docs: 更新文档和技能

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## Project Structure & Module Organization ## Project Structure & Module Organization
- 根目录:`README.md` 给出全貌,`Makefile` 封装日常命令,`CONTRIBUTING.md` 说明贡献流程,`LICENSE` 载明协议。保持根目录扁平,避免巨石文件。 - 根目录:`README.md` 给出全貌,`Makefile` 封装日常命令,`CONTRIBUTING.md` 说明贡献流程,`LICENSE` 载明协议。保持根目录扁平,避免巨石文件。
- 多语言 i18n`i18n/<lang>/` 统一三层结构documents / prompts / skills。现有语言中文 `zh`、英文 `en`、希伯来语 `he`,以及高频/常用的 `es`、`hi`、`ar`、`pt`、`ru`、`fr`、`de`、`ja`、`ko`、`it`、`tr`、`nl`、`pl`、`id`、`vi`、`th`、`fa`、`uk`、`bn`、`ta`、`ur`、`ms`、`sw`、`ha`;新增语言遵循同样层级。 - 多语言 i18n`i18n/<lang>/` 统一三层结构documents / prompts / skills。现有语言中文 `zh`、英文 `en`、希伯来语 `he`,以及 `es`、`hi`、`ar`、`pt`、`ru`、`fr`、`de`、`ja`、`ko`、`it`、`tr`、`nl`、`pl`、`id`、`vi`、`th`、`fa`、`uk`、`bn`、`ta`、`ur`、`ms`、`sw`、`ha`;新增语言遵循同样层级。
- 文档库:`i18n/zh/documents/` 仍是默认中文方法论入口(如 `i18n/zh/documents/模板与资源/代码组织.md` 等),新增语言文档按需在对应 `documents/` 下创建并保持同步。 - 文档库:`i18n/zh/documents/` 是默认中文方法论入口,含子目录:`方法论与原则/`、`模板与资源/`、`教程与指南/`、`外部资源聚合/`、`胶水编程/`、`从零开始vibecoding/` 等。
- 提示词资产:`i18n/zh/prompts/` 按角色拆分system / assistant / coding / user`libs/external/prompts-library/` 提供 Excel ↔ Markdown 互转工具与脚本目录,便于批量维护提示词,适合作为“单一真实来源”。 - 提示词资产:`i18n/zh/prompts/` 按角色拆分(`system_prompts/`、`assistant_prompts/`、`coding_prompts/`、`user_prompts/`、`meta_prompts/``libs/external/prompts-library/` 提供 Excel ↔ Markdown 互转工具。
- 代码与集成:`libs/` 预留核心实现骨架,`common/`、`database/`、`external/` 分别对应通用模型、存储适配与外部依赖登记;新增模块需保持分层边界与单一职责,避免跨层调用。 - 技能库:`i18n/zh/skills/` 包含模块化技能集,如 `ccxt/`、`postgresql/`、`telegram-dev/`、`claude-code-guide/`、`claude-skills/` 等 16+ 个技能目录。
- 备份:`backups/` 内含 `一键备份.sh``快速备份.py`,用于本地快照或同步,请先在隔离目录试跑,确认输出路径与权限。 - 代码与集成:`libs/` 预留核心实现骨架,`common/`(含 `models/`、`utils/`)、`database/`、`external/` 分别对应通用模型、存储适配与外部依赖。
- 外部工具:`libs/external/` 含 `prompts-library/`、`l10n-tool/`、`my-nvim/`、`MCPlayerTransfer/`、`XHS-image-to-PDF-conversion/` 等。
- 备份:`backups/` 内含 `一键备份.sh`、`快速备份.py` 和 `gz/` 存档目录。
- 脚本:`scripts/` 目录预留项目脚本。
- GitHub 配置:`.github/` 含 `ISSUE_TEMPLATE/`、`PULL_REQUEST_TEMPLATE.md`、`SECURITY.md`、`FUNDING.yml`。
## Build, Test, and Development Commands ## Build, Test, and Development Commands
- `make help`:列出所有 Make 目标,是新人快速上手的入口。 - `make help`:列出所有 Make 目标。
- `make lint`:使用 `markdownlint-cli` 校验全仓库 Markdown一旦新增文档请先跑通需本地 Node/npm 环境,可用 `npm install -g markdownlint-cli` 安装)。 - `make lint`:使用 `markdownlint-cli` 校验全仓库 Markdown。
- `make build` / `make test` / `make clean`:目前为占位,落地具体实现后务必更新脚本和说明;建议在 `Makefile` 旁补充注释并保持幂等,避免修改全局状态。 - `make build` / `make test` / `make clean`:目前为占位。
- 提示词转换:进入 `libs/external/prompts-library/` 后执行 `python main.py` 按交互提示进行转换,运行前请确认虚拟环境、依赖与输出目录,并在完成后检查生成 Markdown 是否符合 lint 规则。 - 提示词转换:`cd libs/external/prompts-library && python main.py`。
- 备份:`bash backups/一键备份.sh` 或 `python backups/快速备份.py`
## Coding Style & Naming Conventions ## Coding Style & Naming Conventions
- 文字层:文档、注释、日志使用中文;代码符号(函数 / 变量 / 模块)统一英文且语义直白,避免晦涩缩写 - 文字层:文档、注释、日志使用中文;代码符号统一英文且语义直白。
- 缩进与排版全仓保持空格缩进2 或 4 空格任选其一但混用);Markdown 列表、代码块与表格对齐清晰,行宽控制在 120 列内。Git diff 可读性优先。 - 缩进与排版全仓保持空格缩进2 或 4 空格不混用);行宽控制在 120 列内。
- 设计品味:优先消除分支与重复;函数力求单一职责且短小;命名遵循小写加中划线或下划线,不使用空格与特殊字符;跨模块接口保持稳定签名 - 设计品味:优先消除分支与重复;函数单一职责且短小;命名小写加中划线或下划线。
- 依赖管理:新增工具或库时记录安装方式、最小版本与来源,必要时在 `i18n/zh/documents/模板与资源/工具集.md` 或 README 中补充,并说明为何需要它(性能、兼容、功能) - 依赖管理:新增工具或库时记录安装方式、最小版本与来源。
## Testing Guidelines ## Testing Guidelines
- 当前无实测用例;引入代码时请至少提供最小可复现测试。推荐 Python 使用 `pytest`,文件命名 `test_*.py`,夹具精简可读,遵循 red-green-refactor 循环。 - 当前无实测用例;引入代码时请至少提供最小可复现测试。
- 文档与提示词改动:提交前运行 `make lint`;如转换脚本涉及数据,附带示例输入 / 输出说明或最小数据样例,确保可重复 - 文档与提示词改动:提交前运行 `make lint`
- 覆盖率基线由模块维护者设定;若暂无标准,确保主流程和边界条件均可被测试验证,必要时在 PR 描述中写明未覆盖的风险,并建议后续补测计划 - 覆盖率基线由模块维护者设定。
## Commit & Pull Request Guidelines ## Commit & Pull Request Guidelines
- Commit 建议遵循简化 Conventional Commits`feat|fix|docs|chore|refactor|test: scope summary`,一句话说明行为与范围;避免笼统的 “update” - Commit 遵循简化 Conventional Commits`feat|fix|docs|chore|refactor|test: scope summary`。
- PR 必填:变更摘要、动机或关联 Issue、测试与验证步骤(列出运行的命令与结果概览);涉及文档 / UI 的修改应附对比截图或链接,方便 reviewer 快速复核 - PR 必填:变更摘要、动机或关联 Issue、测试与验证步骤。
- 提交前清单:跑通 `make lint`若新增脚本 / 依赖,更新对应文档与 `Makefile` 目标;确认不携带临时文件或机密数据,并在描述中注明潜在风险或需要 reviewer 特别关注的点 - 提交前清单:跑通 `make lint`;更新对应文档与 `Makefile` 目标;确认不携带临时文件或机密数据。
