semvec/pypi-packages huggingface dataset:
cosmic-oracle 0.1.0
A mystical CLI decision oracle built with Rich.
Requires Python: >=3.9



# 🔮 Cosmic Oracle

**Cosmic Oracle** is a mystical command-line decision oracle built in Python using the Rich terminal UI framework.
Ask any question, and the Oracle responds with animated prophecies rendered in a dramatic terminal interface.

While playful in spirit, the project is designed as a structured CLI system that will gradually evolve into an intelligent rule-based decision engine.

---

## ✨ Features

* Interactive command-line question system
* Animated prophecy reveal panels
* Clean modular architecture (CLI → UI → Engine separation)
* Rich-powered terminal styling and loaders
* Lightweight, fast, and fully offline

---

## 📦 Installation

```bash
pip install cosmic-oracle
```

---

## 🚀 Usage

Run the Oracle from your terminal:

```bash
oracle
```

Then simply ask your question and receive the prophecy.

---

## 🧠 Project Philosophy

Cosmic Oracle began as a fun experimental CLI project, but it is being developed with real engineering practices:

* modular system architecture
* clean packaging and release workflow
* incremental feature evolution
* structured intelligence layers

The goal is to transform a playful terminal tool into a progressively smarter **rule-driven prophecy engine**.

---

## 🔭 Future Roadmap

Planned upgrades include:

* Context-aware prophecy generation
* Regex-based question classification
* Template-driven response synthesis
* Adaptive response learning modules
* Extended CLI command system
* Optional analytics and history tracking

Eventually, the Oracle will move from random responses toward **lightweight NLP-style reasoning**, while remaining fast and fully offline.

---

## 🛠 Development

Clone the repository and run locally:

```bash
git clone <repo-url>
cd cosmic-oracle
python -m venv venv
source venv/bin/activate
pip install -e .
```

Then run:

```bash
oracle
```

---

## 📜 License

MIT License

---

## 🌌 Closing Note

Some tools are built purely for productivity.
Some are built for curiosity.
Cosmic Oracle is built for both — a small terminal ritual today, a smarter decision engine tomorrow.
matchescu-base 0.24.0
Base abstract data types for Entity Resolution
Requires Python: !=2.7.*,!=3.0.*,!=3.1.*,!=3.10.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*,!=3.9.*,>=3.11

# matchescu-base

This Python package includes common abstract data types (`adt` package),
utilities (`common` package) and generic data extraction algorithms for entity
resolution.
These abstractions are used in other packages such as:

* `matchescu-reference-extraction`: extracts entity references from data sources
* `matchescu-reference-stores`: stores entity references efficiently
* `matchescu-comparison-space-generation`: generates the comparison space used
for matching or clustering
* `matchescu-matching`: various methods of scoring the similarity of entity
references
* `matchescu-clustering`: various methods of scoring the colocation of entity
references
* `matchescu-profile-assembly`: algorithms used to build concrete entity
profiles from specific data structures (tuples, lists or graphs)

On its own, the package may be used to create other structured approaches
towards entity resolution, particularly based on the Resolvi reference
architecture.

## Set up dev environment

1. (_optional_) install pyenv
2. install Python 3.11
3. install [Poetry](https://python-poetry.org)
4. clone this repository
5. run a couple of shell commands
```shell
$ cd <REPO_ROOT>
$ poetry install
```

## Run tests

```shell
$ poetry run pytest
```

## Activate virtual environment

```shell
$ poetry shell
```
-or-
```shell
$ source .venv/bin/activate
```
nonebot-plugin-sensitive-lexicon 0.1.6
插件描述
Requires Python: >=3.10

<div align="center">
    <a href="https://v2.nonebot.dev/store">
    <img src="https://raw.githubusercontent.com/fllesser/nonebot-plugin-template/refs/heads/resource/.docs/NoneBotPlugin.svg" width="310" alt="logo"></a>

