semvec/pypi-packages huggingface dataset:
catchfn 0.0.1
Server implementation for CatchFn bug reporting
Requires Python: >=3.8

# catchfn

Python server implementation for CatchFn bug reporting.

## Installation

```bash
pip install catchfn sfns
```

## Usage

### FastAPI Example

```python
from fastapi import FastAPI
from superfunctions.db import Adapter # Import your specific adapter
from catchfn import CatchFnService, create_catchfn_router
from superfunctions.fastapi import to_fastapi # Hypothetical adapter

app = FastAPI()
db = Adapter(...) # Initialize your DB adapter

service = CatchFnService(db)
router = create_catchfn_router(service)

# Mount the router
app.include_router(to_fastapi(router), prefix="/api/catchfn")
```

## Features

- Works with `superfunctions` abstractions
- Stores bug reports with full context
- Pydantic models for type safety
cxv-cli 0.1.0
CXV.ONE CLI
Requires Python: >=3.9

# cxv-cli

CLI for CXV.ONE.

## Install (local)

This CLI is bundled in the CXV.ONE repo. Install it from the repo path:

```bash
pip install /opt/discord-bot-builder/packages/cxv-cli
```

Optional editable install for development:

```bash
pip install -e /opt/discord-bot-builder/packages/cxv-cli
```

## Login

```bash
cxv login --email you@example.com --password "..."
```

## Usage

```bash
cxv whoami
cxv projects list
cxv projects create --name "My Bot"
cxv workflows list --project-id <project-id>
cxv workflows publish --workflow-id <workflow-id>
cxv workflows deploy --workflow-id <workflow-id>
```

## Notes
- CLI stores an auth cookie in `~/.cxv/config.json`.
- Use `cxv logout` to clear the session.
ppxai 0.1.0
Perplexity AI search from your terminal
Requires Python: >=3.9
Keywords: ai, cli, perplexity, search

# ppx

Perplexity AI search from your terminal. Zero dependencies.

## Install

```bash
pip install ppxai
```

## Setup

Get an API key from [perplexity.ai/settings/api](https://www.perplexity.ai/settings/api), then:

```bash
export PERPLEXITY_API_KEY=pplx-...
```

Or save it permanently:

```bash
mkdir -p ~/.config/ppx
echo "PERPLEXITY_API_KEY=pplx-..." > ~/.config/ppx/config
chmod 600 ~/.config/ppx/config
```

## Usage

```bash
# Default: research mode (sonar-pro)
ppx "best practices for Python async"

# Quick question
ppx --ask "latest Python version"

# Direct web results (no AI synthesis)
ppx --search "FastAPI middleware" --max-results 5

# Chain-of-thought reasoning
ppx --reason "Redis vs PostgreSQL for sessions"

# Deep research (requires cost confirmation)
ppx --deep --yes-expensive "comprehensive guide to OpenTelemetry"

# JSON output (pipe to jq, scripts, etc.)
ppx --json "test query" | python -m json.tool
```

## Modes & Cost

| Flag | Model | Cost/query | Use for |
|------|-------|------------|---------|
| *(default)* | sonar-pro | ~$0.05 | General research |
| `--ask` | sonar | ~$0.01 | Quick factual questions |
| `--search` | *(direct)* | ~$0.01 | Raw web results |
| `--reason` | sonar-reasoning-pro | ~$0.10 | Complex decisions |
| `--deep` | sonar-deep-research | ~$3.00 | Exhaustive research |

`--deep` requires `--yes-expensive` to prevent accidental spend.

## Options

| Flag | Description |
|------|-------------|
| `--json` | Output raw JSON |
| `--max-results N` | Max results for `--search` (default: 10) |
| `--recency PERIOD` | Filter: day, week, month, year |
| `--domains DOMAIN` | Limit to specific domains |
| `--model MODEL` | Override model selection |
| `--yes-expensive` | Confirm `--deep` queries |

## License

MIT
tokenizesentences 0.3
Python module to tokenize english sentences.
Requires Python: >=3.10
Keywords: tokenize, english, sentences

# tokenizesentences
*Python module to tokenize english sentences.*
Based on the answer of D Greenberg in StackOverflow:
https://stackoverflow.com/questions/4576077/python-split-text-on-sentences