## Security & Configuration Tips ## Security & Configuration Tips
- 运行备份或转换脚本前,确认输出目录不会覆盖私有数据;建议先在临时目录试跑并检查生成文件,必要时使用只读副本 - 运行备份或转换脚本前,确认输出目录不会覆盖私有数据。
- 外部依赖来源记录在 `libs/external/` 目录下,增减依赖时同步维护,保持可追溯;引入第三方脚本需标明许可证与来源。 - 外部依赖来源记录在 `libs/external/` 目录下,引入第三方脚本需标明许可证与来源。
## Architecture Overview & Workflow ## Architecture Overview & Workflow
- 工作流倡导「规划 → 上下文固定 → 分步实现 → 自测 → 复盘」,对应资产分别存放在 `i18n/zh/documents/`、`i18n/zh/prompts/`、`libs/` 与备份脚本中。保持单向数据流和清晰责任边界可以避免后期维护成本激增 - 工作流倡导「规划 → 上下文固定 → 分步实现 → 自测 → 复盘」。
- 设计决策与目录结构更新后,请同步修订本文件与相关文档,确保团队共享同一真相源,减少口头约定与隐式规则 - 设计决策与目录结构更新后,请同步修订本文件与相关文档。
--- ---
@ -50,146 +55,95 @@ This is the **Vibe Coding CN** repository, a workflow, toolset, and knowledge ba
## Key Commands ## Key Commands
### Prompt Library Management
```bash ```bash
# Enter the library directory # Prompt library conversion
cd libs/external/prompts-library cd libs/external/prompts-library && python3 main.py
# Run the interactive conversion tool # Lint all markdown files
python3 main.py
```
### Development & Maintenance
```bash
# Lint all markdown files in the repository
make lint make lint
# Create a full project backup (respects .gitignore) # Create a full project backup
bash backups/一键备份.sh bash backups/一键备份.sh
``` ```
## Architecture & Structure ## Architecture & Structure
### Core Directories ### Core Directories
- **`i18n/zh/prompts/`**: The core asset. A massive, well-organized library of prompts. - **`i18n/zh/prompts/`**: Core prompt library (`coding_prompts/`, `system_prompts/`, `user_prompts/`, `assistant_prompts/`, `meta_prompts/`)
- `coding_prompts/`, `system_prompts/`, `user_prompts/`, `meta_prompts/` - **`i18n/zh/skills/`**: Modular skills library (16+ skills including `ccxt`, `postgresql`, `telegram-dev`, `claude-skills`)
- **`i18n/zh/skills/`**: A modular library of skills for the AI, providing domain-specific knowledge for various tools like `ccxt`, `postgresql`, `telegram-dev`, - **`i18n/zh/documents/`**: Knowledge base (`方法论与原则/`, `模板与资源/`, `教程与指南/`, `胶水编程/`, `从零开始vibecoding/`)
etc. - **`libs/external/prompts-library/`**: Excel ↔ Markdown conversion tool
- **`i18n/zh/documents/`**: The project's knowledge base, containing methodology, principles, and guides. - **`libs/external/`**: External tools (`l10n-tool/`, `my-nvim/`, `MCPlayerTransfer/`)
- **`libs/external/prompts-library/`**: A Python-based tool for converting prompts between Excel and Markdown formats. - **`backups/`**: Backup scripts and archives
- **`backups/`**: Scripts for project backups. - **`scripts/`**: Project scripts placeholder
- **`libs/`**: Skeleton for shared Python library code.
### Key Technical Details ### Key Technical Details
1. **Prompt Organization**: Prompts use a `(row,col)_` prefix for categorization. 1. **Prompt Organization**: Prompts use `(row,col)_` prefix for categorization.
2. **Conversion Tool**: The `prompts-library` uses Python with `pandas` and `openpyxl`. 2. **Conversion Tool**: Uses Python with `pandas` and `openpyxl`.
3. **Documentation Standard**: User-facing documentation is in Chinese. Code, file names, and structure are in English. 3. **Documentation Standard**: User-facing docs in Chinese; code/filenames in English.
4. **Skills**: The `skills` directory provides context and knowledge for specific tools and domains, each with its own `SKILL.md`. 4. **Skills**: Each skill has its own `SKILL.md`.
## Development Workflow ## Development Workflow
When modifying this repository: 1. Follow existing prompt and skill categorization systems.
1. Follow the existing prompt and skill categorization systems. 2. Use `prompts-library` tool for prompt updates.
2. Use the `prompts-library` tool to maintain consistency when updating prompts. 3. Run `make lint` after Markdown changes.
3. Run `make lint` after changing any Markdown files. 4. Run backup before major refactoring.
4. Run a backup with `bash backups/一键备份.sh` before any major refactoring.
--- ---
