self-improvement-frontend-design-skill-loop-for-codex

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

self-improvement-frontend-design-skill-loop-for-codex

★ 13 Open GitHub ↗

No description available.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Improved Frontend Skills for GPT Codex

This project is an experiment in making GPT Codex produce much stronger frontend design outputs through instruction tuning and iterative self-correction loops.

## Thesis

Can Codex be pushed toward Opus-like visual quality by repeatedly refining `frontend-design` skill instructions and critiquing outputs against strong reference sets?

This repo treats frontend quality as an optimization loop:

1. Keep the prompt fixed.
2. Change skill instructions intentionally.
3. Generate output.
4. Critique against target visuals.
5. Repeat.

## Why I built this

A couple of days ago, I watched Theo's video on which model performs best for frontend design:

- Video: https://www.youtube.com/watch?v=f2FnYRP5kC4
  [![Theo video: best model for frontend design](https://i.ytimg.com/vi/f2FnYRP5kC4/hqdefault.jpg)](https://www.youtube.com/watch?v=f2FnYRP5kC4)

In that video, the benchmark paired GPT with the `frontend-design` skill written by Anthropic for Claude models. That made me question whether this is a fair portability benchmark across model families.

My hypothesis was simple: Codex is highly instruction-sensitive. If we tune the skill language specifically for Codex, with clear constraints and quality signals, we should be able to push outputs much closer to Opus-level frontend quality.

This repository is that experiment.

## Important clarification

This project does **not** fine-tune base model weights.

When we say "influence weights" here, we mean practical instruction weighting:

- stronger priority rules,
- tighter quality gates,
- explicit anti-failure constraints,
- iterative critique feedback.

The objective is to influence model behavior reliably, not to modify model internals.

## Core experiment design

- One canonical benchmark prompt: `prompt.md`
- Versioned experiments under `experiments/version-X/`
- Each version is self-contained:
  - `.agents/skills/frontend-design/SKILL.md`
  - `t4-canvas/`
  - `README.md`
  - `CRITQUES.md`
  - `screenshots/` for `/1` to `/5`
- New versions are isolated by default (fresh app workspace, no inherited generated UI code).

## Optimization strategy

Two experiment topologies are used:

- Vertical: sequential refinement from best baseline.
- Horizontal: parallel sibling branches with different mutation axes.

Each iteration changes one primary hypothesis so we can attribute quality changes.

## Success criteria

We treat success as measurable movement toward the target visual bar:

- stronger hierarchy and composition,
- better concept divergence,
- less generic "AI slop" structure,
- better polish, readability, and interaction quality,
- closer fit to Opus-with-skill reference traits.

## Reference targets

- `research/targetted-designs/`
- `research/theo-screenshots-2k-clean/opus45_with_skill/`
- `research/theo-screenshots-2k-clean/opus_iterations/`

## Quick start

1. Read `AGENTS.md`.
2. Keep `prompt.md` unchanged for fair comparisons.
3. Run experiments from inside `experiments/version-X/`.
4. Use `.agents/skills/frontend-design-improvements-loop/SKILL.md` as the operational workflow.

self-improvement-frontend-design-skill-loop-for-codex

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README