Metadata-Version: 2.4
Name: unvibecode
Version: 0.3.1
Summary: Turn complex code into connected LLM context, business workflows, and critical risk findings
Author: UnvibeCode
License-Expression: LicenseRef-Proprietary
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: jsonschema>=4.20
Requires-Dist: openai<3,>=2.50
Requires-Dist: pyvis>=0.3.2
Requires-Dist: PyYAML>=6.0
Requires-Dist: tiktoken>=0.8
Requires-Dist: tinycss2>=1.2
Requires-Dist: tree-sitter-language-pack>=0.9
Provides-Extra: dev
Requires-Dist: build>=1.2; extra == "dev"
Requires-Dist: pytest>=8.0; extra == "dev"

# UnvibeCode

## Vibe the prototype. Unvibe the codebase.

**Turn complex vibe code into verified engineering—understand its connections,
business workflows, and critical business risks.**

Vibe coding works well for prototypes. As a repository grows, behavior becomes
spread across files, services, state changes, and external calls. UnvibeCode
reviews that repository as one connected system and produces customer-ready
HTML reports plus reusable LLM context.

## Install and review in two commands

```bash
pip install unvibecode
```

```bash
python -m unvibecode review --repository "/path/to/repository"
```

Windows example:

```powershell
python -m unvibecode review --repository "D:\upstox"
```

macOS or Linux users can use `python3` when that is their installed Python
command:

```bash
python3 -m unvibecode review --repository "/Users/example/repository"
```

That is the complete customer setup:

- No activation or access key.
- No OpenAI API key or `.env` file.
- No virtual environment, wheel, ZIP, or PATH configuration.
- The repository path is the only required input.
- Results save automatically to `./shipready_results`.

## What UnvibeCode produces

### 1. Connected Code Map for LLMs

Large repositories are difficult to understand one file at a time. The
interactive code map shows meaningful file, symbol, import, and call
connections. Select a file to download a compact package containing that file
and its relevant connected code, ready to give to an LLM.

![Connected Code Map showing contextually connected files and an LLM-ready download](https://shipready-api.alphashots.ai/unvibecode/readme/connected_code_map_example.png)

### 2. Complete Repository Context

Repeatedly rebuilding repository context for every LLM conversation is slow
and inconsistent. UnvibeCode creates one normalized ZIP containing ordered
code chunks, verified connections, context selections, metadata, and usage
instructions for later LLM or API workflows.

```text
complete_repository_context_for_llm.zip
├── manifest.json
├── chunks.jsonl
├── connections.jsonl
├── selections.jsonl
└── README.md
```

### 3. Business Workflow Map

A technical file tree does not explain what the software does for customers or
the business. UnvibeCode reconstructs related code paths as understandable
workflows, showing what starts a workflow, the important decisions and state
changes, and the resulting business outcome.

![Illustrative workflow connecting checkout, payment processing, and fulfilment](https://shipready-api.alphashots.ai/unvibecode/readme/business_workflow_map_generic_example.png)

### 4. Business Risk Findings

Technical scanners can miss failures that emerge across a connected business
workflow. UnvibeCode identifies critical business workflow risks in the code
and shows the supporting evidence. Each finding explains the trigger, what the
code does, the business rule, the downside, affected customers or assets, what
to change, and how to verify the correction.

![Illustrative payment risk with business path, remediation, acceptance check, and code evidence](https://shipready-api.alphashots.ai/unvibecode/readme/business_risk_findings_generic_payment_example.png)

If no candidate passes the evidence threshold, the report clearly records a
no-findings outcome for the completed review scope.

> The images are illustrative examples. Workflows and findings are generated
> from the repository being reviewed.

## Customer results

For a supported repository, the automatically created results folder contains:

```text
shipready_results/
└── analysis_<timestamp>/
    └── customer_results/
        ├── 01_connected_code_map_for_llm.html
        ├── complete_repository_context_for_llm.zip
        ├── 02_business_workflow_map.html
        └── 03_business_risk_findings.html
```

The terminal displays continuous progress. When the review completes, the HTML
reports open automatically.

## Large repositories

Repositories above approximately two million estimated source tokens receive
the Connected Code Map and Complete Repository Context. The deeper business
workflow and risk review does not run above that threshold.

## Initial public observation period

During the initial two-day public observation period, UnvibeCode requires no
activation, access key, customer API key, usage charge, or customer quota.
Technical request-size and concurrency protections remain in place to keep the
service reliable. Usage controls may be introduced in a later package version
after actual demand is understood.

## Data processing

Repository scanning, graph construction, connected-context preparation, and
report rendering run on the customer's computer. Relevant structured code
context is sent securely to the hosted UnvibeCode analysis service for the
business workflow and risk review. Customers do not provide an OpenAI key.

Review only repositories you are authorized to process.

## Requirements

- Python 3.11 or newer.
- Internet access for the hosted business analysis.
- Read access to the repository being reviewed.
