semvec/pypi-packages huggingface dataset:
tracemem-claude 0.1.3
TraceMem — memory hooks installer for Claude Code
Requires Python: >=3.12

# tracemem-claude

Memory hooks installer for Claude Code. Captures your coding interactions into a knowledge graph for contextual recall.

## Install

```bash
uvx tracemem-claude init          # local mode (per-project)
uvx tracemem-claude init --global # global mode (~/.claude/)
```

## Uninstall

```bash
uvx tracemem-claude uninstall
uvx tracemem-claude uninstall --global
```

## Prerequisites

- [uv](https://docs.astral.sh/uv/) — required for hook execution (PEP 723 script resolution)
- `TRACEMEM_OPENAI_API_KEY` environment variable — OpenAI API key for embeddings

## Configuration

After install, edit `.claude/skills/tracemem/config.yaml` to customize settings.
pybuildtools 0.7.9
A set of tools for putting together buildscripts and other CLI applications
Requires Python: <4.0,>=3.12

# python-build-tools

A toolkit containing many powerful utilities, including:

 * OS utilities (buildtools.os_utils)
 * Indented logging with colorama support (buildtools.bt_logging.log)
 * The powerful Maestro build management system (buildtools.maestro)
 * A powerful VCS repository wrapper system (buildtools.repo)
 * A mess of other random things.

This is mostly a personal toolkit that grew out of control.

## os_utils
```python
from buildtools import os_utils

# Ensure test/ exists
os_utils.ensureDirExists('test')

# Get copy of current environmental variables.
ENV = os_utils.ENV.clone()

# Add .bin/ to the beginning of PATH in our virtual environment
ENV.prependTo('PATH', '.bin/')

# Remove any duplicate entries from PATH
ENV.removeDuplicatedEntries('PATH')

# Find gcc in our virtual environment (checks PATHEXT on Windows, too!)
# Returns the path to gcc, or None if it couldn't be found.
GCC = ENV.which('gcc')

# Ensure bash exists before continuing (same rules as above)
ENV.assertWhich('bash')

# Bring up gcc's help page. Crash if non-0 exit code, echo command to console, and output STDOUT/STDERR to console.
os_utils.cmd([GCC, '--help'], critical=True, echo=True, show_output=True)
```

## Logging
```python
from buildtools import log

def a():
  log.info('This will be indented if a() is called in a log block.')

log.info('No indentation, yet.')
with log.warning('A warning. Next line will be indented.'):
  log.error('Error!')
  with log.info('The following function\'s log output will be indented by another layer.')
    a()
    log.critical('So will %s!', 'this')
```

## Maestro

Maestro is currently synchronous, with plans to make it async.

```python
from buildtools.maestro import BuildMaestro
from buildtools.maestro.fileio import ReplaceTextTarget
from buildtools.maestro.coffeescript import CoffeeBuildTarget
from buildtools.maestro.web import SCSSBuildTarget, SCSSConvertTarget

bm = BuildMaestro()

# Compile CoffeeScript to JS
bm.add(CoffeeBuildTarget('htdocs/js/vgws.js',                 ['coffee/src/vgws.coffee']))
bm.add(CoffeeBuildTarget('htdocs/js/editpoll.multichoice.js', ['coffee/editpoll.multichoice.coffee'], dependencies=['htdocs/js/vgws.js']))
bm.add(CoffeeBuildTarget('htdocs/js/editpoll.option.js',      ['coffee/editpoll.editpoll.coffee'], dependencies=['htdocs/js/vgws.js']))

# Convert CSS to SCSS
bm.add(SCSSBuildTarget('htdocs/css/style.css', ['style/style.scss'], [], import_paths=['style'], compass=True))

# Compile, taking dependencies into count when ordering operations.
bm.run()

# Same as above, but providing command line arguments such as --clean, and --rebuild.
bm.as_app()
```
nsfc-spider 0.1.6
国自然大数据平台智能爬虫 - AI驱动的基金项目数据采集工具
Requires Python: >=3.11
Keywords: data-mining, funding, nsfc, research, scraper, spider

