semvec/pypi-packages huggingface dataset:
lumberjack-cli 0.1.0
Git worktree manager for parallel Claude Code development
Requires Python: >=3.11

# Lumberjack

Git worktree manager for parallel coding agent development.

## Install

```bash
uv tool install .
# or
uv pip install -e .
```

## Quick Start

```bash
cd your-repo
lj init --defaults          # create .lumberjack.toml
lj new auth-refactor        # create worktree + branch
lj new --type fix           # auto-named fix worktree
lj ls                       # list all worktrees
cd $(lj cd auth-refactor)   # jump into worktree
lj rm auth-refactor         # clean up
```

## Commands

| Command | Description |
|---------|-------------|
| `lj init` | Interactive setup, write `.lumberjack.toml` |
| `lj new [name]` | Create worktree (aliases: `create`) |
| `lj ls` | List worktrees (aliases: `list`). `--json`, `--paths` |
| `lj rm <name>` | Remove worktree (aliases: `remove`). `--force`, `--keep-branch`, `--all` |
| `lj cd <name>` | Print worktree path. `--shell` for subshell |
| `lj status` | Git status across all worktrees |
| `lj sync [name]` | Rebase/merge on base branch. `--all`, `--merge` |
| `lj agent [name]` | Launch coding agent in worktree. `--new` |
| `lj run-init [name]` | Re-run dependency install |
| `lj env sync [name]` | Re-copy .env files from main repo |
| `lj env ls` | List discovered .env files |
| `lj env diff [name]` | Show .env differences |
| `lj pr [name]` | Create PR via gh CLI. `--draft` |
| `lj clean` | Prune stale worktrees. `--dry-run` |
| `lj config show` | Print resolved config |
| `lj config set <k> <v>` | Set config value |
| `lj config edit` | Open config in $EDITOR |
| `lj shell-init` | Output shell integration script |

## Config

`.lumberjack.toml` in repo root:

```toml
[lumberjack]
worktree_dir = ".lj"
branch_template = "{user}/{type}/{name}"
user = "nc9"
default_type = "feature"
base_branch = "main"
naming_scheme = "minerals"  # minerals | cities | compound
default_agent = "claude"   # claude | codex | opencode | aider

[lumberjack.init]
auto_init = true
# init_command = "bun run init"
# post_init = ["echo done"]

[lumberjack.env]
auto_copy = true
patterns = [".env", ".env.*", "!.env.example", "!.env.template"]
scan_depth = 3

[lumberjack.submodules]
auto_init = true
recursive = true

[lumberjack.agent]
auto_launch = false
inject_context = true
```

## Shell Integration

```bash
# Add to .zshrc / .bashrc:
eval "$(lj shell-init)"

# Then use:
ljcd obsidian       # cd into worktree
lj-prompt           # worktree name for PS1
```

## Development

```bash
uv sync
make test     # pytest
make lint     # ruff + basedpyright
make fmt      # ruff format
make check    # all of the above
```
torc-client 0.12.3
Workflow management system
Requires Python: <3.14,>=3.11
Keywords: hpc, workflow, pipeline

# Workflow Management System

## Components

### Python CLI (`pytorc`)
Command-line interface for workflow management operations.

### Web Dashboard (`torc-dash`)
A web-based dashboard for managing and monitoring Torc workflows. Provides an intuitive interface for:
- Viewing and filtering workflows, jobs, results, events, files, and other resources
- Running workflows locally or submitting to HPC/Slurm
- Real-time monitoring with auto-refresh
- Interactive tables with sorting and filtering

See [src/torc/dash_app/README.md](src/torc/dash_app/README.md) for detailed documentation.

## Installation

```bash
cd python_client
pip install -e .
```

## Usage

### Web Dashboard

```bash
torc-dash
```

Then open your browser to http://localhost:8050

### CLI

```bash
pytorc --help
```
trustworthy-llm 0.0.1
Core functionality for TLM
Requires Python: >=3.10

# Trustworthy Language Model (TLM)

The [Trustworthy Language Model](https://cleanlab.ai/blog/trustworthy-language-model/) scores the **trustworthiness** of outputs from *any* LLM in *real-time*.

Automatically detect hallucinated/incorrect responses in: Q&A (RAG), Chatbots, Agents, Structured Outputs, Data Extraction, Tool Calling, Classification/Tagging, Data Labeling, and other LLM applications.

Use TLM to:
- Guardrail AI mistakes before they are served to user
- Escalate cases where AI is untrustworthy to humans
- Discover incorrect LLM (or human) generated outputs in datasets/logs
- Boost AI accuracy

Powered by *uncertainty estimation* techniques, TLM **works out of the box**, and does **not** require: <br>
data preparation/labeling work or custom model training/serving infrastructure.