## ✨ nonebot-plugin-sensitive-lexicon ✨
[![LICENSE](https://img.shields.io/github/license/zifox666/nonebot-plugin-sensitive-lexicon.svg)](./LICENSE)
[![pypi](https://img.shields.io/pypi/v/nonebot-plugin-sensitive-lexicon.svg)](https://pypi.python.org/pypi/nonebot-plugin-sensitive-lexicon)
[![python](https://img.shields.io/badge/python-3.12-blue.svg)](https://www.python.org)
[![uv](https://img.shields.io/badge/package%20manager-uv-black?style=flat-square&logo=uv)](https://github.com/astral-sh/uv)
<br/>
[![ruff](https://img.shields.io/badge/code%20style-ruff-black?style=flat-square&logo=ruff)](https://github.com/astral-sh/ruff)
[![pre-commit](https://results.pre-commit.ci/badge/github/zifox666/nonebot-plugin-sensitive-lexicon/master.svg)](https://results.pre-commit.ci/latest/github/zifox666/nonebot-plugin-sensitive-lexicon/master)

</div>

## 📖 介绍

通过 [违禁词词库](https://github.com/konsheng/Sensitive-lexicon/) 审核群消息, 触犯规则后自动禁言或踢出, 可选禁止重新加群

## 💿 安装

<details open>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-sensitive-lexicon --upgrade

</details>

<details>
<summary>使用包管理器安装</summary>
在 nonebot2 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令

<details open>
<summary>uv</summary>

    uv add nonebot-plugin-sensitive-lexicon
安装仓库 master 分支

    uv add git+https://github.com/zifox666/nonebot-plugin-sensitive-lexicon@master
</details>

<details>
<summary>pdm</summary>

    pdm add nonebot-plugin-sensitive-lexicon
安装仓库 master 分支

    pdm add git+https://github.com/zifox666/nonebot-plugin-sensitive-lexicon@master
</details>
<details>
<summary>poetry</summary>

    poetry add nonebot-plugin-sensitive-lexicon
安装仓库 master 分支

    poetry add git+https://github.com/zifox666/nonebot-plugin-sensitive-lexicon@master
</details>

打开 nonebot2 项目根目录下的 `pyproject.toml` 文件, 在 `[tool.nonebot]` 部分追加写入

    plugins = ["nonebot_plugin_sensitive_lexicon"]

</details>

<details>
<summary>使用 nbr 安装(使用 uv 管理依赖可用)</summary>

[nbr](https://github.com/fllesser/nbr) 是一个基于 uv 的 nb-cli，可以方便地管理 nonebot2

    nbr plugin install nonebot-plugin-sensitive-lexicon
使用 **pypi** 源安装

    nbr plugin install nonebot-plugin-sensitive-lexicon -i "https://pypi.org/simple"
使用**清华源**安装

    nbr plugin install nonebot-plugin-sensitive-lexicon -i "https://pypi.tuna.tsinghua.edu.cn/simple"

</details>


## ⚙️ 配置

在 nonebot2 项目的`.env`文件中添加下表中的必填配置

| 配置项  | 必填 | 默认值 |    说明     |
| :-----: |:--:|:---:|:---------:|
| KICK_COUNT | 否  |  5  | 触犯几次规则后踢人 |
| MUTE_DAY | 否  |  1  | 首次触发禁言天数  |
|REJECT_ADD_REQUEST|否|False| 踢出后禁止重新加群 |
|OCR_URL|否|OCR服务地址|

## 🎉 使用
### 指令表
| 指令  | 权限  | 需要@ | 范围 |   说明   |
|:---:|:---:| :---: |:--:|:------:|
| esl | 管理员 |  否   | 群聊 | 开关本群审核 |
treepeat 0.4.1
treepeat: a tool to find similarities in a codebase
Requires Python: >=3.11

# Overview

`treepeat` is a tool that analyzes finds similarities in your codebase.

- Find **duplicate** code blocks meaningful to the language (classes/functions), not just lines (`--ruleset none`)
- Find **near-duplicates**, ignore whitespace, strings, high level AST nodes such as function and names (`--ruleset default`)
- Find **structurally similar** code, ignoring anonymizing identifiers, constants, etc (`--ruleset loose`)

Pull requests welcome: This is very much an proof of concept - I'm happy with it, but I haven't supported very many languages at present. PRs welcome!