# GEMINI.md - 项目上下文文档 (Project Context Document) # GEMINI.md - 项目上下文文档
## 项目概述 (Project Overview) ## 项目概述
`vibe-coding-cn` 项目旨在提供一个通过与 AI 结对编程实现“将想法变为现实”的终极工作流程。它强调“规划驱动”和“模块化”的核心理念,旨在避免 AI 失控导致的项目混乱。该项目不仅仅是一个代码库,更是一个全面的 AI 结对编程指南、庞大的提示词库和模块化的技能工具集,涵盖了从项目构思、技术选型、实施规划到具体开发、调试和扩展的全过程 `vibe-coding-cn` 是一个通过与 AI 结对编程实现"将想法变为现实"的终极工作流程。强调"规划驱动"和"模块化"核心理念
**核心理念:** 规划是核心,通过结构化、模块化的方式引导 AI确保项目可控、可维护。 ## 技术栈
## 技术栈 (Technology Stack) - **核心语言:** Python
- **CLI 交互:** `rich`, `InquirerPy`
- **数据处理:** `pandas`, `openpyxl`
- **配置管理:** `PyYAML`
- **文档规范:** `markdownlint-cli`
- **版本控制:** Git
- **自动化:** Makefile
本项目主要的技术栈和相关工具包括: ## 文件结构
* **核心语言:** Python (用于 `prompts-library` 工具和备份脚本)
* **CLI 交互:** `rich`, `InquirerPy` (用于 `prompts-library` 提供友好的命令行界面)
* **数据处理:** `pandas`, `openpyxl` (用于 `prompts-library` 处理 Excel 文件)
* **配置管理:** `PyYAML` (用于 `prompts-library` 的配置)
* **文档规范:** `markdownlint-cli` (用于 `Makefile` 中的 `lint` 任务)
* **版本控制:** Git
* **自动化:** Makefile
* **操作系统:** 兼容 Linux
## 主要功能与工作流程 (Key Features & Workflow)
1. **AI 提示词库 (`i18n/zh/prompts/`):**
* 一个极其庞大和精细分类的提示词集合,是项目的核心资产。
* `coding_prompts/`: 专注于编程和代码生成的提示词。
* `system_prompts/`: 用于设定 AI 行为和思维框架的系统级提示词。
* `user_prompts/`: 用户自定义或常用的提示词。
* `meta_prompts/`: 元提示词与提示工程辅助。
2. **提示词库管理工具 (`libs/external/prompts-library/`):**
* 提供 Python 工具 (`main.py`),用于在 Excel 工作簿 (`prompt_excel/`) 和 Markdown 文档 (`prompt_docs/`) 之间进行提示词的相互转换。
* 支持交互式和非交互式操作。
3. **技能库 (`i18n/zh/skills/`):**
* 一个模块化的技能集合,为 AI 提供了特定工具和领域的知识。
* 每个技能(如 `ccxt`, `postgresql`, `telegram-dev`)都包含独立的 `SKILL.md` 描述, 参考资料和脚本。
4. **项目备份工具 (`backups/`):**
* `快速备份.py` 脚本能根据 `.gitignore` 规则智能地打包项目文件为 `.tar.gz` 格式。
5. **知识库与文档 (`i18n/zh/documents/`):**
* 包含代码组织、开发经验、系统提示词构建原则、项目架构模板等各类文档。
6. **外部工具与个人配置 (`libs/external/`):**
* 存放非核心项目代码但有用的外部工具、个人配置或实验性代码。例如:`my-nvim/` (nvim 配置), `XHS-image-to-PDF-conversion/` (图片转PDF工具)。
## 文件结构 (File Structure)
``` ```
. .
├── .gitignore # Git 版本控制忽略文件配置 ├── .github/ # GitHub 配置 (Issue/PR 模板, SECURITY, FUNDING)
├── AGENTS.md # 面向 AI Agent 的贡献与行为准则 ├── AGENTS.md # AI Agent 行为准则
├── CLAUDE.md # 面向 Claude 模型上下文与指令。 ├── CLAUDE.md # Claude 模型上下文
├── CODE_OF_CONDUCT.md # 项目行为准则。 ├── GEMINI.md # Gemini 模型上下文
├── CONTRIBUTING.md # 贡献指南。 ├── CODE_OF_CONDUCT.md # 行为准则
├── GEMINI.md # 面向 Gemini 模型的上下文与指令 (本文档)。 ├── CONTRIBUTING.md # 贡献指南
├── LICENSE # 项目许可证。 ├── LICENSE # MIT 许可证
├── Makefile # 项目自动化脚本 (lint, backup 等)。 ├── Makefile # 自动化脚本
├── README.md # 项目主文档,包含项目概览、使用指南等。 ├── README.md # 项目主文档
├── i18n/ ├── i18n/ # 多语言资产 (29 种语言)
│ ├── zh/{documents,prompts,skills} # 中文主语料与方法论。 │ ├── zh/ # 中文主语料
│ ├── en/{documents,prompts,skills} # 英文版本资产。 │ │ ├── documents/ # 文档库 (方法论/模板/教程/胶水编程等)
│ ├── he/{documents,prompts,skills} # 希伯来语(以色列)。 │ │ ├── prompts/ # 提示词库 (system/coding/user/assistant/meta)
│ ├── es|hi|ar|pt|ru|fr|de|ja|ko|it|tr|nl|pl|id|vi|th|fa|uk|bn|ta|ur|ms|sw|ha/{documents,prompts,skills} # 其他常用语言骨架。 │ │ └── skills/ # 技能库 (16+ 技能)
│ ├── en/ # 英文版本
│ └── ... # 其他语言骨架
├── libs/ # 核心库代码。 ├── libs/ # 核心库代码
│ ├── common/ # 通用功能和工具库。 │ ├── common/ # 通用模块 (models/, utils/)
│ │ ├── __init__.py # Python 包初始化文件。 │ ├── database/ # 数据库模块
│ │ ├── models/ # 数据模型定义。 │ └── external/ # 外部工具
│ │ └── utils/ # 实用工具函数。 │ ├── prompts-library/ # Excel-Markdown 互转工具
│ ├── database/ # 数据库相关代码。 │ ├── l10n-tool/ # 多语言翻译脚本
│ └── external/ # 外部工具、个人配置或实验性代码。 │ ├── my-nvim/ # Neovim 配置
│ ├── prompts-library/ # 提示词库管理工具 (Excel-Markdown 互转)。 │ ├── MCPlayerTransfer/ # MC 玩家迁移工具
│ │ ├── main.py # 提示词库管理工具主程序。 │ └── XHS-image-to-PDF-conversion/
│ │ ├── requirements.txt # 工具依赖。
│ │ ├── prompt_excel/ # Excel 格式提示词。
│ │ ├── prompt_docs/ # Markdown 格式提示词文档。
│ │ └── ... (其他 prompts-library 内部文件)
│ ├── l10n-tool/ # 多语言批量翻译脚本,保护代码块,先机翻后润色。
│ ├── my-nvim/ # 个人 Neovim 配置。
│ └── XHS-image-to-PDF-conversion/ # 小红书图片转 PDF 工具。
├── i18n/zh/prompts/ # 核心资产AI 提示词库。 ├── backups/ # 备份脚本与存档
│ ├── coding_prompts/ # 编程与代码生成相关提示词。 │ ├── 一键备份.sh
│ ├── system_prompts/ # AI 系统级提示词(含 CLAUDE 版本目录)。 │ ├── 快速备份.py
│ ├── user_prompts/ # 用户自定义提示词。 │ └── gz/ # 压缩存档
│ └── meta_prompts/ # 元提示词与提示工程辅助。
└── i18n/zh/skills/ # 模块化技能库。 └── scripts/ # 项目脚本
├── ccxt/ # CCXT 加密货币交易库技能。
├── claude-code-guide/ # Claude Code 使用指南技能。
├── postgresql/ # PostgreSQL 数据库技能。
├── telegram-dev/ # Telegram Bot 开发技能。
└── ... (其他 10+ 个技能)
``` ```