## Installation
### Install with uv:
```
uv add tokenizesentences
```

## Usage
```
In [1]: import tokenizesentences

In [2]: m = tokenizesentences.SplitIntoSentences()

In [3]: m.split_into_sentences(
    "Mr. John Johnson Jr. was born in the U.S.A but earned his Ph.D. in Israel before joining Nike Inc. as an engineer. He also worked at craigslist.org as a business analyst."
    )

Out[3]: 
[
    'Mr. John Johnson Jr. was born in the U.S.A but earned his Ph.D. in Israel before joining Nike Inc. as an engineer.',
    'He also worked at craigslist.org as a business analyst.'
]
```
uploade 1.8.0
Collective memory for AI agents
Requires Python: >=3.9

# Uploade

Collective memory for AI agents. One agent learns, all agents benefit.

## Install
```bash
pip install uploade
```

## Quick Start
```python
from uploade import Uploade

u = Uploade(api_key="your_key")  # Get key at https://uploade.org/setup

# Search when stuck
results = u.search("python", tags=["async"])
warnings = u.warnings("python", ["async"])
tips = u.tips("python", ["database"])

# Get full content
content = u.get("experience-id")

# Share learnings (anonymized!)
u.share(
    category="python",
    title="Connection pooling prevents DB exhaustion",
    content="Problem: Too many connections.\nCause: No pooling.\nSolution: Use connection pool.\nResult: Stable under load.",
    tags=["database", "connections"],
    type="solution"
)
```

## Register
```python
u = Uploade()
data = u.register(
    agent_name="my-agent",
    wallet_address="0x...",
    tweet_url="https://x.com/user/status/123"
)
# data["api_key"] is your key
```

## API Docs
https://uploade.org/docs

## Links
- Website: https://uploade.org
- Setup: https://uploade.org/setup
- GitHub: https://github.com/uploadeorg/uploade
codemate-cli 1.0.1
AI-powered CLI assistant with rich streaming interface
Requires Python: >=3.8
Keywords: cli, ai, assistant, llm

# 🚀 CodeMate CLI

CodeMate CLI is a premium AI-powered command-line assistant designed to provide high-performance, context-aware coding assistance directly in your terminal. It features a beautiful streaming interface, rich markdown rendering, and a powerful Knowledge Base system to index your codebase or documentation.

## ✨ Key Features

- 🧠 **Context-Aware AI** - Chat with an AI that understands your specific codebase and documentation.
- 📚 **Knowledge Base Management** - Create, index, and manage local codebase or web-based knowledge bases.
- 🎨 **Premium Terminal UI** - Enjoy a sleek interface with real-time markdown rendering and syntax highlighting.
- 🌊 **Streaming Responses** - Experience zero-latency with real-time response streaming.
- 🔐 **Secure Authentication** - Seamless web-based login with local session management.
- 🔧 **Automatic Service Handling** - Self-managing background services for optimal performance.

---

## 📦 Installation

Install the latest version of CodeMate CLI directly from PyPI:

```bash
pip install codemate-cli
```

---

## 🚀 Getting Started

### 1. Initial Setup
CodeMate CLI requires a one-time setup of its internal environment. 

### 2. Authentication
On your first run, the CLI will prompt you to authenticate:

1.  Run `codemate-cli`.
2.  Follow the provided link to `https://identity.codemate.ai/`.
3.  Login and copy your **Access Token**.
4.  Paste the token back into the terminal.

### 3. Start Chatting
Once authenticated, you will enter the interactive mode. Simply type your question:
```text
You ❯ Explain the authentication flow in this project.
```

---

## 📚 Knowledge Base (KB) Usage

The most powerful feature of CodeMate is its ability to understand your files.

### Creating a Knowledge Base
You can index a local directory or a set of documentation URLs:

1.  Type `/createkb` in the interactive session.
2.  Choose **Codebase** to index a local folder.
    - Provide the absolute path to your project.
    - The CLI will automatically respect your `.gitignore` and index relevant files.
3.  Choose **Docs** to index web-based documentation (provide URLs).

### Referencing a KB in Chat
Once a KB is created (e.g., named "my-api"), you can reference it using the `@` symbol:

```text
You ❯ How do I add a new route in @my-api?
```
The AI will use the indexed files from `my-api` as context to provide a precise answer.