# NSFC Spider - 国自然大数据平台智能爬虫

🕷️ AI驱动的国家自然科学基金项目数据采集工具

基于 [browser-use](https://github.com/browser-use/browser-use) 框架，使用大语言模型智能理解网页结构，自动适应页面变化。

## 特性

- 🤖 AI 驱动：使用 LLM 理解网页，自动适应页面变化
- 💬 对话式 CLI：交互式命令行界面
- 🔧 灵活配置：支持多种 LLM 提供商
- 📊 结构化输出：JSON/CSV/Markdown 格式导出

## 安装

### 方式一：使用 pip 安装（推荐）

```bash
# 1. 安装包
pip install nsfc-spider

# 2. 安装浏览器
playwright install chromium
```

### 方式二：从源码安装

```bash
# 1. 克隆仓库
git clone https://github.com/yourusername/nsfc-spider.git
cd nsfc-spider

# 2. 安装依赖
pip install -e .

# 3. 安装 Playwright 浏览器
playwright install chromium
```

## 配置

### 快速配置（推荐）

安装后首次运行，使用交互式配置：

```bash
nsfc-spider
```

在 CLI 中输入 `/config` 按提示配置 LLM API。

### 手动配置

创建配置文件 `config/.env` 或 `.env`：

```env
# LLM 配置
LLM_PROVIDER=openai
LLM_MODEL=gpt-4o
LLM_API_KEY=your-api-key-here
LLM_BASE_URL=https://api.openai.com/v1

# 浏览器配置
HEADLESS=false
```

**获取 API Key**：
- OpenAI: https://platform.openai.com/api-keys
- Anthropic: https://console.anthropic.com/
- DeepSeek: https://platform.deepseek.com/

详细配置说明请查看 [配置指南](docs/CONFIGURATION.md)。

## 快速开始

### 使用命令行工具

```bash
# 安装后直接运行
nsfc-spider

# 或使用 Python 模块方式
python -m nsfc_spider.cli
```

### 开发模式运行

```bash
# 从源码目录运行
cd nsfc-spider
python run.py
```

## CLI 命令

```
nsfc> help          # 查看帮助
nsfc> config        # 配置 LLM 和浏览器
nsfc> search 智慧港口    # 搜索项目
nsfc> search 人工智能 -p 3  # 搜索并爬取3页
nsfc> task <自然语言任务>   # 执行自定义任务
nsfc> status        # 查看配置状态
nsfc> exit          # 退出
```

## 支持的 LLM

- OpenAI (gpt-4o, gpt-4o-mini)
- Anthropic (claude-sonnet-4)
- DeepSeek
- Ollama (本地模型)

## 项目结构

```
nsfc-spider/
├── src/nsfc_spider/
│   ├── __init__.py
│   ├── agent.py      # Agent 封装
│   ├── config.py     # 配置管理
│   ├── cli/          # CLI 模块
│   └── tasks/        # 爬取任务
│       ├── direct_scraper.py   # 直接爬取
│       ├── nsfc_search.py      # AI搜索
│       ├── analyzer.py         # 数据分析
│       ├── llm_filter.py       # LLM筛选
│       └── review_generator.py # 综述生成
├── config/
│   └── .env.example
├── tests/            # 测试文件
├── docs/             # 文档
├── run.py            # 开发入口
├── main.py           # 打包入口
└── pyproject.toml    # 项目配置
```

## 文档

- 📖 [安装指南](docs/INSTALLATION.md) - 详细的安装步骤
- 🔧 [配置指南](docs/CONFIGURATION.md) - 环境变量和 API 配置
- 📚 [使用指南](docs/USAGE.md) - 完整的功能说明和示例
- 🤝 [贡献指南](docs/CONTRIBUTING.md) - 如何参与项目开发
- 🚀 [发布指南](docs/RELEASE.md) - 版本发布流程

