project-deep-analyzer
深度分析项目的系统边界、核心概念、模块架构、关键算法、技术选型以及错误排查。当用户需要深入理解代码库或定位疑难问题时调用。
Best use case
project-deep-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
深度分析项目的系统边界、核心概念、模块架构、关键算法、技术选型以及错误排查。当用户需要深入理解代码库或定位疑难问题时调用。
Teams using project-deep-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/project-deep-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How project-deep-analyzer Compares
| Feature / Agent | project-deep-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
深度分析项目的系统边界、核心概念、模块架构、关键算法、技术选型以及错误排查。当用户需要深入理解代码库或定位疑难问题时调用。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Project Deep Analyzer 本技能提供了一套结构化的方法论,用于深度剖析和理解任何规模的软件项目。它通过六个核心维度引导用户或 AI 进行全方位的项目审计。 ## 核心分析流程 ### 1. 系统边界分析 (System Boundary) 明确项目的外部交互和运行环境。 - **项目目标**:核心解决什么问题? - **运行环境**:支持哪些平台(macOS/Linux/Windows)? - **对外接口**:提供哪些 CLI、API 或 SDK? - **技术栈**:核心语言、框架、数据库及外部服务。 - **安装与使用**:前置条件、依赖项及启动步骤。 ### 2. 核心概念体系 (Core Concept System) 提取项目中的关键领域模型和术语。 - **术语清单**:列出项目中的所有关键术语。 - **定义与动机**:每个概念的定义及其存在的原因。 - **关系图谱**:层级、依赖或组合关系。 - **核心 vs 辅助**:区分核心业务逻辑和周边支持工具。 - **代码映射**:概念在代码结构中的具体位置。 ### 3. 模块架构分析 (Module Architecture) 拆解项目的物理和逻辑组织。 - **目录结构**:模块的物理划分。 - **职责划分**:每个模块的核心职责和边界。 - **依赖关系**:模块间的耦合度和调用链。 - **代码度量**:行数、文件数及其揭示的开发重心。 - **解耦建议**:识别可独立拆分的模块和强耦合点。 ### 4. 核心算法分析 (Core Algorithm) 深入理解项目的逻辑心脏。 - **算法清单**:项目使用的关键算法及其作用。 - **性能评估**:时间/空间复杂度及限制。 - **数据结构**:算法的输入输出及核心数据流动。 - **源码定位**:算法的具体实现文件和函数。 ### 5. 技术选型评估 (Technical Selection) 反思设计决策的合理性。 - **自研 vs 第三方**:哪些部分是自研的?是否有更好的替代方案? - **必要性审查**:自研部分是否存在过度设计? - **重构建议**:如果重新设计,哪些组件应该保留或替换? - **知识沉淀**:项目中值得吸收的技术亮点或算法。 ### 6. 错误排查与分析 (Error Handling) 结构化定位和修复问题。 - **日志定位**:通过错误日志定位具体的文件和函数。 - **链路追溯**:分析完整的调用栈(Stack Trace)。 - **根本原因**:解释错误发生的深层原因。 - **修复方案**:提供具体的修复代码或配置建议。 ## 使用场景 - **新项目接手**:快速建立对陌生代码库的全局视野。 - **架构审计**:识别现有项目中的设计缺陷或优化空间。 - **技术调研**:分析开源项目的实现原理,评估其是否适合集成。 - **复杂 Bug 定位**:通过结构化的调用链路分析解决疑难杂症。
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