---

## 🛠️ Command Reference

### Special Commands (Inside Interactive Mode)
| Command | Description |
| :--- | :--- |
| `/help` | Show the help menu with all available commands |
| `/listkb` | See all your indexed Knowledge Bases and their status |
| `/createkb` | Start the workflow to index a new codebase or documentation |
| `/deletekb` | Remove an existing Knowledge Base |
| `/clear` | Clear the current conversation history |
| `/logout` | Remove your session and log out |
| `exit` | Safely exit the CodeMate environment |




© 2026 CodeMate.AI. All rights reserved.
databricks-ddbxutils 0.4.0
extends databricks dbutils
Requires Python: <4.0,>=3.11

# databricks-ddbxutils

dbutils 로 부족한 부분을 확장한 ddbxutils

## Feature

* [x] `dbutils.widgets` 에 jinja2 template 적용

## setup

```shell
cd <PROJECT_ROOT>
pip install poetry
```

## venv

```shell
poetry shell
```

## Build

```shell
poetry build
```

## Run

### in databricks w/o init_script(= Serverless)

* Add Wheel
  * wheel upload 용 Volume 생성 후 upload
    * `/Volumes/<CATALOG>/<DATABASE>/<VOLUME_NAME>/ddbxutils-<VERSION>-py3-none-any.whl`
  * notebook 의 우측 Environment 에서 Environment version 2로 지정 후 volume 에 upload 한 wheel file 추가 후 Apply
* Usage
  ```python
  # dbutils.widgets.text('rawdate', '2025-05-24', 'Raw Date')
  # dbutils.widgets.text('next_day', '{{add_days(rawdate, "%Y-%m-%d", "", 1)}}', 'Next Day')
  import ddbxutils
  next_day = ddbxutils.widgets.get('next_day')
  # next_day: 2025-05-25
  ```

### in databricks w/ init_script

* Add Wheel
  * wheel upload 용 Volume 생성 후 upload
    * `/Volumes/<CATALOG>/<DATABASE>/<VOLUME_NAME>/ddbxutils-<VERSION>-py3-none-any.whl`
  * Libraries
    * `/Volumes/<CATALOG>/<DATABASE>/<VOLUME_NAME>/ddbxutils-<VERSION>-py3-none-any.whl`
* `/Volumes/<CATALOG>/<DATABASE>/<VOLUME_NAME>/init_script_ddbxutils.sh`
  ```shell
  #! /bin/bash

  STARTUP_SCRIPT=/tmp/pyspark_startup.py

  cat >> ${STARTUP_SCRIPT} << EOF

  prefix = 'PYTHONSTARTUP_ddbxutils'
  print(f'{prefix} custom startup script loading...')
  try:
    import ddbxutils
    print(f'{prefix} Custom modules [ddbxutils] are loaded.')
  except Exception as e:
    print(f'{prefix} e={e}')
    print(f'{prefix} import ddbxutils failed')
  EOF
  ```
* Spark config
  ```text
  spark.executorEnv.PYTHONSTARTUP /tmp/pyspark_startup.py
  ```
* Environment variables
  ```shell
  PYTHONSTARTUP=/tmp/pyspark_startup.py
  ```
* Init scripts
  ```text
  /Volumes/<CATALOG>/<DATABASE>/<VOLUME_NAME>/init_script_ddbxutils.sh
  ```
* Usage
  ```python
  # dbutils.widgets.text('rawdate', '2025-05-24', 'Raw Date')
  # dbutils.widgets.text('next_day', '{{add_days(rawdate, "%Y-%m-%d", "", 1)}}', 'Next Day')
  next_day = ddbxutils.widgets.get('next_day')
  # next_day: 2025-05-25
  ```
kobo-mcp 0.1.4
MCP server for KoboToolbox - deploy surveys and fetch submissions
Requires Python: >=3.10
Keywords: anthropic, claude, kobo, kobotoolbox, mcp, survey, xlsform

# Kobo MCP

[![PyPI version](https://badge.fury.io/py/kobo-mcp.svg)](https://pypi.org/project/kobo-mcp/)

An MCP server for KoboToolbox that enables Claude to deploy surveys and fetch submissions.