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TRANSLATED CONTENT: # 🎯 AI Skills Library
# 🎯 AI Skills 技能库
`i18n/zh/skills/` 目录存放 AI 技能Skills这些是比提示词更高级的能力封装可以让 AI 在特定领域表现出专家级水平。当前包含 **14 个**专业技能。 The `i18n/zh/skills/` directory stores AI Skills. These are advanced capability encapsulations, more sophisticated than simple prompts, that enable AI to perform at an expert level in specific domains. Currently includes **14** professional skills.
## 目录结构 ## Directory Structure
``` ```
i18n/zh/skills/ i18n/zh/skills/
├── README.md # 本文件 ├── README.md # This file
├── # === 元技能(核心) === ├── # === Meta Skills (Core) ===
├── claude-skills/ # ⭐ 元技能:生成 Skills 的 Skills11KB ├── claude-skills/ # ⭐ Meta Skill: Skills for Generating Skills (11KB)
├── # === Claude 工具 === ├── # === Claude Tools ===
├── claude-code-guide/ # Claude Code 使用指南9KB ├── claude-code-guide/ # Guide for using Claude Code (9KB)
├── claude-cookbooks/ # Claude API 最佳实践9KB ├── claude-cookbooks/ # Best practices for Claude API (9KB)
├── # === 数据库 === ├── # === Databases ===
├── postgresql/ # ⭐ PostgreSQL 专家技能76KB最详细 ├── postgresql/ # ⭐ PostgreSQL Expert Skill (76KB, most detailed)
├── timescaledb/ # 时序数据库扩展3KB ├── timescaledb/ # Time-series Database Extension (3KB)
├── # === 加密货币/量化 === ├── # === Cryptocurrency / Quant ===
├── ccxt/ # 加密货币交易所统一 API18KB ├── ccxt/ # Unified Cryptocurrency Exchange API (18KB)
├── coingecko/ # CoinGecko 行情 API3KB ├── coingecko/ # CoinGecko Market Data API (3KB)
├── cryptofeed/ # 加密货币实时数据流6KB ├── cryptofeed/ # Cryptocurrency Real-time Data Stream (6KB)
├── hummingbot/ # 量化交易机器人框架4KB ├── hummingbot/ # Quant Trading Bot Framework (4KB)
├── polymarket/ # 预测市场 API6KB ├── polymarket/ # Prediction Market API (6KB)
├── # === 开发工具 === ├── # === Development Tools ===
├── telegram-dev/ # Telegram Bot 开发18KB ├── telegram-dev/ # Telegram Bot Development (18KB)
├── twscrape/ # Twitter/X 数据抓取11KB ├── twscrape/ # Twitter/X Data Scraping (11KB)
├── snapdom/ # DOM 快照工具8KB ├── snapdom/ # DOM Snapshot Tool (8KB)
└── proxychains/ # 代理链配置6KB └── proxychains/ # Proxy Chains Configuration (6KB)
``` ```
## Skills 一览表 ## Skills Overview Table
### 按文件大小排序(详细程度) ### Sorted by File Size (Detail Level)
| 技能 | 大小 | 领域 | 说明 | | Skill | Size | Domain | Description |
|------|------|------|------| |---|---|---|---|
| **postgresql** | 76KB | 数据库 | ⭐ 最详细PostgreSQL 完整专家技能 | | **postgresql** | 76KB | Database | ⭐ Most detailed, complete PostgreSQL expert skill |
| **telegram-dev** | 18KB | Bot 开发 | Telegram Bot 开发完整指南 | | **telegram-dev** | 18KB | Bot Development | Complete guide for Telegram Bot development |
| **ccxt** | 18KB | 交易 | 加密货币交易所统一 API | | **ccxt** | 18KB | Trading | Unified API for cryptocurrency exchanges |
| **twscrape** | 11KB | 数据采集 | Twitter/X 数据抓取 | | **twscrape** | 11KB | Data Collection | Twitter/X data scraping |
| **claude-skills** | 11KB | 元技能 | ⭐ 生成 Skills 的 Skills | | **claude-skills** | 11KB | Meta Skill | ⭐ Skills for Generating Skills |
| **claude-code-guide** | 9KB | 工具 | Claude Code 使用最佳实践 | | **claude-code-guide** | 9KB | Tools | Best practices for Claude Code usage |
| **claude-cookbooks** | 9KB | 工具 | Claude API 使用示例 | | **claude-cookbooks** | 9KB | Tools | Claude API usage examples |
| **snapdom** | 8KB | 前端 | DOM 快照与测试 | | **snapdom** | 8KB | Frontend | DOM snapshots and testing |
| **cryptofeed** | 6KB | 数据流 | 加密货币实时数据流 | | **cryptofeed** | 6KB | Data Stream | Cryptocurrency real-time data stream |
| **polymarket** | 6KB | 预测市场 | Polymarket API 集成 | | **polymarket** | 6KB | Prediction Market | Polymarket API integration |
| **proxychains** | 6KB | 网络 | 代理链配置与使用 | | **proxychains** | 6KB | Network | Proxy chains configuration and usage |
| **hummingbot** | 4KB | 量化 | 量化交易机器人框架 | | **hummingbot** | 4KB | Quant | Quant trading bot framework |
| **timescaledb** | 3KB | 数据库 | PostgreSQL 时序扩展 | | **timescaledb** | 3KB | Database | PostgreSQL time-series extension |
| **coingecko** | 3KB | 行情 | CoinGecko 行情 API | | **coingecko** | 3KB | Market Data | CoinGecko Market Data API |
### 按领域分类 ### Categorized by Domain
#### 🔧 元技能与工具 #### 🔧 Meta Skills & Tools
| 技能 | 说明 | 推荐场景 | | Skill | Description | Recommended Scenarios |
|------|------|----------| |---|---|---|
| `claude-skills` | 生成 Skills 的 Skills | 创建新技能时必用 | | `claude-skills` | Skills for Generating Skills | Essential for creating new skills |
| `claude-code-guide` | Claude Code CLI 使用指南 | 日常开发 | | `claude-code-guide` | Claude Code CLI Usage Guide | Daily development |
| `claude-cookbooks` | Claude API 最佳实践 | API 集成 | | `claude-cookbooks` | Claude API Best Practices | API integration |
#### 🗄️ 数据库 #### 🗄️ Databases
| 技能 | 说明 | 推荐场景 | | Skill | Description | Recommended Scenarios |
|------|------|----------| |---|---|---|
| `postgresql` | PostgreSQL 完整指南76KB | 关系型数据库开发 | | `postgresql` | Complete PostgreSQL Guide (76KB) | Relational database development |
| `timescaledb` | 时序数据库扩展 | 时间序列数据 | | `timescaledb` | Time-series Database Extension | Time-series data |
#### 💰 加密货币/量化 #### 💰 Cryptocurrency / Quant
| 技能 | 说明 | 推荐场景 | | Skill | Description | Recommended Scenarios |
|------|------|----------| |---|---|---|
| `ccxt` | 交易所统一 API | 多交易所对接 | | `ccxt` | Unified Exchange API | Multi-exchange integration |
| `coingecko` | 行情数据 API | 价格查询 | | `coingecko` | Market Data API | Price queries |
| `cryptofeed` | 实时数据流 | WebSocket 行情 | | `cryptofeed` | Real-time Data Stream | WebSocket market data |
| `hummingbot` | 量化交易框架 | 自动化交易 | | `hummingbot` | Quant Trading Framework | Automated trading |
| `polymarket` | 预测市场 API | 预测市场交易 | | `polymarket` | Prediction Market API | Prediction market trading |
#### 🛠️ 开发工具 #### 🛠️ Development Tools
| 技能 | 说明 | 推荐场景 | | Skill | Description | Recommended Scenarios |
|------|------|----------| |---|---|---|
| `telegram-dev` | Telegram Bot 开发 | Bot 开发 | | `telegram-dev` | Telegram Bot Development | Bot development |
| `twscrape` | Twitter 数据抓取 | 社交媒体数据 | | `twscrape` | Twitter Data Scraping | Social media data |
| `snapdom` | DOM 快照 | 前端测试 | | `snapdom` | DOM Snapshots | Frontend testing |
