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github/postgresql-optimization

专注于 PostgreSQL 独特功能、高级数据类型和专属能力的开发助手,涵盖 JSONB 操作、数组类型、自定义类型、范围/几何类型、全文搜索、窗口函数以及扩展生态系统。

postgresql-optimization 是什么?

该技能是一个面向 PostgreSQL 开发者的专业助手,专注于利用 PostgreSQL 独有的特性和高级数据类型来优化数据库查询与架构设计。它涵盖了 JSONB 的高效操作、数组和自定义类型的灵活使用、范围与几何类型的空间数据处理、全文搜索的实现、窗口函数的分析计算,以及 PostgreSQL 扩展生态系统的集成。支持在 Claude Code、Cursor 等 AI 编码代理中作为上下文工具,帮助开发者通过自然语言交互深入探索 PostgreSQL 的进阶功能。其 SKILL.md 文档详细描述了各项能力的用法与最佳实践,使 AI 能够提供精准的代码示例和优化建议,从而提升数据库应用的性能与可维护性。

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

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文档

postgresql-optimization 是做什么的?

PostgreSQL 专属开发助手,专注于利用 PostgreSQL 的独特功能、高级数据类型和专属能力来优化开发流程。涵盖 JSONB 操作、数组类型、自定义类型、范围/几何类型、全文搜索、窗口函数以及 PostgreSQL 扩展生态系统。

核心能力

JSONB 操作

  • 使用 ->, ->>, #>, #>> 等运算符进行 JSONB 数据的高效查询与路径遍历。
  • 索引优化:利用 GIN 索引加速 JSONB 字段的搜索(如 gin 或 jsonb_path_ops)。
  • 支持修改函数:jsonb_set()、jsonb_insert() 和 jsonb_delete()。

数组与自定义类型

  • 数组类型的创建与查询(如 text[]、integer[])。
  • 使用 ANY、ALL 和数组运算符进行集合操作。
  • 自定义类型的定义(CREATE TYPE)及其在函数中的使用。

范围与几何类型

  • 范围类型(如 int4range、tsrange)用于有效区间查询。
  • 几何类型(如 point、path、polygon)支持空间计算。
  • 索引推荐:SP-GiST 和 GiST 索引适用于范围与几何数据。

全文搜索

  • 使用 to_tsvector() 和 to_tsquery() 实现词法搜索。
  • 配置文本搜索字典(如 english、simple)。
  • 排名与高亮:ts_rank() 和 ts_headline() 函数。

窗口函数

  • 聚合窗口:ROW_NUMBER()、RANK()、DENSE_RANK()。
  • 滑动窗口:SUM()、AVG() 等聚合函数结合 ROWS BETWEEN。
  • 分页优化:使用 ROW_NUMBER() 配合 WHERE 子句替代 OFFSET。

PostgreSQL 扩展生态系统

  • 常用扩展:pg_trgm(模糊字符串匹配)、postgis(地理空间)、hstore(键值存储)。
  • 安装与激活:CREATE EXTENSION IF NOT EXISTS。
  • 性能与兼容性评估。

使用方式

在 AI 编码代理(如 Claude Code 或 Cursor)中调用本技能,描述您的 PostgreSQL 查询或架构优化需求。例如:

  • “使用 JSONB 优化存储用户偏好的模式。”
  • “为标签列创建 GIN 索引以加速文本搜索。”
  • “设计一个基于范围类型的事件调度系统。”
  • “使用窗口函数计算月度销售滚动平均。”

限制

  • 本技能不涵盖标准 SQL 基础或通用关系数据库设计。
  • 不适用于非 PostgreSQL 数据库(如 MySQL、SQLite)。
  • 操作建议须结合具体数据分布和硬件环境测试。

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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