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Found 739 Skills
Alibaba Cloud SLS (Simple Log Service) log query & analysis skill. Use this skill to help users write, explain, optimize, execute, or troubleshoot SLS index search, SQL analytics, and SPL scan/pipeline statements through the aliyun CLI. Triggers: "SLS 查询", "SLS 分析", "日志查询", "日志分析", "log query", "analyze sls logs", "aliyun log query".
Use these skills when you need to provision new Cloud SQL instances, create databases and users, clone existing environments, and monitor the progress of long-running operations.
Provides MaxCompute SQL intelligent generation capabilities for AI agents, covering text2sql conversion principles, dialect syntax differences (DQL/DDL/DML), common query pattern templates (Top N, PIVOT, window functions, etc.), and ODPS error code diagnostics. Use when generating, debugging, or migrating MaxCompute / ODPS SQL.
End-to-end data engineering pipeline using Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization
PostgreSQL and Drizzle ORM best practices. Triggers on: PostgreSQL, Postgres, Drizzle, database, schema, tables, columns, indexes, queries, migrations, ORM, relations, joins, transactions, SQL, drizzle-kit, connection pooling, N+1, JSONB, RLS. Use when: writing database schemas, queries, migrations, or any database-related code. Proactively apply when creating APIs, backends, or data models.
Identifies and fixes XSS, SQL injection, and command injection vulnerabilities with validation schemas, sanitization libraries, and safe coding patterns. Use for "input validation", "XSS prevention", "SQL injection", or "sanitization".
Professional Skills and Methodologies for SQL Injection Testing
Python backend implementation patterns for FastAPI applications with SQLAlchemy 2.0, Pydantic v2, and async patterns. Use during the implementation phase when creating or modifying FastAPI endpoints, Pydantic models, SQLAlchemy models, service layers, or repository classes. Covers async session management, dependency injection via Depends(), layered error handling, and Alembic migrations. Does NOT cover testing (use pytest-patterns), deployment (use deployment-pipeline), or FastAPI framework mechanics like middleware and WebSockets (use fastapi-patterns).
Use when working with SQLiteData library (@Table, @FetchAll, @FetchOne macros) for SQLite persistence, queries, writes, migrations, or CloudKit private database sync.
Collect app events via evalpkgs into sqlite, then filter/report capture_results to Feishu Bitable with retry-safe writeback. Use for collect-start/collect-stop/filter/report/retry-reset workflows.
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
Enforces Rust language and module standards for maintainable codebases. Use when writing Rust code, structuring modules, separating SQL/prompts from code, and enforcing one-thing-per-file discipline.