| `proxychains` | 代理链配置 | 网络代理 | | `proxychains` | Proxy Chains Configuration | Network proxy |
## Skills vs Prompts 的区别 ## Difference Between Skills vs Prompts
| 维度 | Prompts提示词 | Skills技能 | | Dimension | Prompts | Skills |
|------|------------------|----------------| |---|---|---|
| 粒度 | 单次任务指令 | 完整能力封装 | | Granularity | Single task instruction | Complete capability encapsulation |
| 复用性 | 复制粘贴 | 配置后自动生效 | | Reusability | Copy-paste | Automatically effective after configuration |
| 上下文 | 需手动提供 | 内置领域知识 | | Context | Needs manual provision | Built-in domain knowledge |
| 适用场景 | 临时任务 | 长期项目 | | Use Case | Temporary tasks | Long-term projects |
| 结构 | 单文件 | 目录(含 assets/scripts/references | | Structure | Single file | Directory (includes assets/scripts/references) |
## 技能目录结构 ## Skill Directory Structure
每个技能遵循统一结构: Each skill follows a unified structure:
``` ```
skill-name/ skill-name/
├── SKILL.md # 技能主文件,包含领域知识和规则 ├── SKILL.md # Main skill file, contains domain knowledge and rules
├── assets/ # 静态资源(图片、配置模板等) ├── assets/ # Static resources (images, config templates, etc.)
├── scripts/ # 辅助脚本 ├── scripts/ # Helper scripts
└── references/ # 参考文档 └── references/ # Reference documents
``` ```
## 快速使用 ## Quick Start
### 1. 查看技能 ### 1. View a Skill
```bash ```bash
# 查看元技能 # View meta-skill
cat i18n/zh/skills/claude-skills/SKILL.md cat i18n/zh/skills/claude-skills/SKILL.md
# 查看 PostgreSQL 技能(最详细) # View PostgreSQL skill (most detailed)
cat i18n/zh/skills/postgresql/SKILL.md cat i18n/zh/skills/postgresql/SKILL.md
# 查看 Telegram Bot 开发技能 # View Telegram Bot development skill
cat i18n/zh/skills/telegram-dev/SKILL.md cat i18n/zh/skills/telegram-dev/SKILL.md
``` ```
### 2. 复制到项目中使用 ### 2. Copy to Project for Use
```bash ```bash
# 复制整个技能目录 # Copy entire skill directory
cp -r i18n/zh/skills/postgresql/ ./my-project/ cp -r i18n/zh/skills/postgresql/ ./my-project/
# 或只复制主文件到 CLAUDE.md # Or just copy main file to CLAUDE.md
cp i18n/zh/skills/postgresql/SKILL.md ./CLAUDE.md cp i18n/zh/skills/postgresql/SKILL.md ./CLAUDE.md
``` ```
### 3. 结合 Claude Code 使用 ### 3. Use with Claude Code
在项目根目录创建 `CLAUDE.md`,引用技能: Create `CLAUDE.md` in the project root, referencing skills:
```markdown ```markdown
# 项目规则 # Project Rules
请参考以下技能文件: Please refer to the following skill files:
@i18n/zh/skills/postgresql/SKILL.md @i18n/zh/skills/postgresql/SKILL.md
@i18n/zh/skills/telegram-dev/SKILL.md @i18n/zh/skills/telegram-dev/SKILL.md
``` ```
## 创建自定义 Skill ## Create Custom Skill
### 方法一:使用元技能生成(推荐) ### Method 1: Generate using Meta Skill (Recommended)
1. 准备领域资料(文档、代码、规范) 1. Prepare domain materials (documents, code, specifications)
2. 将资料和 `i18n/zh/skills/claude-skills/SKILL.md` 一起提供给 AI 2. Provide materials along with `i18n/zh/skills/claude-skills/SKILL.md` to AI
3. AI 会生成针对该领域的专用 Skill 3. AI will generate a dedicated Skill for that domain
```bash ```bash
# 示例:让 AI 读取元技能后生成新技能 # Example: Let AI generate a new skill after reading the meta-skill
cat i18n/zh/skills/claude-skills/SKILL.md cat i18n/zh/skills/claude-skills/SKILL.md
# 然后告诉 AI请根据这个元技能为 [你的领域] 生成一个新的 SKILL.md # Then tell AI: Based on this meta-skill, please generate a new SKILL.md for [your domain]
``` ```
### 方法二:手动创建 ### Method 2: Manual Creation
```bash ```bash
# 创建技能目录 # Create skill directory
mkdir -p i18n/zh/skills/my-skill/{assets,scripts,references} mkdir -p i18n/zh/skills/my-skill/{assets,scripts,references}
# 创建主文件 # Create main file
cat > i18n/zh/skills/my-skill/SKILL.md << 'EOF' cat > i18n/zh/skills/my-skill/SKILL.md << 'EOF'
# My Skill # My Skill
## 概述 ## Overview
简要说明技能用途和适用场景 Briefly describe skill purpose and applicable scenarios
## 领域知识 ## Domain Knowledge
- 核心概念 - Core concepts
- 最佳实践 - Best practices
- 常见模式 - Common patterns
## 规则与约束 ## Rules & Constraints
- 必须遵守的规则 - Mandatory rules
- 禁止的操作 - Prohibited operations
- 边界条件 - Boundary conditions
## 示例 ## Examples
具体的使用示例和代码片段 Specific usage examples and code snippets
## 常见问题 ## FAQ
FAQ 和解决方案 FAQ and solutions
EOF EOF
``` ```
## 核心技能详解 ## Core Skill Details
### `claude-skills/SKILL.md` - 元技能 ### `claude-skills/SKILL.md` - Meta Skill
**生成 Skills 的 Skills**,是创建新技能的核心工具。 **Skills for Generating Skills**, is the core tool for creating new skills.
使用方法: Usage:
1. 准备你的领域资料(文档、代码、规范等) 1. Prepare your domain materials (documents, code, specifications, etc.)
2. 将资料和 SKILL.md 一起提供给 AI 2. Provide materials along with SKILL.md to AI
3. AI 会生成针对该领域的专用 Skill 3. AI will generate a dedicated Skill for that domain
### `postgresql/SKILL.md` - PostgreSQL 专家 ### `postgresql/SKILL.md` - PostgreSQL Expert
最详细的技能76KB包含 The most detailed skill (76KB), includes:
- 数据库设计最佳实践 - Database design best practices
- 查询优化技巧 - Query optimization techniques
- 索引策略 - Indexing strategies
- 性能调优 - Performance tuning
- 常见问题解决方案 - Common problem solutions
- SQL 代码示例 - SQL code examples
### `telegram-dev/SKILL.md` - Telegram Bot 开发 ### `telegram-dev/SKILL.md` - Telegram Bot Development
完整的 Telegram Bot 开发指南18KB Complete Telegram Bot development guide (18KB):
- Bot API 使用 - Bot API usage
- 消息处理 - Message handling
- 键盘与回调 - Keyboards and callbacks
- Webhook 配置 - Webhook configuration
- 错误处理 - Error handling
### `ccxt/SKILL.md` - 加密货币交易所 API ### `ccxt/SKILL.md` - Cryptocurrency Exchange API
统一的交易所 API 封装18KB Unified exchange API encapsulation (18KB):
- 支持 100+ 交易所 - Supports 100+ exchanges
- 统一的数据格式 - Unified data format
- 订单管理 - Order management
- 行情获取 - Market data retrieval
## 相关资源 ## Related Resources
- [Skills 生成器](https://github.com/yusufkaraaslan/Skill_Seekers) - 把任何资料转为 AI Skills - [Skills Generator](https://github.com/yusufkaraaslan/Skill_Seekers) - Convert any material into AI Skills
- [元技能文件](./claude-skills/SKILL.md) - 生成 Skills 的 Skills - [Meta Skill File](./claude-skills/SKILL.md) - Skills for Generating Skills
- [提示词库](../prompts/) - 更细粒度的提示词集合 - [Prompt Library](../prompts/) - More granular prompt collections
- [Claude Code 指南](./claude-code-guide/SKILL.md) - Claude Code 使用最佳实践 - [Claude Code Guide](./claude-code-guide/SKILL.md) - Claude Code Usage Best Practices
- [文档库](../documents/) - 方法论与开发经验 - [Document Library](../documents/) - Methodologies and development experiences