Learn more and see precision/recall benchmarks with frontier models (from OpenAI, Anthropic, Google, etc): <br>
[Blog](https://cleanlab.ai/blog/), [Research Paper](https://aclanthology.org/2024.acl-long.283/)

## Usage

See [notebooks](notebooks) for Jupyter notebooks with example usage.
derivkit 1.1.0
A robust toolkit for stable numerical derivatives
Requires Python: >=3.10

# derivkit

[![CI](https://github.com/derivkit/derivkit/actions/workflows/ci.yml/badge.svg)](https://github.com/derivkit/derivkit/actions/workflows/ci.yml)
[![Deploy Sphinx Documentation](https://github.com/derivkit/derivkit/actions/workflows/docs.yml/badge.svg)](https://github.com/derivkit/derivkit/actions/workflows/docs.yml)
[![License](https://img.shields.io/github/license/derivkit/derivkit)](LICENSE)


**DerivKit** is a robust Python toolkit for stable numerical derivatives, built for scientific computing, cosmology, and any domain requiring accurate gradients or higher-order expansions.

Detailed documentation, examples, and API reference can be found at the [derivkit documentation](https://docs.derivkit.org).


## License
MIT License © 2025 Niko Šarčević, Matthijs van der Wild, Cynthia Trendafilova and contributors.
temp-text 2026.2.1
A simple package to allow for text on the terminal which is overwritten by the next call to print.
Requires Python: >=3.8
Keywords: ANSI, Terminal

# temp_text

## usage

```python
from time import sleep

import temp_text

for i in range(100):
    if i % 10 == 0:
        print(f"Found special number: {i}")
    temp_text.prnt(f"Looking at {i}")
    sleep(0.3)
```

```python
from time import sleep
from itertools import *

import temp_text

for c in chain.from_iterable(tee("-\\|/", 10)):
    temp_text.prnt(c)
    sleep(0.3)
```

```python
import os

import temp_text

for d, _, _ in os.walk("/"):
    temp_text.prnt(d)
```

## description

A simple package to allow for text on the terminal which is
overwritten by the next call to print.

## prnt

Print a temporary string to the console.
Must not contain newline characters, because
only the line the cursor is currently on is cleared.
This function calls hide if there is a temporary string
on the console.

If trim_to_width is True (this is the default), then
the string has some characters in the middle replaced with
ellipsis so it does not span multiple lines.

## hide

Clears the line the cursor is currently on
if there is a temporary string there.

Hide is also called if the program exits.

## use_write_guards

If this setting is True (this is the default) then 
calling regular print (or any other function that invokes
sys.stdout.write or sys.stderr.write) first clears the current
line, assuming there is a temporary string there.

```python
import temp_text
temp_text.use_write_guards = False
temp_text.prnt("Hello, World")
# Output: "Hello, World"
print("Hello, World!")
# Output: "Hello, WorldHello, World!"
# If you inserted a temp_text.hide() before the print,
# then the output would be the expected "Hello, World!"
# use_write_guards = True does this automatically
```
datasette-pretty-traces 0.7
Prettier formatting for ?_trace=1 traces
Requires Python: >=3.10

# datasette-pretty-traces

[![PyPI](https://img.shields.io/pypi/v/datasette-pretty-traces.svg)](https://pypi.org/project/datasette-pretty-traces/)
[![Changelog](https://img.shields.io/github/v/release/simonw/datasette-pretty-traces?include_prereleases&label=changelog)](https://github.com/simonw/datasette-pretty-traces/releases)
[![Tests](https://github.com/simonw/datasette-pretty-traces/workflows/Test/badge.svg)](https://github.com/simonw/datasette-pretty-traces/actions?query=workflow%3ATest)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-pretty-traces/blob/main/LICENSE)

Prettier formatting for `?_trace=1` traces

## Installation

Install this plugin in the same environment as Datasette.
```bash
datasette install datasette-pretty-traces
```
## Usage

Once installed, run Datasette using `--setting trace_debug 1`:
```bash
datasette fixtures.db --setting trace_debug 1
```
Then navigate to any page and add `?_trace=` to the URL:

    http://localhost:8001/?_trace=1

The plugin will scroll you down the page to the visualized trace information.

## Demo

You can try out the demo here:

- [/?_trace=1](https://latest-with-plugins.datasette.io/?_trace=1) tracing the homepage
- [/github/commits?_trace=1](https://latest-with-plugins.datasette.io/github/commits?_trace=1) tracing a table page

## Screenshot

![Screenshot showing the visualization produced by the plugin](https://user-images.githubusercontent.com/9599/145883732-a53accdd-5feb-4629-94cd-f73407c7943d.png)

## Development

To set up this plugin locally, checkout the code and run the tests with `uv run pytest`:
```bash
cd datasette-pretty-traces
uv run pytest
```
To try your development version of plugin run it like this:
```bash
uv run datasette fixtures.db --setting trace_debug 1
```
bsky2masto 0.1.0
Find Mastodon/Fediverse accounts from Bluesky follows and generate import CSV files
Requires Python: >=3.10
Keywords: bluesky, mastodon, fediverse, migration, social-graph

# bsky2masto

Find followable Mastodon/Fediverse accounts from the people you follow on Bluesky, then generate a Mastodon import CSV.