**[Documentation](https://bbdaniels.github.io/kobo-mcp/)**

## Features

- **list_forms** - List all KoboToolbox surveys
- **get_form** - Get detailed form information
- **get_submissions** - Fetch survey responses
- **deploy_form** - Upload and deploy XLSForm files
- **replace_form** - Update existing forms preserving submissions
- **export_data** - Export data as CSV or Excel

## Installation

```bash
pip install kobo-mcp
```

Or run directly with uvx (no install):

```bash
uvx kobo-mcp
```

## Configuration

### 1. Get your API token

Go to [KoboToolbox](https://kf.kobotoolbox.org) → Account Settings → Security → Display

### 2. Add to Claude

Add to `~/.claude.json`:

```json
{
  "mcpServers": {
    "kobotoolbox": {
      "type": "stdio",
      "command": "kobo-mcp",
      "env": {
        "KOBO_API_TOKEN": "your-token-here"
      }
    }
  }
}
```

Or with uvx:

```json
{
  "mcpServers": {
    "kobotoolbox": {
      "type": "stdio",
      "command": "uvx",
      "args": ["kobo-mcp"],
      "env": {
        "KOBO_API_TOKEN": "your-token-here"
      }
    }
  }
}
```

For EU server, add `"KOBO_SERVER": "https://eu.kobotoolbox.org"` to env.

## Development

```bash
git clone https://github.com/bbdaniels/kobo-mcp.git
cd kobo-mcp
pip install -e .
```

## License

MIT
openbq 0.1.0
Open Source Biometric Quality Framework Command-Line Interface (bqConnect CLI).
Requires Python: <3.15,>=3.10
Keywords: openbq, bqconnect, cli, biometric

# bqConnect CLI

[![PyPI - Version](https://img.shields.io/pypi/v/openbq)](https://pypi.python.org/pypi/openbq)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/openbq)](https://pypi.python.org/pypi/openbq)
[![PyPI - Format](https://img.shields.io/pypi/format/openbq)](https://pypi.python.org/pypi/openbq)
[![PyPI - License](https://img.shields.io/pypi/l/openbq)](https://pypi.python.org/pypi/openbq)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/openbq)](https://pypi.python.org/pypi/openbq)

> bqconnect CLI (`openbq`) is part of the openbq framework. openbq is an open-source tool designed to empower biometric quality assessment, for more details please visit: [openbq](https://openbq.io).

> bqconnect CLI provides a command line interface to access biometric data quality evaluations for various modalities, including [face](https://docs.openbq.io/modalities/face.html), [fingerprint](https://docs.openbq.io/modalities/fingerprint.html), [iris](https://docs.openbq.io/modalities/iris.html), and [voice](https://docs.openbq.io/modalities/voice.html).

## Highlights

- 🚀 An easy‑to‑use tool for working with the `openbq` framework.
- 🖥️ Compatible with macOS, Linux, and Windows.

## Prerequisites

- [Docker](https://www.docker.com/)

## Quick Start

1. Install

    ```sh
    pip install openbq
    ```

2. Display version info

    ```sh
    openbq --version
    ```

3. Validate installation (run benchmarking task)

    ```sh
    openbq --benchmark
    ```

4. Run biometric quality assessment

    ```sh
    openbq --mode face --input data/face
    ```

5. Run biometric quality assessment and compile a EDA report

    ```sh
    openbq --mode fingerprint --input data/fingerprint --report
    ```

## Option flags

| Flag | Description |
| --- | --- |
| --help | Print help info. |
| --version, -V | Display version info. |
| --update, -U | Update `openbq` backend service container. |
| --uninstall | Uninstall `openbq`. |

> Please refer to the [openbq](https://docs.openbq.io/) documentations for further details.
snail-mcp 0.1.2
General-purpose MCP server (Excel read and more).
Requires Python: >=3.13

# snail-mcp

General-purpose MCP server.

## Setup

```bash
uv sync
```

## Run MCP server

After `uv sync`, use either:

```bash
uv run snail-mcp
```

or (with venv activated) run the script directly:

```bash
snail-mcp
```

Alternative: `uv run python -m snail_mcp.server`

## Run via uvx

```bash
uvx snail-mcp
```

If you see `ImportError: cannot import name 'main' from 'mcp.server'`, the cached package is old. Clear cache then retry:

```bash
uv cache clean snail-mcp
uvx snail-mcp
```

## Publish

1. Build: `uv build`
2. Upload: `uv publish` (need PyPI token) or `twine upload dist/*`
3. Then anyone can run: `uvx snail-mcp`

## Tools

- **read_excel**: Read .xlsx cells (value, formula, style). Params: filepath, out (v/b/f/n/z/c/g), fmt (j/t), sheets, row/col range, sparse.

---

*This README was generated by AI.*