semvec/pypi-packages huggingface dataset:
mujoco-mojo 1.0.0
Pythonic MuJoCo MJCF generation and validation toolkit built on Pydantic v2.
Requires Python: >=3.13

Pythonic MuJoCo MJCF generation and validation toolkit built on Pydantic v2. It is built for Python 3.13 and above.

> [Documentation](https://hydrowelder.github.io/mujoco-mojo/)
tiktok-api-unofficial 0.1.0
Unofficial TikTok API library: scrape video/user metadata and optionally download media (no API key required)
Requires Python: >=3.8
Keywords: tiktok, api, scraper, metadata, unofficial

# tiktok-api

Unofficial TikTok API library: scrape video and user profile metadata, and optionally download videos, slides, and audio. No official API key required.

## Install

```bash
pip install -e .
```

Or install dependencies only:

```bash
pip install requests beautifulsoup4 browser_cookie3
```

Then use the single module `tiktok_api.py` from this directory (e.g. add the directory to `PYTHONPATH` or copy the file).

## Usage

```python
from tiktok_api import TT_Content_Scraper

scraper = TT_Content_Scraper(
    wait_time=0.35,
    output_files_fp="data/",
    progress_file_fn="progress_tracking/scraping_progress.db",
    clear_console=False,
)

# Add video IDs (from TikTok URL) and/or usernames
scraper.add_objects(ids=["7398323154424171806"], title="batch1", type="content")
scraper.add_objects(ids=["tagesschau", "tiktok"], title="users", type="user")

# Scrape: metadata only, or with media (mp4/jpeg/mp3)
scraper.scrape_pending(scrape_files=False)   # metadata only
# scraper.scrape_pending(scrape_files=True)  # also download videos/slides/audio
```

- **Content:** use `type="content"` and video IDs from the video URL.
- **Users:** use `type="user"` and usernames (with or without `@`).

Output is written under `output_files_fp`:

- `content_metadata/<video_id>.json` – video/post metadata
- `user_metadata/<username>.json` – user profile metadata
- `content_files/` – mp4, jpeg, mp3 when `scrape_files=True`

Progress is stored in the SQLite database at `progress_file_fn` (pending/completed/error per ID).

## Run as script

```bash
python tiktok_api.py
```

Runs a one-off example: one video ID, metadata only, output in `data/`.
canvas-download 1.1.8
Downloads Canvas modules/files into a local directory.
Requires Python: >=3.10
Keywords: canvas, canvas-grab, download, downloader, offline, sync, utility

# canvas-download
canvas-download downloads Canvas modules/files into a local directory for offline access. A file called 'login.json' under the '.config' directory will be created during the first run of the utility, which will require you to fill out the details of your Canvas login credentials. In the second run of the application, '.config/courses.json' will be created by the application and allows you to choose whether to download "files" or "modules" for each course. After those details are filled out, you can simply call 'canvas-download' in the terminal to start the process of downloading files from Canvas.

# How to install
```
pip install canvas-download
```
# Support
If you encounter any particular issue with this application, feel free to reach out by opening an issue.
codetree-skill 0.1.0
Clawdbot Skill for CodeTree - Query code repositories using natural language
Requires Python: >=3.10
Keywords: clawdbot, skill, codetree, rag, code, llm

# CodeTree Skill for Clawdbot

Clawdbot skill for querying code repositories using natural language. Built on vectorless RAG technology.

## Installation

```bash
pip install codetree-skill
```

## Usage

### CLI Commands

```bash
# Index a repository
codetree-skill index /path/to/repo

# Query the codebase
codetree-skill query /path/to/repo "How does authentication work?"

# Show code structure
codetree-skill tree /path/to/repo

# Find symbol references
codetree-skill find /path/to/repo "UserService"

# Get statistics
codetree-skill stats /path/to/repo
```

### As Clawdbot Skill

Copy the skill files to your Clawdbot skills directory:

```bash
# Find where the skill is installed
python -c "import codetree_skill; print(codetree_skill.__file__)"

# Copy to Clawdbot skills (adjust path as needed)
cp -r /path/to/codetree_skill ~/.clawdbot/skills/codetree/
```

## Features

- 🚫 **No Vector Database** - Uses LLM reasoning instead of embeddings
- 🌳 **AST Parsing** - Understands actual code structure
- 🔗 **Cross-File References** - Tracks imports and dependencies
- 💬 **Natural Language** - Ask questions in plain English

## Supported Languages

- Python
- JavaScript / TypeScript
- Go
- Rust
- Java

## Requirements

- Python 3.10+
- OpenAI or Anthropic API key

## Links

- [CodeTree Main Package](https://pypi.org/project/codetree-rag/)
- [CodeTree MCP Server](https://pypi.org/project/codetree-mcp/)
- [GitHub Repository](https://github.com/toller892/Oh-Code-Rag)
codetree-mcp 0.1.0
MCP Server for CodeTree - Query code repositories from Claude Desktop and other MCP clients
Requires Python: >=3.10
Keywords: mcp, codetree, rag, code, claude, llm

# CodeTree MCP Server

MCP (Model Context Protocol) server for CodeTree. Query code repositories using natural language from Claude Desktop, Cline, Continue, and other MCP-compatible clients.

## Installation

```bash
pip install codetree-mcp
```

## Setup for Claude Desktop

Add to your Claude Desktop config:

**macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json`
**Windows:** `%APPDATA%\Claude\claude_desktop_config.json`

```json
{
  "mcpServers": {
    "codetree": {
      "command": "codetree-mcp",
      "env": {
        "OPENAI_API_KEY": "sk-your-key-here"
      }
    }
  }
}
```

Restart Claude Desktop after configuration.

## Available Tools

| Tool | Description |
|------|-------------|
| `codetree_index` | Index a repository (required before other operations) |
| `codetree_query` | Ask natural language questions about code |
| `codetree_tree` | Show the hierarchical code structure |
| `codetree_find` | Find all references to a symbol |
| `codetree_stats` | Get repository statistics |

## Usage in Claude

Once configured, ask Claude:

- "Use codetree to index /path/to/my/project"
- "Query the codebase: how does authentication work?"
- "Show me the code structure"
- "Find all references to UserService"

## Requirements

- Python 3.10+
- OpenAI or Anthropic API key (for LLM queries)

## Links

- [CodeTree Main Package](https://pypi.org/project/codetree-rag/)
- [GitHub Repository](https://github.com/toller892/Oh-Code-Rag)
videosdk-plugins-rnnoise-dev 0.0.63.dev4
VideoSDK Agent Framework plugin for RNNoise.
Requires Python: >=3.11
Keywords: ai, audio, lmnt, rime, tts, video, videosdk

# VideoSDK RNNoise Plugin

Agent Framework plugin for de-noising with RNNoise.

## Installation

```bash
pip install videosdk-plugins-rnnoise-dev
```

## Building RNNoise for your OS

To avoid OS security issues with prebuilt libraries, build RNNoise locally:
1. Ensure you have git and build tools (autoconf/make on Mac/Linux, Visual Studio with nmake on Windows).
2. Run `python build_rnnoise.py` in the project root.
3. This clones RNNoise, builds the library for your OS, and places it in videosdk/plugins/rnnoise/files/.
zendir 1.5.1.0
Python Interface to the Zendir API for simulations

This is the Python interface library for the Zendir API.         The Zendir API allows access to spacecraft and space-domain simulation functions         and the framework for simulating high-fidelity satellites.         It enables accessing the REST API simulation functions in an easy format.        Examples of how to construct Zendir simulations are provided on the        Zendir documentation found at https://docs.zendir.io.