I built a CLI that gives any AI instant context about your project

[MyrinNew]

Every time I start a new AI session, I spend the first few minutes explaining the same things:

• "This is a FastAPI project"
• "We use SQLAlchemy for the ORM"
• "The main entry point is src/api/main.py"
• "Recent work has been on the auth module"

It's tedious. And AI tools like Claude Code, ChatGPT, and Gemini start cold every session.


So I built ctx.






pip install ctx

ctx save myproject # scan project, save as context pack
ctx inject myproject # paste into any AI chat instantly
ctx inject myproject --target claude # write CLAUDE.md for Claude Code







What it does

ctx save scans your project and builds a context pack automatically:

• Stack detection — finds pyproject.toml, package.json, Cargo.toml, go.mod, Gemfile, etc.
• Structure map — directory tree of your src/, tests/, api/ folders
• Git log — last 10 commits so the AI understands what you've been working on
• README summary — first few lines as project context
• Your notes — add anything extra on top

The result is a clean Markdown file that any AI can parse immediately.


Inject anywhere

ctx inject myproject puts the context pack where you need it:






ctx inject myproject # → clipboard (paste into ChatGPT, Gemini, etc.)
ctx inject myproject --target claude # → writes CLAUDE.md in current directory
ctx inject myproject --target chatgpt # → clipboard, formatted as a system prompt







Claude Code reads CLAUDE.md automatically when you open a project. No paste needed.


For ChatGPT, Gemini, or anything else — one paste at the start of the session and you're fully loaded.


What a pack looks like





# myproject

## Stack
- Python
- Detected from: pyproject.toml

## Structure
src/
api/
models/
tests/

## Recent commits
- feat: add user auth
- fix: resolve migration conflict
- refactor: extract service layer

## README
MyProject is a FastAPI app for managing...

## Notes
Main entry: src/api/main.py
Auth lives in src/auth/ — JWT-based, no sessions







Global vs local packs





ctx save myproject --scope global # ~/.ctx/packs/myproject.md (default, any directory)
ctx save myproject --scope local # .ctx/myproject.md (project-specific, git-committable)







Local packs take priority. Commit .ctx/ to your repo and your whole team gets the same context.


The full command set





ctx save myproject # scan + save
ctx list # show all packs
ctx show myproject # print pack to terminal
ctx inject myproject # inject (clipboard by default)
ctx edit myproject # open in $EDITOR
ctx delete myproject # remove pack







Why this matters

"Context engineering" is the new prompt engineering. The models are good. What holds them back is not having enough context about your project — your conventions, your current work, your architecture decisions.


ctx is a local, zero-dependency way to fix that. No account. No sync service. Just Markdown files you control.


Try it





pip install ctx

# In any project
ctx save myproject --notes "Add anything you want the AI to know"
ctx inject myproject --target claude







Source: github.com/LakshmiSravyaVedantham/ctx





What's your biggest friction starting an AI session on an existing codebase? Drop it in the comments.




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