Current languages supported so far: bash, css, go, html, javascript, markdown, python, sql, typescript, java, kotlin

## Usage

### Installation

```sh
pip insteall treepeat
```

### detect

Scan a codebase for similar or duplicate code blocks using tree-sitter AST analysis and locality-sensitive hashing.

Key flags:
- `--ruleset`: Normalization ruleset to use (`none`, `default`, `loose`) - controls how code is normalized before comparison
- `--similarity`: Percent similarity from 1-100 (default: 100 for exact duplicates)
- `--min-lines`: Minimum number of lines for a match (default: 5)
- `--diff`: Show side-by-side comparisons of similar blocks
- `--format`: Output format - `console` (default) or `sarif` for CI integration

```bash
# Find exact duplicates
treepeat detect /path/to/codebase

# Find near-duplicates with 80% similarity threshold
treepeat detect --similarity 80 /path/to/codebase

# Show diffs between similar blocks and use loose ruleset
treepeat --ruleset loose detect --diff --min-lines 10 /path/to/codebase

# Output results in SARIF format for CI tools
treepeat detect --format sarif -o results.sarif /path/to/codebase
```

### Other sub commands

#### list-ruleset

List all rules in a ruleset, along with their descriptions. Use `--language` to see which rules apply to a specific language.

#### treesitter

Display how treepeat normalizes source code into tree-sitter tokens for similarity detection -- helpful for debugging why a certain section of a file might be similar to another. Shows the original source code side-by-side with the normalized token representation.

## Dev setup

```bash
make setup
make test
```

## ADRs

Architecture Decision Records live in docs/adr.
litellm-mcp 1.0.4
MCP server giving AI agents access to 100+ LLMs through LiteLLM
Requires Python: >=3.10
Keywords: ai-agents, litellm, llm, mcp, multi-model

# LiteLLM Agent MCP Server

<!-- mcp-name: io.github.BerriAI/litellm-mcp -->

**Give your AI agent access to 100+ LLMs.**

This MCP server lets AI agents (Claude Code, Cursor, etc.) call any LLM through LiteLLM's unified API. Stop being limited to one model — use the right model for each task.

## Why?

AI agents are typically stuck on a single model. With this MCP server, your agent can:

- 🔀 **Call any model** — GPT-4, Claude, Gemini, Mistral, and 100+ more
- ⚖️ **Compare outputs** — Get responses from multiple models and pick the best
- 🎯 **Use the right tool** — Code tasks → GPT-4, writing → Claude, long docs → Gemini
- 💰 **Save costs** — Route simple queries to cheaper models

## Tools

| Tool | Description |
|------|-------------|
| `call` | Call any LLM model (OpenAI chat completions format) |
| `responses` | Use OpenAI Responses API format (stateful, tools, structured output) |
| `messages` | Use Anthropic Messages API format (native Claude format) |
| `generate_content` | Use Google generateContent format (native Gemini format) |
| `compare` | Compare responses from multiple models |
| `models` | List available models and their strengths |
| `recommend` | Get model recommendation for a task type |

## Installation

### Claude Desktop / Cursor

Add to your MCP config:

```json
{
  "mcpServers": {
    "litellm": {
      "command": "python",
      "args": ["-m", "litellm_agent_mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-..."
      }
    }
  }
}
```

### From PyPI

```bash
pip install litellm-agent-mcp
```

### From Source

```bash
git clone https://github.com/BerriAI/litellm-agent-mcp
cd litellm-agent-mcp
pip install -e .
```

## Usage Examples

### Call a specific model

```
Use the `call` tool:
- model: "gpt-4o"  
- prompt: "Explain this code: [code here]"
```

### Compare multiple models

```
Use the `compare` tool:
- models: ["gpt-4o", "claude-sonnet-4-20250514"]
- prompt: "What's the best approach to implement caching?"
```

### Get a recommendation

```
Use the `recommend` tool:
- task_type: "code"

→ Returns: gpt-4o (Strong at code generation, debugging, and review)
```