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--- ---
name: coingecko name: coingecko
description: CoinGecko API documentation - cryptocurrency market data API, price feeds, market cap, volume, historical data. Use when integrating CoinGecko API, building crypto price trackers, or accessing cryptocurrency market data. description: CoinGecko API documentation - cryptocurrency market data API, price feeds, market cap, volume, historical data. Use when integrating CoinGecko API, building crypto price trackers, or accessing cryptocurrency market data.

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TRANSLATED CONTENT:
# Coingecko - Authentication # Coingecko - Authentication
**Pages:** 3 **Pages:** 3

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# Coingecko - Coins # Coingecko - Coins
**Pages:** 65 **Pages:** 65

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# Coingecko - Contract # Coingecko - Contract
**Pages:** 1 **Pages:** 1

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# Coingecko - Exchanges # Coingecko - Exchanges
**Pages:** 14 **Pages:** 14

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# Coingecko - Introduction # Coingecko - Introduction
**Pages:** 4 **Pages:** 4

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# Changelog # Changelog
Source: https://docs.coingecko.com/changelog Source: https://docs.coingecko.com/changelog

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TRANSLATED CONTENT:
# CoinGecko API # CoinGecko API
## Docs ## Docs

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# Coingecko - Market Data # Coingecko - Market Data
**Pages:** 3 **Pages:** 3

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# Coingecko - Nfts # Coingecko - Nfts
**Pages:** 2 **Pages:** 2

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# Coingecko - Other # Coingecko - Other
**Pages:** 16 **Pages:** 16

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# Coingecko - Pricing # Coingecko - Pricing
**Pages:** 1 **Pages:** 1

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# Coingecko - Reference # Coingecko - Reference
**Pages:** 9 **Pages:** 9

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# Coingecko - Trending # Coingecko - Trending
**Pages:** 2 **Pages:** 2

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--- ---
name: cryptofeed name: cryptofeed
description: Cryptofeed - Real-time cryptocurrency market data feeds from 40+ exchanges. WebSocket streaming, normalized data, order books, trades, tickers. Python library for algorithmic trading and market data analysis. description: Cryptofeed - Real-time cryptocurrency market data feeds from 40+ exchanges. WebSocket streaming, normalized data, order books, trades, tickers. Python library for algorithmic trading and market data analysis.

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# Cryptocurrency Exchange Feed Handler # Cryptocurrency Exchange Feed Handler
[![License](https://img.shields.io/badge/license-XFree86-blue.svg)](LICENSE) [![License](https://img.shields.io/badge/license-XFree86-blue.svg)](LICENSE)
![Python](https://img.shields.io/badge/Python-3.8+-green.svg) ![Python](https://img.shields.io/badge/Python-3.8+-green.svg)

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# Cryptofeed Documentation Index # Cryptofeed Documentation Index
## Categories ## Categories

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# Cryptofeed - Other # Cryptofeed - Other
**Pages:** 1 **Pages:** 1

