Product Introduction

1. GitProbe is an intelligent code analysis tool that automatically generates interactive function call graphs and architectural summaries for any GitHub repository. It uses advanced parsing algorithms to map code execution paths, dependency relationships, and module interactions across entire codebases. The tool outputs structured data optimized for large language models (LLMs), providing instant technical context for AI-powered development workflows.
2. The core value of GitProbe lies in its ability to transform complex codebases into machine-readable architectural blueprints within seconds. By creating visual and textual representations of code logic, it eliminates manual reverse-engineering and enables faster onboarding, code audits, and LLM context preparation.

Main Features

1. GitProbe generates interactive function call graphs that visually map execution flows and cross-module dependencies through collapsible node diagrams. These graphs support zoomable interfaces with clickable nodes that reveal function parameters, return types, and nested calls across file boundaries. The system automatically detects programming language syntax to build language-agnostic representations.
2. The tool produces structured summaries containing code architecture breakdowns, dependency matrices, and logic flow descriptions formatted for LLM ingestion. Outputs include JSON-LD schemas with entity relationships, size-filtered code snippets under 50kb, and natural language explanations of core components.
3. Users can customize analysis through URL parameters that exclude non-essential files (e.g., binaries, assets) while focusing on critical source code. The "probe" URL convention (replacing "hub" in GitHub links) enables instant repository analysis without configuration, with optional path whitelisting for targeted component inspection.

Problems Solved

1. GitProbe addresses the time-intensive process of understanding complex or undocumented codebases by automating architectural analysis. Developers often waste hours tracing function calls manually when contributing to open-source projects or maintaining legacy systems.
2. The tool specifically targets AI developers needing structured code context for LLM prompts, engineering teams onboarding new members, and security researchers conducting dependency audits. It serves users who require immediate technical insights without deep code immersion.
3. Typical use cases include preparing code context for AI pair programmers, conducting technical due diligence during acquisitions, and accelerating feature development in large-scale repositories. It also aids in identifying dead code paths and undocumented API endpoints.

Unique Advantages

1. Unlike static analysis tools that produce flat dependency lists, GitProbe visualizes runtime execution paths and dynamic code interactions. It combines abstract syntax tree parsing with control flow analysis to detect actual execution patterns rather than theoretical references.
2. The tool introduces automatic size-based filtering that excludes non-critical files while preserving code structure context, preventing LLM context window pollution. Its URL-based probing requires no authentication or local installation, offering instant public repository analysis.
3. Competitive advantages include multi-language support through adaptive parsers, real-time collaboration features for shared graph exploration, and integration readiness with AI development platforms via standardized JSON outputs. The system outperforms manual documentation methods with 98% faster context generation.

Frequently Asked Questions (FAQ)

1. How does GitProbe ensure analysis accuracy across different programming languages? The tool uses language-specific abstract syntax tree generators combined with pattern-agnostic call tracing algorithms. It validates results through cross-referencing static analysis with import/export declarations and runtime simulation heuristics.
2. Can GitProbe analyze monorepos or enterprise-scale codebases? The system efficiently processes large repositories through chunked analysis and smart memory management. It automatically prioritizes entry points and core modules while deferring non-essential file parsing.
3. What security measures protect repository data during analysis? GitProbe operates as a read-only tool without code execution capabilities, analyzing public repositories through GitHub's API. No source code is stored or transmitted beyond temporary processing buffers during analysis sessions.
4. How does the 50kb file size limit impact analysis completeness? The threshold focuses on human-written source code while excluding generated files and binaries. Critical logic in larger files can be partially included through smart snippet extraction that preserves surrounding context.
5. Can teams collaborate on GitProbe-generated diagrams? Users can share permalinks to interactive graphs that maintain zoom levels and node expansion states. The system supports concurrent viewing with real-time cursor tracking for distributed code reviews.