## What it does

Given a Bluesky account, `bsky2masto`:

1. Fetches followed accounts via the public Bluesky API
2. Extracts possible Fediverse handles from bios/display names
3. Optionally checks Bridgy Fed opt-in (`@handle@bsky.brid.gy`)
4. Optionally verifies discovered handles with WebFinger
5. Writes:
   - `mastodon-import.csv` (Mastodon "Following list" import format)
   - `matches.csv` (detailed match diagnostics)

## Installation

### Recommended (end users): pipx

```bash
pipx install bsky2masto
```

> If the first PyPI release is not live yet, use the "From source" section below.

### One-shot usage: uvx

```bash
uvx bsky2masto --actor your-handle.bsky.social --include-bridgy
```

### Standard pip

```bash
python -m pip install bsky2masto
```

### From source (development)

```bash
uv sync --group dev
uv run bsky2masto --actor your-handle.bsky.social --include-bridgy
```

## Quickstart

```bash
bsky2masto \
  --actor your-handle.bsky.social \
  --output mastodon-import.csv \
  --matches-output matches.csv \
  --include-bridgy
```

Useful flags:

- `--verify` → verify discovered handles via WebFinger
- `--max-follows N` → scan only first `N` follows (faster testing)
- `--scan-workers N` → worker count for verify/bridgy checks (default: `8`)
- `--bridgy-pause-ms N` → per-check Bridgy pause (default: `150`)
- `--quiet` → suppress progress logs
- `--version` → print CLI version

## Import into Mastodon

1. Open your Mastodon instance
2. Go to **Preferences → Import and export → Import**
3. Data type: **Following list**
4. Upload `mastodon-import.csv`

## Exit codes

- `0` success
- `1` network/HTTP/unexpected runtime error
- `2` CLI usage error (argparse)

## Limitations and expectations

- Best-effort parsing: false positives are possible.
- Always inspect `matches.csv` before importing.
- `--verify` and Bridgy checks perform additional network requests and may be slower.
- Some profile strings (emails, non-Mastodon `@` patterns) can still look handle-like.

## Development

```bash
uv sync --group dev
uv run ruff check .
uv run pytest -q
uv build
```

Supported Python versions: 3.10+

## Security

For vulnerability reports, see [SECURITY.md](SECURITY.md).
passagemath-docbuild 10.8.1
passagemath: Build system of the Sage documentation
Requires Python: <3.15,>=3.11

================================================================================
 passagemath: Build system of the Sage documentation
================================================================================

`passagemath <https://github.com/passagemath/passagemath>`__ is open
source mathematical software in Python, released under the GNU General
Public Licence GPLv2+.

It is a fork of `SageMath <https://www.sagemath.org/>`__, which has been
developed 2005-2025 under the motto “Creating a Viable Open Source
Alternative to Magma, Maple, Mathematica, and MATLAB”.

The passagemath fork was created in October 2024 with the following
goals:

-  providing modularized installation with pip, thus completing a `major
   project started in 2020 in the Sage
   codebase <https://github.com/sagemath/sage/issues/29705>`__,
-  establishing first-class membership in the scientific Python
   ecosystem,
-  giving `clear attribution of upstream
   projects <https://groups.google.com/g/sage-devel/c/6HO1HEtL1Fs/m/G002rPGpAAAJ>`__,
-  providing independently usable Python interfaces to upstream
   libraries,
-  providing `platform portability and integration testing
   services <https://github.com/passagemath/passagemath/issues/704>`__
   to upstream projects,
-  inviting collaborations with upstream projects,
-  `building a professional, respectful, inclusive
   community <https://groups.google.com/g/sage-devel/c/xBzaINHWwUQ>`__,
-  developing a port to `Pyodide <https://pyodide.org/en/stable/>`__ for
   serverless deployment with Javascript,
-  developing a native Windows port.

`Full documentation <https://passagemath.org/docs/latest/html/en/index.html>`__ is
available online.

passagemath attempts to support all major Linux distributions and recent versions of
macOS. Use on Windows currently requires the use of Windows Subsystem for Linux or
virtualization.

Complete sets of binary wheels are provided on PyPI for Python versions 3.10.x-3.14.x.


About this pip-installable distribution package
-----------------------------------------------

This is the build system of the Sage documentation, based on Sphinx.
fastkmeanspp 0.2.1
KMeans++ clustering algorithm
Requires Python: >=3.10

# 📊 KMeans++

**fastkmeanspp** is a Python package that implements a KMeans clone from `scikit-learn` but with a much faster centroid initialization and optimized for speed with FAISS. It is designed to be a drop-in replacement for `scikit-learn`'s KMeans implementation.

---

## 🚀 Installation

```bash
pip install fastkmeanspp
```

## 📖 Learn More

For tutorials, API reference, visit the official site:  
👉 [fastkmeanspp's documentation](https://felixlaplante0.github.io/fastkmeanspp)