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--- ---
name: timescaledb name: timescaledb
description: TimescaleDB - PostgreSQL extension for high-performance time-series and event data analytics, hypertables, continuous aggregates, compression, and real-time analytics description: Manage time-series data in PostgreSQL with TimescaleDB. Use this skill to install, configure, optimize, and interact with TimescaleDB for high-performance time-series data storage and analysis. This includes creating hypertables, continuous aggregates, handling data retention, and querying time-series data efficiently.
--- ---
# Timescaledb Skill # TimescaleDB Skill
Comprehensive assistance with timescaledb development, generated from official documentation. Manage time-series data in PostgreSQL using TimescaleDB, extending PostgreSQL for high-performance time-series workloads.
## When to Use This Skill ## When to Use This Skill
This skill should be triggered when: Use this skill when you need to:
- Working with timescaledb - Work with time-series data in PostgreSQL
- Asking about timescaledb features or APIs - Install and configure TimescaleDB
- Implementing timescaledb solutions - Create and manage hypertables
- Debugging timescaledb code - Optimize performance for time-series data
- Learning timescaledb best practices - Implement continuous aggregates for rollup data
- Manage data retention and compression
- Query and analyze time-series data
- Migrate existing PostgreSQL tables to hypertables
- Integrate with other PostgreSQL tools and extensions
## Not For / Boundaries
This skill is NOT for:
- General PostgreSQL administration (use a specific PostgreSQL skill for that)
- Deep database tuning unrelated to time-series performance
- Replacing dedicated time-series databases if TimescaleDB's PostgreSQL foundation is not a requirement
- Providing data visualization beyond basic SQL queries (use a BI tool or separate visualization library)
## Quick Reference ## Quick Reference
### Common Patterns ### Installation & Configuration
*Quick reference patterns will be added as you use the skill.* **Install TimescaleDB Extension (Debian/Ubuntu):**
### Example Code Patterns
**Example 1** (bash):
```bash ```bash
rails new my_app -d=postgresql sudo apt install -y postgresql-{{pg_version}}-timescaledb
cd my_app sudo pg_createcluster {{pg_version}} main --start
sudo pg_ctlcluster {{pg_version}} main start
sudo -u postgres psql -c "CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE;"
``` ```
*(Replace `{{pg_version}}` with your PostgreSQL version, e.g., 16)*
**Example 2** (ruby): **Configuration (postgresql.conf):**
```ruby ```ini
gem 'timescaledb' # Add to postgresql.conf
shared_preload_libraries = 'timescaledb'
timescaledb.max_background_workers = 8 # Adjust based on CPU cores
max_connections = 100 # Adjust based on workload
``` ```
*(After changes, restart PostgreSQL: `sudo systemctl restart postgresql`)*
**Example 3** (shell): ### Hypertables
```shell
kubectl create namespace timescale
```
**Example 4** (shell): **Create Hypertables:**
```shell
kubectl config set-context --current --namespace=timescale
```
**Example 5** (sql):
```sql ```sql
DROP EXTENSION timescaledb; CREATE TABLE sensor_data (
time TIMESTAMPTZ NOT NULL,
device_id INT,
temperature DOUBLE PRECISION,
humidity DOUBLE PRECISION
);
SELECT create_hypertable('sensor_data', 'time');
``` ```
## Reference Files **Convert Existing Table to Hypertable:**
```sql
SELECT create_hypertable('your_existing_table', 'time_column', migrate_data => true);
```
This skill includes comprehensive documentation in `references/`: **Show Hypertables:**
```sql
\d+
SELECT * FROM timescaledb_information.hypertables;
```
- **api.md** - Api documentation ### Continuous Aggregates
- **compression.md** - Compression documentation
- **continuous_aggregates.md** - Continuous Aggregates documentation
- **getting_started.md** - Getting Started documentation
- **hyperfunctions.md** - Hyperfunctions documentation
- **hypertables.md** - Hypertables documentation
- **installation.md** - Installation documentation
- **other.md** - Other documentation
- **performance.md** - Performance documentation
- **time_buckets.md** - Time Buckets documentation
- **tutorials.md** - Tutorials documentation
Use `view` to read specific reference files when detailed information is needed. **Create Continuous Aggregate:**
```sql
CREATE MATERIALIZED VIEW device_hourly_summary
WITH (timescaledb.continuous) AS
SELECT
time_bucket('1 hour', time) AS bucket,
device_id,
AVG(temperature) AS avg_temp,
MAX(temperature) AS max_temp
FROM sensor_data
GROUP BY time_bucket('1 hour', time), device_id
WITH NO DATA; -- Initially create without data
## Working with This Skill -- Refresh the continuous aggregate
CALL refresh_continuous_aggregate('device_hourly_summary', NULL, NULL);
```
### For Beginners **Get Continuous Aggregates Info:**
Start with the getting_started or tutorials reference files for foundational concepts. ```sql
SELECT * FROM timescaledb_information.continuous_aggregates;
```
### For Specific Features ### Data Retention & Compression
Use the appropriate category reference file (api, guides, etc.) for detailed information.
### For Code Examples **Set Data Retention Policy (Drop data older than 3 months):**
The quick reference section above contains common patterns extracted from the official docs. ```sql
SELECT add_retention_policy('sensor_data', INTERVAL '3 months');
```
## Resources **Enable Compression (Compress data older than 7 days):**
```sql
ALTER TABLE sensor_data SET (timescaledb.compress = TRUE);
SELECT add_compression_policy('sensor_data', INTERVAL '7 days');
```
### references/ **Show Compression Status:**
Organized documentation extracted from official sources. These files contain: ```sql
- Detailed explanations SELECT * FROM timescaledb_information.compression_settings;
- Code examples with language annotations ```
- Links to original documentation
- Table of contents for quick navigation
### scripts/ ### Querying Time-Series Data
Add helper scripts here for common automation tasks.
### assets/ **Basic Time-Range Query:**
Add templates, boilerplate, or example projects here. ```sql
SELECT * FROM sensor_data
WHERE time >= NOW() - INTERVAL '1 day'
AND time < NOW()
ORDER BY time DESC;
```
## Notes **Gapfilling and Interpolation:**
```sql
SELECT
time_bucket('1 hour', time) AS bucket,
AVG(temperature) AS avg_temp,
locf(AVG(temperature)) OVER (ORDER BY time_bucket('1 hour', time)) AS avg_temp_locf
FROM sensor_data
GROUP BY bucket
ORDER BY bucket;
```
- This skill was automatically generated from official documentation ### High-Performance Queries
- Reference files preserve the structure and examples from source docs
- Code examples include language detection for better syntax highlighting
- Quick reference patterns are extracted from common usage examples in the docs
## Updating **Approximate Count:**
```sql
SELECT COUNT(*) FROM sensor_data TABLESAMPLE BERNOULLI (1);
```
To refresh this skill with updated documentation: **Top-N Queries:**
1. Re-run the scraper with the same configuration ```sql
2. The skill will be rebuilt with the latest information SELECT time, device_id, temperature
FROM sensor_data
WHERE time >= NOW() - INTERVAL '1 day'
ORDER BY temperature DESC
LIMIT 10;
```
## Examples
### Example 1: IoT Sensor Data Pipeline
- Input: Stream of sensor readings (time, device_id, value)
- Steps:
1. Create a hypertable for `iot_readings`.
2. Ingest data into the hypertable.
3. Create a continuous aggregate to compute hourly average readings.
4. Query the continuous aggregate for a specific device's hourly trend.
5. Set a retention policy to keep only 1 year of raw data.
- Expected output / acceptance: Efficient storage, automatic hourly rollups, and proper data pruning.
### Example 2: Financial Tick Data Analysis
- Input: High-frequency financial tick data (timestamp, symbol, price, volume)
- Steps:
1. Create a hypertable `tick_data` with proper chunk sizing for high ingest rate.
2. Enable compression for older `tick_data`.
3. Query `tick_data` to calculate 5-minute VWAP (Volume Weighted Average Price) for a specific symbol.
4. Visualize the VWAP over the last trading day.
- Expected output / acceptance: Ability to ingest and analyze millions of rows/second, with optimized storage and fast analytical queries.
### Example 3: Monitoring System Metrics
- Input: Server metrics (timestamp, host_id, cpu_usage, memory_usage, network_io)
- Steps:
1. Create a hypertable `system_metrics` partitioned by `time` and `host_id`.
2. Use a `time_bucket_gapfill` query to find CPU usage for all hosts over the last 24 hours, filling in missing data points.
3. Create an alert based on `MAX(cpu_usage)` exceeding a threshold using a continuous aggregate.
- Expected output / acceptance: Comprehensive monitoring with gap-filled data for visualization and real-time alerting.
## References
- `references/installation.md`: Detailed installation and setup
- `references/hypertables.md`: Deep dive into hypertable management
- `references/continuous_aggregates.md`: Advanced continuous aggregate techniques
- `references/compression.md`: Comprehensive guide to data compression
- `references/api.md`: TimescaleDB SQL functions and commands reference
- `references/performance.md`: Performance tuning and best practices
- `references/getting_started.md`: Official TimescaleDB Getting Started Guide
- `references/llms.md`: Using TimescaleDB with LLMs (e.g., storing embeddings, RAG)
- `references/llms-full.md`: Full LLM integration scenarios
- `references/tutorials.md`: Official TimescaleDB Tutorials and Use Cases
- `references/time_buckets.md`: Guide to `time_bucket` and gapfilling functions
- `references/hyperfunctions.md`: Advanced analytical functions for time-series
## Maintenance
- Sources: Official TimescaleDB Documentation, GitHub repository, blog posts.
- Last updated: 2025-12-17
- Known limits: This skill focuses on core TimescaleDB features. Advanced PostgreSQL features (e.g., PostGIS, JSONB) are covered by other specialized skills.
## Troubleshooting
### Slow Queries
- Ensure indexes are on `time` and other frequently queried columns.
- Verify chunk sizing is appropriate for your data ingestion rate.
- Use `EXPLAIN ANALYZE` to identify bottlenecks.
- Consider creating continuous aggregates for frequently accessed aggregated data.
### High Disk Usage
- Implement data retention policies for older, less critical data.
- Enable compression for older chunks.
- Regularly run `VACUUM ANALYZE` on your tables.
### Failed to Create Hypertable
- Ensure the `time` column is `TIMESTAMPTZ` or a supported integer type.
- The table must be empty or you must use `migrate_data => true`.
- Check for existing triggers or foreign keys that might conflict.
---
**This skill provides a robust foundation for managing time-series data with TimescaleDB!**

