airflow/dbt/Spark 优化和数据质量治理，适合设计数据架构、搭建现代数据栈与排查性能问题。

✎ 复杂数据管道、ETL/ELT 和治理难题交给它，凭 Spark、Airflow、dbt 等现代数据栈经验，能更稳地搭起可扩展的数据基础设施。

覆盖Prompt优化、Few-shot设计、结构化输出、RAG评测与Agent工作流编排，适合分析token成本、评估LLM输出质量，并搭建可落地的AI智能体系统。

✎ 把提示优化、LLM评测到RAG与智能体设计串成一套方法，适合想系统提升AI开发效率的人。

覆盖模型部署、MLOps 流水线、特征库、漂移监控与 RAG/LLM 集成，适合把训练好的模型稳定上线，并持续优化性能、成本与迭代效率。

✎ 想把模型真正跑进生产环境，ML工程师把部署、MLOps、RAG到漂移监控串成完整闭环，连成本优化都替你想到。

Programmatic backtesting framework for trading strategies. Runs backtests with historical price data (yfinance or CSV), supports momentum/mean-reversion/factor/signal-based strategies, walk-forward optimization, out-of-sample testing, transaction cost modeling, regime-aware splits, and full performance metrics (Sharpe, Sortino, Calmar, max drawdown, CAGR, win rate, profit factor). Distinct from einstein-research-backtest (which provides methodology guidance). Use when a user wants to actually run a backtest, test a specific strategy on historical data, or generate performance metrics.

Write high-performing, persuasive, and authentic LinkedIn posts across any professional niche. Uses research-backed hooks, proven post structures, and LinkedIn-specific formatting to maximize reach, saves, comments, and profile actions. Use when creating LinkedIn posts, carousels-by-text, thought leadership, founder updates, case studies, hiring posts, launch posts, or converting X/Twitter ideas into LinkedIn content.

Multi-channel notification delivery with customizable templates, email and SMS sending, and full delivery logging — built for AI agents.

Event tracking, funnel analysis, cohort retention, multi-touch attribution, revenue metrics, and real-time analytics — built for AI agents.

聚焦高级 Prompt 设计与优化，覆盖 few-shot、chain-of-thought、模板体系和 system prompt，适合为复杂推理任务构建可复用、可评估、可上线的生产级提示方案。