Project Analyzer

Analyze codebases to produce directory trees, lines-of-code metrics, and TODO/FIXME pattern findings.

1|Updated Feb 9, 2026

Search Tags:#codebase #metrics #mcp #todo #static-analysis #analyze #lines-of-code

Ready to run

npx skills add https://github.com/kartik786-git/DotNetSkills --skill project-analyzer-kartik786-git

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Project Analyzer
Source: https://github.com/kartik786-git/DotNetSkills/tree/main/SkillsQuickstart/src/SkillsQuickstart/skills/project-analyzer
Command: npx skills add https://github.com/kartik786-git/DotNetSkills --skill project-analyzer-kartik786-git

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers and maintainers quickly understand a project's structure, size, and surface-level technical debt by using deterministic MCP analysis tools to gather factual data instead of relying on guesses.

Core Features & Use Cases

Directory Structure Analysis: Produce a navigable tree view of a repository to identify projects, modules, and configuration files using analyze_directory.
Size & Language Metrics: Calculate lines of code, blank lines, and language distribution with count_lines to assess project scale and focus refactoring efforts.
Technical Debt Detection: Locate TODOs, FIXMEs, HACKs, and other patterns across source files with find_patterns to prioritize maintenance work and bug fixes.
Use Case: Triage an unfamiliar repository before onboarding by generating a project overview, LOC breakdown, and a list of actionable code comments.

Quick Start

Analyze the project at C:/path/to/project focusing on .cs and .json files to produce a directory tree, lines-of-code counts, and any TODO/FIXME findings.

Frequently Asked Questions about Project Analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze a codebase to get lines-of-code metrics and directory structure?▼

To analyze a codebase for lines-of-code metrics and directory structure, use MCP analysis tools to generate a navigable tree view and calculate language distribution. You must provide a valid project path and specify file extension filters to retrieve factual project size data.

What is the best way to find TODO and FIXME patterns across source files?▼

The best way to find TODO and FIXME patterns across source files is using deterministic pattern finding tools to locate technical debt. This approach searches code comments for TODOs, FIXMEs, and HACKs to prioritize maintenance work and bug fixes.

Can I filter lines-of-code counts by specific file extensions like .cs and .json?▼

Yes, you can filter lines-of-code counts by specific file extensions like .cs and .json. The analysis requires a valid project path with extension filters to calculate blank lines, code lines, and language distribution for targeted file types.

Does codebase analysis require any specific environment setup or dependencies?▼

Codebase analysis requires access to MCP analysis tools including analyze_directory, count_lines, and find_patterns. No external dependencies are needed, but you must provide a valid local project path to perform the repository overview and technical debt detection.

How do I triage an unfamiliar repository before onboarding?▼

To triage an unfamiliar repository before onboarding, generate a project overview by analyzing the directory tree, calculating lines-of-code breakdown, and listing actionable code comments. This provides a quick understanding of project structure, size, and surface-level technical debt.

What are the limitations of static analysis for technical debt detection?▼

Static analysis for technical debt detection is limited to locating surface-level patterns like TODOs and FIXMEs in code comments. It does not assess runtime behavior or complex architectural issues, focusing only on textual pattern searches and deterministic lines-of-code metrics.