semvec/pypi-packages huggingface dataset:
terraformreviewer 0.1.3
Generate Terraform READMEs and inline comments using Claude

# terraformreviewer: AI-Powered Terraform Documentation and Comment Generator

`terraformreviewer` is a command-line tool that uses Anthropic’s Claude AI to automatically generate production-ready documentation (`README.md`) and add clear, helpful inline comments to your Terraform (`.tf`) files. This makes your infrastructure-as-code projects easier to understand, maintain, and onboard new team members.

---

## Features

- **Automatic README Generation:** Produces a comprehensive, human-readable `README.md` for your Terraform project.
- **Inline Comments:** Adds concise, informative comments to all `.tf` files, explaining resources, blocks, and configuration.
- **Powered by Claude AI:** Leverages Anthropic’s Claude for high-quality, context-aware documentation and code explanations.

---

## Installation

Install the tool directly from PyPI:

```bash
pip install terraformreviewer
```

Or, if you want the latest development version:

```bash
git clone https://github.com/Zayan-ahmed953/Terraform_Reviewer_AI_Tool.git
cd Terraform_AI_Reviewer
pip install -e .
```

---

## Setup

Before using the tool, you must set your Anthropic API key as an environment variable:

```bash
export ANTHROPIC_API_KEY=<your valueapi key>
```

You can obtain an API key from [Anthropic](https://docs.anthropic.com/claude/docs/quickstart-guide).

---

## Usage

Run the tool from your terminal, pointing it to your Terraform project directory:

```bash
terraformreviewer /path/to/your/terraform/project
```

This will:
- Generate a `README.md` describing your infrastructure.
- Add helpful inline comments to all `.tf` files in the project.

**Example:**

```bash
terraformreviewer /home/zayan/Desktop/My-Projects/AI-Generated-sample-terraform/
```

---

## Notes

- If you rerun the tool without changing your Terraform files, it will skip regeneration for efficiency.
- Ensure your API key is valid and you have internet access.

---

## Troubleshooting

- **No .tf files found:** Make sure you provide the path to a directory containing Terraform files.
- **API key error:** Double-check that `ANTHROPIC_API_KEY` is set in your environment.
- **Other errors:** Please open an issue with the error message and steps to reproduce.
matplotlib-surface-plotting 0.14
Brain mesh plotting in matplotlib

# Matplotlib surface plotting

Matplotlib 3D mesh plotter for plotting brain meshes
![plot](https://github.com/kwagstyl/matplotlib_surface_plotting/blob/main/figs/demo_plot.png?raw=true)

## Installation

```bash
pip install matplotlib-surface-plotting
```

## Usage

Run example:

```bash
python examples/script_matplotlib.py
```

This will create demo_plot.png using the provided demo files.

## Background

Contributors: M. Ripart, U. Popple, K. Wagstyl 
Based on a matplotlib blogpost by Nicolas P. Rougier
gemini-wt 0.1.3
Manage parallel Gemini CLI instances
Requires Python: >=3.12
Keywords: ai, gemini, git

# 🔷 Gemini-Worktree

Run multiple Gemini Code instances in parallel without stepping on each other. This CLI creates isolated git worktrees for each Gemini session, so you can work on different features simultaneously while keeping your main branch clean.

_This is a fork of [claude-wt](https://github.com/jlowin/claude-wt) that works with Gemini instead of Claude. Inspired by [Jeremiah Lowin](https://github.com/jlowin/claude-wt)._

## 🚀 Quick Start

Jump right in without installing anything:

```bash
uvx gemini-wt new
```

**That's it.** You're now working in a clean branch where Gemini can't mess up your pristine codebase.

### Installation Options

If you prefer global installation:

```bash
uv tool install gemini-wt
```

## 🎯 Commands

### ✨ Start Fresh: `new`

Spin up a new isolated Gemini session:

```bash
uvx gemini-wt new
```

Behind the scenes: creates a timestamp branch, sets up a worktree in `.gemini/worktrees/`, and launches Gemini in the isolated environment.

Want a memorable branch name? Use `--name`:

```bash
uvx gemini-wt new --name parser-fix
```

Need to branch from a specific source? Use `--branch`:

```bash
uvx gemini-wt new --branch main --name hotfix-123
```

### 🔄 Pick Up Where You Left Off: `resume`

Gemini sessions are like good TV shows—you want to continue watching:

```bash
uvx gemini-wt resume 20241201-143022 (your the branch name)
```

The session ID is shown when you create it.

### 📋 See What's Running: `list`

See all your active worktrees:

```bash
uvx gemini-wt list
```

Shows each session with its health status.

### 🧹 Clean Up: `clean`

Remove a specific session when you're done:

```bash
uvx gemini-wt clean 20241201-143022
```

Or clean everything:

```bash
uvx gemini-wt clean --all  # The Marie Kondo approach
```

## 🔧 How It Works

Think of it like having multiple parallel universes for your code:

1. **Branch Creation** → Each session gets its own branch (`gemini-{timestamp}` or your custom name)
2. **Worktree Setup** → Creates a separate directory in `.gemini/worktrees/` so files don't conflict
3. **Gemini Launch** → Starts Gemini in the isolated environment with full repo access
4. **Session Management** → Resume, list, and clean up sessions effortlessly

## 🎁 Why You'll Love This

- **Fear-Free Experimentation** → Gemini can't break your main branch even if it tries
- **Mental Clarity** → No more "did I commit that test code?" anxiety
- **Context Switching** → Jump between different Gemini conversations effortlessly
- **Easy Cleanup** → One command to remove all experimental branches
- **Clean History** → Your main branch stays pristine for serious work

## 📋 What You Need

- **Python 3.12+**
- **Git with worktree support** (any recent version)
- **Gemini CLI** (installed and authenticated)

## 🛠️ Development

Uses uv for dependency management:

```bash
uv sync
uv run gemini-wt --help
```

Or test changes without installing:

```bash
uvx --from . gemini-wt --help
```