hailoc12/e2e_test_report_for_vibe_coding — explained in plain English
Analysis updated 2026-05-18
Run an automated QA pass on a product you built with AI coding tools before sharing it.
Get a scored report across multiple quality criteria backed by screenshots.
Check an AI chatbot feature in your product for made-up or inaccurate answers.
Generate a DOCX or PDF report to share test results with teammates or stakeholders.
| hailoc12/e2e_test_report_for_vibe_coding | 0c33/agentic-ai | adennng/stock_strategy_lab | |
|---|---|---|---|
| Stars | 14 | 14 | 14 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | hard | hard |
| Complexity | 2/5 | 4/5 | 4/5 |
| Audience | vibe coder | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Designed to run as a skill inside an AI coding agent rather than standalone.
This project is a testing skill that runs end to end tests against the live link of any AI generated, or vibe coded, product and automatically produces a polished quality assurance report. Rather than relying on a gut feeling that a product seems to work, it aims to give concrete test results, backed by a five layer test matrix, scores across seven criteria, a list of detected features, and screenshots as evidence for each conclusion. According to the README, the generated report is delivered as a DOCX or PDF file. Example output shown in the README includes a cover page with a large colored score badge, a scoring table across seven criteria with color coded rating cells and a highlighted total row, and detail cards explaining each individual criterion. If the tested product includes an AI assistant or a retrieval augmented chatbot, the skill also separately grades the accuracy of the AI responses by checking each answer against a source of truth, specifically to catch cases where the AI invents information that isn't true. The README's guiding idea is stated directly: don't trust what people say about a product, trust the test results. This positions the tool as a way for people who built something quickly using AI coding tools to get an honest, evidence backed assessment of whether their product actually works, before showing it to others or shipping it. The project is written in Python and is packaged as a skill, suggesting it is meant to be invoked by an AI coding assistant or agent framework rather than run as a standalone application. Because the excerpt does not include installation or usage commands, exact setup steps for running it are not shown here.
A testing skill that runs automated end-to-end tests on AI-built products and generates a scored QA report with screenshots as evidence.
Mainly Python. The stack also includes Python.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly vibe coder.
This repo across BitVibe Labs
Verify against the repo before relying on details.