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github/sql-code-review

通用 SQL 代码审查助手,对所有 SQL 数据库(MySQL、PostgreSQL、SQL Server、Oracle)进行全面的安全性、可维护性和代码质量分析。专注于 SQL 注入预防、访问控制、代码规范和反模式检测。与 SQL 优化提示相辅相成,提供完整的开发覆盖。

sql-code-review 是什么?

这是一个通用 SQL 代码审查技能,专为开发者工具(如 Claude Code、Cursor、Codex)设计。它能够自动分析 SQL 查询和数据库代码,识别安全问题(如 SQL 注入、权限检查缺失)、可维护性问题(未使用索引、复杂子查询)以及违反代码规范的行为(命名约定、格式)。该技能支持所有主流 SQL 数据库(MySQL、PostgreSQL、SQL Server、Oracle),通过静态分析和模式匹配检测常见的反模式,并提供改进建议。它与 SQL 优化提示互补,但不重复优化工作。开发者可以直接调用此技能来审查 Pull Request 或新代码片段,从而在开发早期捕获潜在缺陷。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/sql-code-review

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文档

sql-code-review 是做什么的?

通用 SQL 代码审查助手,对所有 SQL 数据库(MySQL、PostgreSQL、SQL Server、Oracle)进行全面的安全性、可维护性和代码质量分析。专注于 SQL 注入预防、访问控制、代码规范和反模式检测。与 SQL 优化提示相辅相成,提供完整的开发覆盖。

功能

  • 安全性分析:检测 SQL 注入风险、未参数化的查询、不安全的动态 SQL 拼接、缺失的输入验证。
  • 访问控制审查:检查权限使用是否合理(如 GRANT/REVOKE 语句)、角色管理、行级安全策略。
  • 代码质量:识别反模式,例如未使用索引的列、过度使用的子查询、错误的连接类型、未处理的 NULL 值。
  • 规范性检查:验证命名约定(表名、列名、别名)、关键词大小写、缩进和注释规范(匹配常见 Style Guide,如 SQL Style Guide)。
  • 兼容性警告:如果代码混合了数据库特定语法(如 MySQL 的 LIMIT 在 SQL Server 中不兼容),会发出警告。

使用场景

  • 开发者在 VS Code 或 JetBrains IDE 中编写 SQL 时,通过 AI 助手自动触发审查。
  • CI/CD 管道中审查每个 SQL 提交的 PR。
  • 学习 SQL 最佳实践的新手作为教育工具。

用法

-- 示例:带 SQL 注入风险的查询
SELECT * FROM users WHERE username = 'admin' OR '1'='1';

审查输出将指出:不安全字符串拼接、建议参数化查询。

局限性

  • 不执行查询性能优化(使用 SQL 优化提示来填充)。
  • 不连接实际数据库——仅静态分析。
  • 可能误判上下文特定模式(如 ORM 生成的代码)。

技术

  • 语言:Markdown 格式输出,兼容所有 AI 代理。
  • 数据库:MySQL、PostgreSQL、SQL Server、Oracle(通用语法)。
  • 依赖:无外部依赖——本地正则和 AST 基础分析。

许可证

MIT

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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