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@ -455,6 +455,241 @@ build-backend = "setuptools.build_meta"
--- ---
在**编程 / 软件开发**里,**项目架构Project Architecture / Software Architecture**指的是:
> **一个项目在“整体层面”是如何被拆分、组织、通信和演进的设计方案**
> ——它决定了代码怎么分层、模块怎么分工、数据怎么流动、系统如何扩展和维护。
---
## 一句话理解
**项目架构 = 不写具体业务代码之前,就先决定“代码怎么放、模块怎么连、职责怎么分”。**
---
## 一、项目架构主要解决什么问题?
项目架构不是“写代码的技巧”,而是解决这些**更高层问题**
* 📦 代码怎么组织才不乱?
* 🔁 模块之间怎么通信?
* 🧱 哪些地方可以独立修改而不影响全局?
* 🚀 项目以后怎么扩展?
* 🧪 如何方便测试、调试、部署?
* 👥 多人协作如何不互相踩代码?
---
## 二、项目架构一般包含哪些内容?
### 1⃣ 目录结构(最直观)
```text
project/
├── src/
│ ├── main/
│ ├── services/
│ ├── models/
│ ├── utils/
│ └── config/
├── tests/
├── docs/
└── README.md
```
👉 决定 **“不同类型代码放哪里”**
---
### 2⃣ 分层设计(核心)
最常见的是 **分层架构Layered Architecture**
```text
表示层UI / API
业务逻辑层Service
数据访问层DAO / Repository
数据库 / 外部系统
```
**规则:**
* 上层可以调用下层
* 下层不能反过来依赖上层
---
### 3⃣ 模块划分(职责边界)
比如一个交易系统:
```text
- market_data # 行情
- strategy # 策略
- risk # 风控
- order # 下单
- account # 账户
```
👉 每个模块:
* 只做一类事情
* 尽量低耦合、高内聚
---
### 4⃣ 数据与控制流
* 数据从哪里来?
* 谁负责处理?
* 谁负责存储?
* 谁负责对外输出?
例如:
```text
WebSocket → 数据清洗 → 指标计算 → AI评分 → SQLite → API → 前端
```
---
### 5⃣ 技术选型(架构的一部分)
* 编程语言Python / Java / Go
* 框架FastAPI / Spring / Django
* 通信方式HTTP / WebSocket / MQ
* 存储SQLite / Redis / PostgreSQL
* 部署(本地 / Docker / 云)
---
## 三、常见项目架构类型(入门必懂)
### 1⃣ 单体架构Monolith
```text
一个项目,一个进程
```
**适合:**
* 个人项目
* 原型
* 小系统
**优点:**
* 简单
* 好调试
**缺点:**
* 后期难扩展
---
### 2⃣ 分层架构(最常见)
```text
Controller → Service → Repository
```
**适合:**
* Web 后端
* 业务系统
---
### 3⃣ 模块化架构
```text
core + plugins
```
**适合:**
* 可插拔系统
* 策略 / 指标系统
👉 **你做量化、AI分析非常适合这个**
---
### 4⃣ 微服务架构(进阶)
```text
每个服务一个独立进程 + API 通信
```
**适合:**
* 大团队
* 高并发
* 长期演进
❌ **新手不建议一开始用**
---
## 四、用一个“真实例子”理解(贴近你现在做的)
假设你做 **币安永续 AI 分析系统**
```text
backend/
├── data/
│ └── binance_ws.py # 行情订阅
├── indicators/
│ └── vpvr.py
├── strategy/
│ └── signal_score.py
├── storage/
│ └── sqlite_writer.py
├── api/
│ └── http_server.py
└── main.py
```
这就是**项目架构设计**
* 每个文件夹只负责一件事
* 可替换、可测试
* 后面想接 Telegram Bot / Web 前端都不用重写核心
---
## 五、初学者常见误区 ⚠️
❌ 一开始就搞微服务
❌ 所有代码写在一个文件
❌ 架构追求“高级感”,而不是“可维护”
❌ 没想清楚数据流就开始写
---
## 六、学习路线建议(很重要)
你现在学 CS很推荐这个顺序
1. **先写能跑的项目(不完美)**
2. **代码开始乱 → 才学架构**
3. 学会:
* 模块拆分
* 分层
* 依赖方向
4. 再学:
* 设计模式
* 微服务 / 消息队列
---
**版本**: 1.0 **版本**: 1.0
**更新日期**: 2025-11-24 **更新日期**: 2025-11-24
**维护**: CLAUDECODEXKIMI **维护**: CLAUDECODEXKIMI