[PR #3011] [CLOSED] Added Jctx tool - Instantly converts Python projects into an AI-ready codebase context file with token metrics and dependency graphs. #8982

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GiteaMirror · 2026-04-18T22:59:41-05:00

GiteaMirror commented

2026-04-18 22:59:41 -05:00

📋 Pull Request Information

Original PR: https://github.com/vinta/awesome-python/pull/3011
Author: @Shashwat-Gupta57
Created: 3/31/2026
Status: ❌ Closed

Base: master ← Head: master

📝 Commits (2)

14a61f4 Add Jctx tool for AI-ready Python code conversion
f0a2d65 Update README.md

📊 Changes

1 file changed (+1 additions, -0 deletions)

View changed files

📝 README.md (+1 -0)

📄 Description

Jctx

Checklist

One project per PR
PR title format: Add project-name
Entry format: * [project-name](url) - Description ending with period.
Description is concise and short

Why This Project Is Awesome

Which criterion does it meet? (pick one)

Industry Standard - The go-to tool for a specific use case
Rising Star - 5000+ stars in < 2 years, significant adoption
Hidden Gem - Exceptional quality, solves niche problems elegantly (close to this, but not that old, but very powerful)

Explain:
Jctx solves the "copy-paste fatigue" for Java and Kotlin developers using AI coding assistants. While built in Python, it provides high-precision context extraction by parsing JVM source files into structured metadata (classes, methods, docs) rather than just dumping raw text. It includes unique developer-centric features like project-internal dependency mapping and token count estimation for specific LLM models, all within a single zero-dependency CLI.

How It Differs

Most Python-based code context tools are either simple file concatenators or general-purpose scrapers. Jctx is unique because:

It also understands Java and Kotlin syntax (parsing classes, members, and docstrings).
Architecture Aware: It generates a project-internal dependency graph to give the AI a high-level architectural view.
Model Specificity: It provides token estimates mapped specifically to current LLM context windows (Gemini, Claude, GPT, etc.).
Zero-Config: It requires no external dependencies beyond the Python standard library, making it extremely portable for enterprise or restricted environments.

If similar entries exist, what makes this one unique?

This one is a smart parser of the codebase, and is an active project, any issues and shortcomings will be fixed as quick as possible.
This one has a simple and interactive TUI, making it extremely beginner-friendly

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/vinta/awesome-python/pull/3011 **Author:** [@Shashwat-Gupta57](https://github.com/Shashwat-Gupta57) **Created:** 3/31/2026 **Status:** ❌ Closed **Base:** `master` ← **Head:** `master` --- ### 📝 Commits (2) - [`14a61f4`](https://github.com/vinta/awesome-python/commit/14a61f43062d8cf8a0d4e59e7822c6e6240a4ba9) Add Jctx tool for AI-ready Python code conversion - [`f0a2d65`](https://github.com/vinta/awesome-python/commit/f0a2d656903b49deed7427dacc7e64772ea57e65) Update README.md ### 📊 Changes **1 file changed** (+1 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+1 -0) </details> ### 📄 Description ## Jctx [Jctx](https://github.com/Shashwat-Gupta57/Jctx) ## Checklist - [x] One project per PR - [x] PR title format: `Add project-name` - [x] Entry format: `* [project-name](url) - Description ending with period.` - [x] Description is concise and short ## Why This Project Is Awesome Which criterion does it meet? (pick one) - [ ] **Industry Standard** - The go-to tool for a specific use case - [ ] **Rising Star** - 5000+ stars in < 2 years, significant adoption - [x] **Hidden Gem** - Exceptional quality, solves niche problems elegantly (close to this, but not that old, but very powerful) Explain: Jctx solves the "copy-paste fatigue" for Java and Kotlin developers using AI coding assistants. While built in Python, it provides high-precision context extraction by parsing JVM source files into structured metadata (classes, methods, docs) rather than just dumping raw text. It includes unique developer-centric features like project-internal dependency mapping and token count estimation for specific LLM models, all within a single zero-dependency CLI. ## How It Differs Most Python-based code context tools are either simple file concatenators or general-purpose scrapers. Jctx is unique because: It also understands Java and Kotlin syntax (parsing classes, members, and docstrings). Architecture Aware: It generates a project-internal dependency graph to give the AI a high-level architectural view. Model Specificity: It provides token estimates mapped specifically to current LLM context windows (Gemini, Claude, GPT, etc.). Zero-Config: It requires no external dependencies beyond the Python standard library, making it extremely portable for enterprise or restricted environments. If similar entries exist, what makes this one unique? This one is a smart parser of the codebase, and is an active project, any issues and shortcomings will be fixed as quick as possible. This one has a simple and interactive TUI, making it extremely beginner-friendly --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2026-04-18 22:59:41 -05:00

GiteaMirror closed this issue

2026-04-18 22:59:44 -05:00

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Reference: github-starred/awesome-python#8982