Does this skill modify my source code?

No, the Project Analyzer is a read-only tool that observes your files to generate reports and JSON data without making any changes to your code.

What is the difference between Quick and Deep analysis?

Quick analysis provides a 5-10 line summary in roughly 30 seconds, while Deep analysis performs an exhaustive crawl of the project structure, metrics, and patterns.

Which programming languages and frameworks does it support?

It is language-agnostic but features specialized detection for Next.js, React, Vue, Express, FastAPI, Rust, Python, and many other modern web technologies.

When should I use the Project Analyzer skill?

Use it when starting work on an unfamiliar project or conducting a comprehensive audit to understand architecture, patterns, and dependencies. Avoid using it on projects you already know well.

Can I export the analysis for use in other tools?

Yes, you can use the --json flag to save a machine-readable analysis to .claude/analysis.json, which is ideal for integration with custom scripts or MCP servers.

Deep Project Analyzer

Name: Deep Project Analyzer
Author: jrc1883

byjrc1883

Productivity & Workflow

Analyzes unfamiliar codebases to map architecture, identify patterns, and uncover improvement opportunities.

About

The Deep Project Analyzer skill provides a comprehensive diagnostic of any software project, helping developers quickly gain context when joining a new codebase or performing a structural audit. It scans for technology stacks, architectural patterns, dependency health, and code quality metrics, generating both human-readable Markdown reports and machine-readable JSON data. By mapping the project's structure, it provides actionable recommendations for performance optimization, security improvements, and workflow automation within the Claude Code environment.

Key Features

Automated detection of frameworks, databases, and package managers
Multi-depth analysis modes ranging from 30-second summaries to exhaustive metrics
0 GitHub stars
Actionable recommendations for project-specific skills and agents
Architectural pattern mapping for both frontend and backend structures
Dependency auditing for security vulnerabilities and outdated packages

Use Cases

Generating structured project metadata for use with automated tooling and MCP servers
Onboarding to a legacy or unfamiliar codebase to understand entry points and patterns
Performing a comprehensive technical audit before major refactoring or migrations

About

Key Features

Automated detection of frameworks, databases, and package managers
Multi-depth analysis modes ranging from 30-second summaries to exhaustive metrics
0 GitHub stars
Actionable recommendations for project-specific skills and agents
Architectural pattern mapping for both frontend and backend structures
Dependency auditing for security vulnerabilities and outdated packages

Use Cases

Generating structured project metadata for use with automated tooling and MCP servers
Onboarding to a legacy or unfamiliar codebase to understand entry points and patterns
Performing a comprehensive technical audit before major refactoring or migrations