semvec/pypi-packages huggingface dataset:
fix-cli 0.5.0
AI-powered command fixer with contract-based dispute resolution
License: MIT
Requires Python: >=3.10

# fix

AI-powered command fixer. A command fails, an LLM diagnoses it, proposes a fix, and a contract system tracks the whole thing. Disputes go to an AI judge.

## Quick start

```sh
pip install git+https://github.com/karans4/fix.git
```

### Local mode (you need an API key)

```sh
export ANTHROPIC_API_KEY=sk-ant-...   # or OPENAI_API_KEY, or run Ollama

fix "gcc foo.c"         # run command, fix if it fails
fix it                  # fix the last failed command
fix --explain "make"    # just explain the error
fix --dry-run "make"    # show fix without running
fix --local "make"      # force Ollama (free, local)
```

### Market mode (free platform agent)

Post a contract to the platform. A free AI agent picks it up and proposes a fix.

```sh
fix --market "gcc foo.c"
```

Platform: `https://fix.notruefireman.org` (free during testing)

Configure in `~/.fix/config.py`:
```python
platform_url = "https://fix.notruefireman.org"
remote = True  # default to remote mode
```

## Shell integration

For `fix it` / `fix !!` to work, add to your shell config:

```sh
# bash/zsh
eval "$(fix shell)"

# fish
fix shell fish | source

# or auto-install
fix shell --install
```

## Safe mode (sandbox)

Default on Linux. Runs fixes in OverlayFS -- changes only committed if verification passes.

```sh
fix "make build"          # sandbox on Linux by default
fix --no-safe "make"      # skip sandbox
fix --safe "make"         # force sandbox
```

## Verification

```sh
fix "gcc foo.c"                              # default: re-run, exit 0 = success
fix --verify=human "python3 render.py"       # human judges
fix --verify="pytest tests/" "pip install x"  # custom command
```

## How it works

1. Command fails, stderr captured
2. Contract built (task, environment, verification terms, escrow)
3. Agent investigates (read-only commands), then proposes fix
4. Fix applied, verified mechanically
5. Multi-attempt: up to 3 tries, feeding failures back as context
6. Disputes go to an AI judge who reviews the full transcript

## Architecture

- `fix` -- CLI entry point
- `server/` -- FastAPI platform (contracts, escrow, reputation, judge)
- `protocol.py` -- state machine, constants
- `scrubber.py` -- redacts secrets from error output before sending to LLM
- `contract.py` -- builds structured contracts
- `client.py` / `agent.py` -- remote mode client and agent

## License

MIT
chemparseplot 1.1.0
Parsers and plotting tools for computational chemistry
License: MIT
Requires Python: >=3.10
Keywords: compchem, parser, plot


# Table of Contents

-   [About](#org1aaacbd)
    -   [Ecosystem Overview](#org86a8ea7)
    -   [Features](#org2d23f41)
        -   [Supported Engines [WIP]](#orgd14b359)
    -   [Rationale](#orgd89d807)
-   [License](#orgef56819)



<a id="org1aaacbd"></a>

# About

![img](branding/logo/chemparseplot_logo.png)

[![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch)

A **pure-python**<sup><a id="fnr.1" class="footref" href="#fn.1" role="doc-backlink">1</a></sup> project to provide unit-aware uniform visualizations
of common computational chemistry tasks. Essentially this means we provide:

-   Parsers for various computational chemistry software outputs
-   Plotting scripts for specific workflows

Computational tasks (surface fitting, structure analysis, interpolation) are
handled by [`rgpycrumbs`](https://github.com/HaoZeke/rgpycrumbs), which is a required dependency. `chemparseplot` parses
output files, delegates heavy computation to `rgpycrumbs`, and produces
publication-quality plots.

This is a spin-off from `wailord` ([here](https://wailord.xyz)) which is meant to handle aggregated
runs in a specific workflow, while here the goal is to do no input handling and
very pragmatic output parsing, with the goal of generating uniform plots.


<a id="org86a8ea7"></a>

## Ecosystem Overview

`chemparseplot` is part of the `rgpycrumbs` suite of interlinked libraries.

![img](branding/logo/ecosystem.png)


<a id="org2d23f41"></a>

## Features

-   [Scientific color maps](https://www.fabiocrameri.ch/colourmaps/) for the plots
    -   Camera ready
-   Unit preserving
    -   Via `pint`


<a id="orgd14b359"></a>

### Supported Engines [WIP]

-   ORCA (**5.x**)
    -   Scanning energies over a degree of freedom (`OPT` scans)
    -   Nudged elastic band (`NEB`) visualizations (over the "linearized" reaction
        coordinate)


<a id="orgd89d807"></a>

## Rationale

`wailord` is for production runs, however often there is a need to collect
"spot" calculation visualizations, which should nevertheless be uniform, i.e.
either Bohr/Hartree or Angstron/eV or whatever.

Also I couldn't find (m)any scripts using the scientific colorschemes.


<a id="orgef56819"></a>

# License

MIT. However, this is an academic resource, so **please cite** as much as possible
via:

-   The Zenodo DOI for general use.
-   The `wailord` paper for ORCA usage


# Footnotes

<sup><a id="fn.1" href="#fnr.1">1</a></sup> To distinguish it from my other thin-python wrapper projects