## 许可证

MIT License - 详见 [LICENSE](LICENSE) 文件

## 贡献

欢迎提交 Issue 和 Pull Request！查看 [贡献指南](docs/CONTRIBUTING.md) 了解详情。

## 更新日志

查看 [CHANGELOG.md](CHANGELOG.md) 了解版本更新历史。

## 致谢

- [browser-use](https://github.com/browser-use/browser-use) - 核心浏览器自动化框架
- [Playwright](https://playwright.dev/) - 浏览器控制
- 所有贡献者和用户

## 联系方式

- 📦 PyPI: https://pypi.org/project/nsfc-spider/
- 🐛 问题反馈: [GitHub Issues](https://github.com/yourusername/nsfc-spider/issues)
bclearer-interop-services 0.7.0
"A set of interop services to integrate and transfer data between different applications, model and storage technologies for the bclearer framework."
Requires Python: >=3.12

# bclearer-interop-services

A set of I/O and interop connectors for the bclearer framework. It provides adapters to read and write data between in-memory “universe” representations and a variety of storage, file formats, and application services.

## Installation

```bash
pip install bclearer-interop-services
```

## Key Features

- **Dictionary Service**
  Convert data to and from generic Python dictionaries (e.g., mapping objects to table dictionaries).
- **DataFrame Service**
  Utilities for standardizing, filtering, merging, and converting Pandas (and PySpark) DataFrames.
- **Delimited Text**
  Read/write CSV and other delimited formats.
- **Excel Services**
  Import/export Excel (.xlsx) files.
- **JSON, XML, HDF5, Parquet**
  Native serializers and readers for common data formats.
- **Relational Database Services**
  Access MS Access, SQLite, and other RDBMS via SQL interfaces.
- **Document Store Services**
  MongoDB and JSON file store support.
- **Graph Services**
  Neo4j connector and network analysis utilities.
- **EA Interop Service**
  COM-based, SQL, and XML import/export for Enterprise Architect models.
- **Session & Orchestration**
  Helpers to manage connections, sessions, and orchestrate multi-step data flows.

## Basic Usage

Below is a simple example using the Dictionary and DataFrame services:

```python
from bclearer_interop_services.b_dictionary_service.table_as_dictionary_service import TableAsDictionaryFromCsvFileReader
from bclearer_interop_services.b_dictionary_service.table_as_dictionary_service import TableAsDictionaryToDataFrameConverter

# Read data from a CSV file into a table-as-dictionary
reader = TableAsDictionaryFromCsvFileReader()
table_dict = reader.read('data/example.csv')

# Convert the table-as-dictionary to a Pandas DataFrame
converter = TableAsDictionaryToDataFrameConverter()
df = converter.convert(table_dict)

# Standardize column names and filter rows using the DataFrame service
from bclearer_interop_services.dataframe_service.dataframe_helper import DataFrameHelper

helper = DataFrameHelper()
df = helper.standardize_column_names(df)
df_filtered = df[df['status'] == 'ACTIVE']
```

## Documentation

Full documentation and examples can be found in the [GitHub repository](https://github.com/OntoLedgy/ol_bclearer_pdk/tree/develop/libraries/interop_services).

## License

This project is licensed under the MIT License. See the [LICENSE](../../LICENSE) file for details.
GPTRouter 1.0.8
A Python package for working with GPTRouter APIs

# GPTRouter Python Client

This is a Python client for the GPTRouter APIs.

