Analysis updated 2026-07-09 · repo last pushed 2026-07-07
Audit a checkout flow by feeding in screen notes and getting back visual diagrams plus a checklist of responsive risks.
Generate before-and-after flow diagrams to communicate proposed UI fixes to designers or engineers.
Produce evidence-backed design documentation that includes accessibility checks and acceptance tests for prioritized fixes.
| vibeforge1111/domain-chip-ui-flow-auditor | 0xhassaan/nn-from-scratch | a-little-hoof/dsr | |
|---|---|---|---|
| Stars | — | 0 | 0 |
| Language | Python | Python | Python |
| Last pushed | 2026-07-07 | — | — |
| Maintenance | Active | — | — |
| Setup difficulty | moderate | moderate | hard |
| Complexity | 3/5 | 4/5 | 5/5 |
| Audience | pm founder | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Runs within the Spark ecosystem and optionally needs the Mermaid CLI installed to render visual SVG diagrams.
domain-chip-ui-flow-auditor takes messy product-screen notes, screenshot descriptions, and user-flow reports and turns them into clean, implementation-ready UI audit packs. If you've ever tried to communicate what's wrong with a screen flow to a designer or engineer using only a wall of text, this tool gives you structured diagrams, prioritized fixes, and validation evidence instead. At a high level, it's a "chip", a plug-in for a system called Spark, that runs a Python script to process your input. You feed it a JSON file describing a UI flow, and it generates a bundle of useful artifacts. These include before-and-after flow diagrams (using a popular diagramming tool called Mermaid), automated accessibility and readability checks, and a list of prioritized fixes with acceptance tests. It also validates the output against a schema to make sure the data is structured correctly, and if you have the Mermaid CLI installed, it will render visual SVG diagrams for you. This is built for product managers, designers, or technical founders working within the Spark ecosystem who need to produce evidence-backed design documentation. For example, if you're auditing a checkout flow, you could feed in notes about each screen and get back a visual diagram of the current flow, a diagram of the proposed fixed flow, and a checklist of responsive or narrow-viewport risks. It's designed to create documentation that proves you've actually thought through failure and retry states, rather than just writing prose. A notable aspect of this project is its strict boundary around what it claims to do. It explicitly refuses to certify things like conversion lift, revenue improvement, or official accessibility compliance. It positions itself as a diagnostic tool that produces local evidence, leaving official quality scoring to human reviewers (or "blind judges"). This tradeoff keeps the tool honest, it generates structured findings and implementation notes, but it doesn't overpromise on business outcomes it can't actually measure.
Turns messy product-screen notes and user-flow reports into clean UI audit packs with flow diagrams, accessibility checks, and prioritized fixes. Runs as a Python-based plug-in for the Spark ecosystem.
Mainly Python. The stack also includes Python, Mermaid, JSON.
Active — commit in last 30 days (last push 2026-07-07).
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly pm founder.
This repo across BitVibe Labs
Verify against the repo before relying on details.