semvec/pypi-packages huggingface dataset:
nonebot-plugin-shiro-signin 0.1.11
A NoneBot2 plugin for sign-in and favorability system themed with Shiro.
License: MIT
Requires Python: >=3.9
Keywords: nonebot, nonebot2, plugin, shiro, signin

# nonebot-plugin-shiro-signin

✨ 基于 NoneBot2 的签到与好感度系统插件 ✨

<p align="center">
  <a href="https://raw.githubusercontent.com/luojisama/nonebot-plugin-shiro-signin/master/LICENSE">
    <img src="https://img.shields.io/github/license/luojisama/nonebot-plugin-shiro-signin.svg" alt="license">
  </a>
  <a href="https://pypi.python.org/pypi/nonebot-plugin-shiro-signin">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-shiro-signin.svg" alt="pypi">
  </a>
  <img src="https://img.shields.io/badge/python-3.9+-blue.svg" alt="python">
  <img src="https://img.shields.io/badge/nonebot-2.2.0+-red.svg" alt="nonebot">
</p>

## 📖 介绍

这是一个 NoneBot2 插件。
包含签到、好感度系统、行动系统、商店系统等功能。

## 💿 安装

### 使用 nb-cli 安装 (推荐)
```bash
nb plugin install nonebot-plugin-shiro-signin
```

### 使用 pip 安装
```bash
pip install nonebot-plugin-shiro-signin
```
并在 `bot.py` 中添加：
```python
nonebot.load_plugin("nonebot_plugin_shiro_signin")
```

## ⚙️ 配置

在 `.env` 文件中可以配置以下项（可选）：

| 配置项 | 类型 | 默认值 | 说明 |
|:-----:|:----:|:----:|:----:|
| sign_in_data_path | Path | plugin_dir/data/user_data.json | 数据存储路径 |
| hitokoto_api_url | str | ... | 一言 API 地址 |

## 🎮 使用

| 指令 | 别名 | 说明 |
|:-----:|:----:|:----:|
| 签到 | - | 每日签到，增加好感度、金币和行动值 |
| 查询好感度 | 好感度, 我的好感度, 个人信息 | 查看当前等级和属性 |
| 商店 | 绪山商店, 绪山百货 | 打开道具商店 |
| 购买 [ID] | - | 购买指定道具 |
| 背包 | 我的背包, 仓库 | 查看已拥有道具 |
| 使用 [名称] | 使用道具, 吃, 穿, 玩 | 使用背包里的道具 |
| 行动 | 进行行动, 互动 | 消耗行动值与真寻互动，增加好感度 |

## 🛠️ TODO

- [ ] 增加更多互动事件
- [ ] 完善成就系统
- [ ] 支持更多适配器

## 📄 开源许可

本项目使用 [MIT](LICENSE) 许可证开源。
hanzo-tools-shell 0.6.1
Shell/command execution tools with auto-detection - only exposes your active shell (zsh, bash, fish, dash, ksh, tcsh, csh)
License: MIT
Requires Python: >=3.12
Keywords: hanzo, tools, shell, mcp, ai, dag, zsh

# hanzo-tools-shell

Shell execution tools for Hanzo MCP with DAG support and Shellflow DSL.

## Installation

```bash
pip install hanzo-tools-shell
```

## Tools

### dag - DAG Execution Engine
Execute commands with directed acyclic graph semantics.

```python
# Serial execution (default)
dag(["ls", "pwd", "git status"])

# Parallel execution
dag(["npm install", "cargo build"], parallel=True)

# Mixed DAG with parallel blocks
dag([
    "mkdir -p dist",
    {"parallel": ["cp a.txt dist/", "cp b.txt dist/"]},
    "zip -r out.zip dist/"
])

# Tool invocations
dag([{"tool": "search", "input": {"pattern": "TODO"}}])

# Named nodes with dependencies
dag([
    {"id": "build", "run": "make build"},
    {"id": "test", "run": "make test", "after": ["build"]},
])
```

### zsh - Primary Shell with Shellflow DSL
Execute shell commands with optional Shellflow syntax.

```python
# Simple command
zsh("ls -la")

# Shellflow DSL syntax
zsh("mkdir dist ; { cp a dist/ & cp b dist/ } ; zip out")

# With shell parameter
zsh("echo $BASH_VERSION", shell="bash")
```

**Shellflow Syntax:**
- `A ; B` - Sequential execution
- `{ A & B }` - Parallel execution
- `A ; { B & C } ; D` - Mixed DAG

### ps - Process Management
Monitor and control background processes.

```python
ps()                     # List all processes
ps(id="abc123")          # Get specific process
ps(kill="abc123")        # Kill process (SIGTERM)
ps(logs="abc123", n=50)  # Last 50 lines of output
```

### Additional Tools
- **shell** - Smart shell (zsh > bash fallback)
- **bash** - Explicit bash execution
- **npx** - Node package execution with auto-backgrounding
- **uvx** - Python package execution with auto-backgrounding
- **open** - Open files/URLs in system apps
- **curl** - HTTP client without shell escaping issues
- **jq** - JSON processor
- **wget** - File/site downloads

## Auto-Backgrounding

Commands that exceed the timeout (default 60s) are automatically backgrounded:

```python
dag(["long-running-command"], timeout=30)
# If command exceeds 30s, it continues in background
# Use ps --logs <id> to view output
```

## Performance

Shellflow DSL is optimized for high throughput:
- Simple commands: ~7M ops/sec
- Sequential: ~2.2M ops/sec  
- Mixed DAG: ~100k ops/sec

## License

MIT
picogpt 0.1.1
A tiny GPT with tiktoken BPE and RoPE (auto-extend), CUDA/MPS/CPU aware.
License: MIT
Requires Python: >=3.9

# picogpt

A tiny GPT with tiktoken BPE and RoPE (auto-extend), with a simple class API.

- Minimal, readable GPT-style Transformer for next-token prediction.
- Tokenization with a production-grade BPE (tiktoken `cl100k_base`).
- Device selection: prefers CUDA (with mixed precision), else MPS, else CPU.
- A single-stream language-modeling dataset with random (x, y) slices.
- Short training run and autoregressive sampling from a prompt.

Dependencies: *numpy, torch, tiktoken*

## Install

```bash
pip install picogpt

pip install build
python -m build
pip install dist/picogpt-*.whl

from picogpt import PicoGPT

picoGPT = PicoGPT()  # defaults: tiny model, CUDA/MPS/CPU auto-detect

# Train on your text file
picoGPT.train_with_file("corpus.txt")

# Save / load
picoGPT.save_model("out/pico.pt")
picoGPT.load_model("out/pico.pt")

# Inspect
picoGPT.info()

# Inference
answer = picoGPT.ask("RL aligns the model")
print(answer)

# defaults

PicoGPT(
  block_size=64, n_embd=96, n_head=3, n_layer=2, dropout=0.1, rope_base=10000.0,
  batch_size=48, train_steps=600, learning_rate=3e-3, seed=0,
  tokenizer_name="cl100k_base", max_new_tokens=200, temperature=0.9, top_k=50,
  device="auto", amp=True
)


---

## Publish to PyPI (quick guide)

```bash
# from the project root (where pyproject.toml lives)
python -m pip install --upgrade build twine
python -m build
twine upload dist/*