## Installation

Use the package manager [pip](https://pypi.org/en/stable) to install gpt-router.

```bash
pip install gpt-router
```

## Usage

```python
from gpt_router.client import GPTRouterClient
from gpt_router.models import ModelGenerationRequest, GenerationParams
from gpt_router.enums import ModelsEnum, ProvidersEnum


client = GPTRouter(base_url='your_base_url', api_key='your_api_key')

messages = [
    {"role": "user", "content": "Write me a short poem"},
]
prompt_params = GenerationParams(messages=messages)
claude_request = ModelGenerationRequest(
    model_name=ModelsEnum.CLAUDE_4_5_HAIKU.value,
    provider_name=ProvidersEnum.CHAT_ANTHROPIC.value,
    order=1,
    prompt_params=prompt_params,
)

response = client.generate(ordered_generation_requests=[claude_request])
print(response.choices[0].text)
```

Remember to replace `'your_base_url'` and `'your_api_key'` with your actual base URL and API key when using the client.
cloud-pak-operations-cli 0.11.3
IBM Cloud Pak Operations CLI
Requires Python: >=3.12

# IBM Cloud Pak Operations CLI

<div align="center">
    <p>
        <a href="https://github.com/IBM/cloud-pak-operations-cli/blob/master/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/IBM/cloud-pak-operations-cli?style=for-the-badge"></a>
	    <a href="https://github.com/IBM/cloud-pak-operations-cli/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/IBM/cloud-pak-operations-cli?style=for-the-badge"></a>
        <a href="https://github.com/IBM/cloud-pak-operations-cli/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/IBM/cloud-pak-operations-cli?style=for-the-badge"></a>
        <a href="https://github.com/IBM/cloud-pak-operations-cli/actions?query=workflow%3A%22Python+Testing%22+branch%3Amaster"><img alt="GitHub Workflow Status (branch)" src="https://img.shields.io/github/actions/workflow/status/IBM/cloud-pak-operations-cli/python-package.yml?branch=master&style=for-the-badge"></a>
    </p>
</div>

The IBM Cloud Pak Operations CLI provides basic functionality to manage Red Hat OpenShift clusters and IBM Cloud Pak for Data on various cloud platforms on top of the OpenShift CLI (oc) and the [IBM Cloud Pak for Data CLI](https://github.com/IBM/cpd-cli) (cpd-cli).

Documentation can be found [here](https://ibm.github.io/cloud-pak-operations-cli/).
tyme4py 1.4.4
Tyme是一个非常强大的日历工具库，可以看作 Lunar 的升级版，拥有更优的设计和扩展性，支持公历、农历、藏历、星座、干支、生肖、节气、法定假日等。
Requires Python: >=3.10
Keywords: 公历, 农历, 藏历, 儒略日, 星座, 干支, 节气, 法定假日

# Tyme [![License](https://img.shields.io/badge/license-MIT-4EB1BA.svg?style=flat-square)](https://github.com/6tail/tyme4py/blob/master/LICENSE)

Tyme是一个非常强大的日历工具库，可以看作 [Lunar](https://6tail.cn/calendar/api.html "https://6tail.cn/calendar/api.html") 的升级版，拥有更优的设计和扩展性，支持公历、农历、藏历、星座、干支、生肖、节气、法定假日等。

> 自v1.4.3起，要求python >= 3.10

## 示例

    $ pip install tyme4py
     
    from tyme4py.solar import SolarDay
     
    # 通过指定年月日初始化公历日
    solar_day = SolarDay.from_ymd(1986, 5, 29)
     
    # 1986年5月29日
    print(solar_day.__str__())
     
    # 农历丙寅年四月廿一
    print(solar_day.get_lunar_day().__str__())
     
    # 第十七饶迥火虎年四月廿一
    print(solar_day.get_rab_byung_day().__str__())

## 文档

请移步至 [https://6tail.cn/tyme.html](https://6tail.cn/tyme.html "https://6tail.cn/tyme.html")

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=6tail/tyme4py&type=Date)](https://star-history.com/#6tail/tyme4py&Date)

## 致谢
1. 感谢许剑伟老师分享的寿星天文历，本项目节气算法引自 [https://github.com/sxwnl/sxwnl](https://github.com/sxwnl/sxwnl "https://github.com/sxwnl/sxwnl")
2. 感谢[stonelf](https://github.com/stonelf "https://github.com/stonelf")，本项目藏历数据引自 [https://github.com/stonelf/zangli](https://github.com/stonelf/zangli "https://github.com/stonelf/zangli")