## Environment Variables

Set API keys for the providers you want to use:

```bash
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-...
GEMINI_API_KEY=...
MISTRAL_API_KEY=...
```

Or point to a LiteLLM proxy:

```bash
LITELLM_API_BASE=https://your-proxy.com
LITELLM_API_KEY=sk-...
```

## Supported Models

| Provider | Models |
|----------|--------|
| OpenAI | gpt-4o, gpt-4o-mini, o1-preview, o1-mini |
| Anthropic | claude-sonnet-4, claude-opus-4 |
| Google | gemini-1.5-pro, gemini-1.5-flash |
| Mistral | mistral-large-latest |
| + 100 more | See [LiteLLM docs](https://docs.litellm.ai/docs/providers) |

## License

MIT

## Links

- [LiteLLM Docs](https://docs.litellm.ai)
- [LiteLLM GitHub](https://github.com/BerriAI/litellm)
- [MCP Spec](https://modelcontextprotocol.io)
untils 1.0.1
Light-weight library for console games or small utils to process user input as commands and describe structure with config.
Requires Python: >=3.12
Keywords: cli, console, lightweight

# Welcome to `untils`

![Version](https://img.shields.io/badge/version-1.0.0-blue)
![License](https://img.shields.io/badge/license-MIT-green)
![Python](https://img.shields.io/badge/python-3.12-yellow)

*untils* - Is light-weight library for console games or small utils to process user input as commands and describe structure with config.

## Abilities

- Module-based config in markup line.
- Custom logic in OOP.
- Validation and parsing user input for 10 lines.
- Process exceptions.
- Almost-perfect detailed annotations everywhere.
- Fast command processing (~208µs).
- Infinitely extendable abilities.
- Calm about anything.

## Architecture

This library was builded with next architecture:

![Architecture diagramm.](docs/assets/diagramm0.svg)

## Installation

```bash
pip install untils
```

## Documentation

[Start Course](start-guide/01-first-project.md "Beginner course into the library.")

[API Reference](api-reference.md)

## Roadmap

I have resolve my thoughs to several categories:

1. **Main**:
    - [ ] Add command description in `"description"` field.
    - [ ] Add reserved commands in format like internal state name: `"__help__"` - Get all and available commands.

2. **Additional**:
    - [ ] ...

3. **Conceptions**:
    - [ ] Typing system in config through `"type"` field.
ai-container 0.6.0
AI - Tool/Agent isolation
Requires Python: >=3.13

# AI-Container

Podman based container for isolating AI tooling for a individual project.

## Context

In the past, I have repeatedly observed and experienced coding agents accessing or using files and content which they either weren't supposed to, or even technically "did not have access to."

On one occasion, for example, file access was explicitly restricted (e.g., the path was blocked). However, the agent exploited its ability to run bash commands, using them to traverse and scan the restricted paths instead of relying on standard read/write methods.

For this reason, I believe it is essential to implement clear restrictions or a contextual sandbox that is shared with the AI, and which it cannot as easily circumvent by simply invoking a different command.


## Prerequisites

- **Podman** (>= 4.0) - Container runtime. [Install](https://podman.io/docs/installation)
- **Python** (>= 3.13) - Required for the CLI. [Install](https://www.python.org/downloads/)

## Installation

Install the `ai-container` package:

```bash
uv tool install ai-container
```

Or from source:

```bash
git clone https://github.com/yourusername/ai-container.git
cd ai-container
uv tool install .
```

The `ai` command will then be available.

## Usage

### Basic Commands

```bash
ai pi /path/to/project              # Run PI coding agent
ai opc /path/to/project             # Run OpenCode coding agent
ai aic /path/to/project             # Run aichat
ai llm /path/to/project             # Run llm
ai shell /path/to/project           # Interactive shell in container
```

Pass additional arguments directly to the tool:
```bash
ai pi /path/to/project --verbose --model claude-3-sonnet
```
### Provided Tools
- **[PI](https://shittycodingagent.ai)** - Coding agent for generation, analysis, and refactoring
- **[OpenCode](https://opencode.ai)** - AI-powered coding assistant
- **[aichat](https://github.com/sigoden/aichat)** - Interactive AI chat interface
- **[llm](https://github.com/simonw/llm)** - Command-line tool for LLM interaction

### Configuration & Persistence

**First run:** The container image is built (one-time, takes a few minutes).

**Tools use their standard configuration methods** (within the container):
- aichat: `~/.config/aichat/config.toml`
- llm: `~/.config/llm/` + environment variables
- PI: `~/.pi/`
- OpenCode: `~/.config/opencode/`

**Configuration persists automatically across all runs and containers** through named Podman volumes (`config`, `state`, `share`, `pi-config`). Configure once, use everywhere:

```bash
# First run: setup credentials
ai shell /path/to/project
# Inside container: aichat, llm keys set openai <key>, etc.

# Subsequent runs: credentials available automatically
ai pi /path/to/project
```

## Other/Previous Work
* [agent-containers](https://github.com/faileon/agent-containers)
* [agent-container](https://github.com/asfaload/agents_container)
* [opencode-dockerized](https://github.com/glennvdv/opencode-dockerized/tree/main)
hossam 0.5.3
Hossam Data Helper
Requires Python: >=3.11.0
Keywords: data, analysis, helper, hossam, tensorflow

---
title: 🎓 Hossam Data Helper
---

# 🎓 Hossam Data Helper

[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Version](https://img.shields.io/badge/version-0.3.19-green.svg)](https://pypi.org/project/hossam/)
[![Documentation](https://img.shields.io/badge/docs-py.hossam.kr-blue.svg)](https://py.hossam.kr)

**Hossam**은 데이터 분석, 시각화, 통계 처리를 위한 종합 헬퍼 라이브러리입니다.

아이티윌(ITWILL)에서 진행 중인 머신러닝 및 데이터 분석 수업을 위해 개발되었으며, 이광호 강사의 강의에서 활용됩니다.

## ✨ 주요 특징

- 📊 **풍부한 시각화**: 25+ 시각화 함수 (Seaborn/Matplotlib 기반)
- 🎯 **통계 분석**: 회귀, 분류, 시계열 분석 도구
- 🤖 **머신 러닝**: 예측, 분류, 군집 학습 모델 구축 및 성능 평가
- 📦 **샘플 데이터**: 학습용 데이터셋 즉시 로드
- 🔧 **데이터 전처리**: 결측치 처리, 이상치 탐지, 스케일링
- 📈 **교육용 최적화**: 데이터 분석 교육에 특화된 설계


## 📦 설치

```bash
pip install hossam --upgrade-strategy eager
```

**요구사항**: Python 3.11.9

## 📚 전체 문서

**완전한 API 문서와 가이드는 [py.hossam.kr](https://py.hossam.kr)에서 확인하세요.**

### 주요 모듈

- **hs_plot**: 25+ 시각화 함수 (선 그래프, 산점도, 히스토그램, 박스플롯, 히트맵 등)
- **hs_stats**: 회귀/분류 분석, 교차검증, 정규성 검정, 상관분석 등
- **hs_prep**: 결측치 처리, 이상치 탐지, 스케일링, 인코딩 등의 데이터 전처리 기능
- **hs_timeserise**: 시계열 분석 기능 지원
- **hs_gis**: GIS 데이터 로드 및 시각화 (대한민국 지도 지원)
- **hs_util**: 예쁜 테이블 출력, 그리드 서치 등
- **hs_cluster**: 군집분석, PCA 등
- **hs_reg**: 머신러닝 예측 분석 유틸리티
- **hs_cls**: 머신러닝 분류 분석 유틸리티 (예정)

자세한 사용법은 [API 문서](https://py.hossam.kr/api/hossam/)를 참고하세요.


## 📄 라이선스

이 프로젝트는 MIT 라이선스를 따릅니다. 자유롭게 사용, 수정, 배포할 수 있습니다.

## 🔗 링크

- **문서**: [py.hossam.kr](https://py.hossam.kr)
- **PyPI**: [pypi.org/project/hossam](https://pypi.org/project/hossam/)
- **강사**: 이광호 (ITWILL 머신러닝 및 데이터 분석)

---

{% include 'HOSSAM_API.md' %}
cdes 1.4.0
Cannabis Data Exchange Standard (CDES) Python SDK
Requires Python: >=3.9
Keywords: cannabinoids, cannabis, cdes, data-exchange, genetics, terpenes

﻿# CDES Python SDK

Official Python SDK for the [Cannabis Data Exchange Standard (CDES)](https://cdes.acidni.net).

## Installation

```bash
pip install cdes
```

## Quick Start

### Validate Cannabis Data

```python
from cdes import validate_strain, validate_terpene_profile

# Validate a strain record
result = validate_strain({
    "id": "blue-dream",
    "name": "Blue Dream",
    "type": "hybrid"
})

if result.valid:
    print(" Strain data is valid!")
else:
    for error in result.errors:
        print(f" {error.path}: {error.message}")
```

### Access Reference Data

```python
from cdes import get_terpene_library, get_terpene_by_id

# Get the complete terpene library
library = get_terpene_library()
print(f"Library version: {library.version}")
print(f"Total terpenes: {len(library.terpenes)}")

# Look up a specific terpene
myrcene = get_terpene_by_id("terp-myrcene")
print(f"{myrcene.name}: CAS {myrcene.casNumber}")
print(f"Aromas: {', '.join(myrcene.aroma)}")
```

## Features

- **Schema Validation** - Validate strains, COAs, terpene profiles, and cannabinoid profiles
- **Reference Data** - Access the official CDES terpene library with CAS numbers and effects
- **Type Safety** - Full type hints and dataclass models
- **Zero Dependencies** - Pure Python, no external packages required

## API Reference

### Validators

| Function | Description |
|----------|-------------|
| `validate_strain(data)` | Validate a strain record |
| `validate_coa(data)` | Validate a Certificate of Analysis |
| `validate_terpene_profile(data)` | Validate a terpene profile |
| `validate_cannabinoid_profile(data)` | Validate a cannabinoid profile |

### Reference Data

| Function | Description |
|----------|-------------|
| `get_terpene_library()` | Get the complete terpene library |
| `get_terpene_by_id(id)` | Look up a terpene by CDES ID |

### Models

| Class | Description |
|-------|-------------|
| `ValidationResult` | Result of a validation operation |
| `ValidationError` | A single validation error |
| `Terpene` | A cannabis terpene |
| `TerpeneLibrary` | The complete terpene reference |

## Development

```bash
# Clone the repository
git clone https://github.com/Acidni-LLC/cdes-sdk-python.git
cd cdes-sdk-python

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black cdes tests
```

## Related Projects

- [cdes-spec](https://github.com/Acidni-LLC/cdes-spec) - JSON Schema specifications
- [cdes-reference-data](https://github.com/Acidni-LLC/cdes-reference-data) - Reference datasets
- [cdes-website](https://github.com/Acidni-LLC/cdes-website) - Documentation website

## License

MIT License - see [LICENSE](LICENSE) for details.

## Maintained By

[Acidni LLC](https://acidni.com) - Cannabis Data Analytics & AI Solutions
willow-exiftool 0.2
ExifTool optimizer for Willow
Requires Python: >=3.10

# willow-exiftool

[ExifTool](https://exiftool.org/) optimizer for [Willow](https://github.com/wagtail/Willow), to
strip out excessive Exif data.

## Installation

Using [pip](https://pip.pypa.io/):

```console
$ pip install willow-exiftool
```

You'll also need to install ExifTool if you don't have it already. For Debian/Ubuntu you can use
apt, other distributions should also have packages:

```console
$ sudo apt install exiftool
```

Edit your Django project's settings module, and add the application to `INSTALLED_APPS`:

```python
INSTALLED_APPS = [
    # ...
    "willow_exiftool",
    # ...
]
```

Also add/update `WILLOW_OPTIMIZERS` to enable the optimizer:

```python
WILLOW_OPTIMIZERS = "exiftool"
```