Explore skills

数仓 ETL 开发流程，适用于生成 ODPS/MaxCompute 建表 DDL、INSERT SQL 和 SR 建表语句，并配合 hooks 自动校验 SQL 规范。直接调用命令：/dw-etl。

数仓性能优化流程，适用于分析 ETL SQL 执行计划、分区裁剪、JOIN 策略、数据倾斜和资源瓶颈。直接调用命令：/dw-performance-optimization。

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

StarRocks/SR 导入设计流程，适用于根据 ODPS 表结构生成 SR 建表语句、同步任务配置和风险分析。直接调用命令：/dw-sr。

Procurement skill — vendor evaluation, spend analytics, contract renewals, cost optimisation. Use when tasks involve vendors, purchases, subscriptions, or cost management.

Expert-level architecture review and design guidance for Python backends, with deep knowledge of FastAPI, PostgreSQL, async patterns, and modern Python project structure. Use this skill whenever the user asks you to review Python code architecture, design a new Python backend, evaluate project structure, assess API design, review database schemas, audit security posture, or discuss scalability of

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI

/cs:ciso-review <plan> — Risk-paranoid interrogation of any plan that touches data, compliance, or production access.

Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — usi

Use when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpark Python, configuring dbt for Snowflake, or troubleshooting Snowflake errors.

Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan.