brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Generate a polished frontend reference screenshot through a configurable GPT-image-2-compatible generations endpoint, save and show versioned previews inside the project, revise selected previews through a configurable /images/edits endpoint, and reproduce only the user-approved
Claude Code
$ npx -y skills add jinshiqwq/image-first-frontend --skill image-first-frontend --agent claude-code
Codex
$ npx skills add jinshiqwq/image-first-frontend --skill image-first-frontend -a codex -y
Cursor
$ npx skills add jinshiqwq/image-first-frontend --skill image-first-frontend -a cursor -y
Gemini CLI
$ npx skills add jinshiqwq/image-first-frontend --skill image-first-frontend -a gemini-cli -y
GitHub Copilot
$ npx skills add jinshiqwq/image-first-frontend --skill image-first-frontend -a github-copilot -y
OpenCode
$ npx skills add jinshiqwq/image-first-frontend --skill image-first-frontend -a opencode -y
Windsurf
$ npx skills add jinshiqwq/image-first-frontend --skill image-first-frontend -a windsurf -y
name: image-first-frontend description: Generate a polished frontend reference screenshot through a configurable GPT-image-2-compatible generations endpoint, save and show versioned previews inside the project, revise selected previews through a configurable /images/edits endpoint, and reproduce only the user-approved design as functional frontend code with high visual fidelity. Use when Codex is asked to design, build, restyle, or recreate a web page, application screen, dashboard, landing page, or UI component through an image-first workflow, design approval loop, screenshot-driven implementation, pixel matching, or a 1:1 frontend recreation.
Generate a visual target before writing UI code. Let the user approve, edit, or redo versioned previews, then recreate the approved target at its exact viewport and adapt it responsively without using the screenshot as the page itself.
1. Read `scripts/generate_reference.py` before the first API call. 2. Configure `GENERATIONS_API_URL`, `EDITS_API_URL`, and `API_KEY` at the top of the script. The URLs may point to any compatible provider; no OpenAI connection is required. Require the generations URL for a new design and the edits URL only when revising an existing preview. 3. Work inside the user's existing project and preserve its framework, design system, dependency choices, and local conventions. 4. Save every preview inside the current project under `design-previews/<page>/`. Use numbered files such as `reference-v01.png`; the helper refuses to overwrite an existing output. 5. Make one billable generation or edit at a time. Make another request only after technical failure or an explicit user request to revise or redo the design.
Write a concise product brief and call the configured generations endpoint:
python <skill-dir>/scripts/generate_reference.py --brief "<product and page requirements>" --output <workspace>/design-previews/<page>/reference-v01.png --size 1536x1024
Use `--brief-file <path>` instead of `--brief` when the prompt is long. The helper requests a flat, implementation-ready webpage screenshot without browser chrome, device frames, perspective, or surrounding scenery.
Inspect the generated file only for technical failure such as an empty image, severe corruption, or an unusable crop. Do not silently discard a successful preview for aesthetic reasons and do not begin implementation yet.
1. Display the generated image in the conversation with the available local image-viewing capability. Also give the user its project-relative path and version number; do not merely state that the file exists. 2. Stop before extracting an implementation spec or editing frontend files. Ask the user to choose one of three outcomes: approve this version, request specific changes, or redo it with a new direction. 3. On explicit approval, record the approved preview path and continue to implementation. Do not infer approval from silence, unrelated feedback, or a request to inspect the image. 4. On specific changes, edit the currently selected preview through `EDITS_API_URL`. Merge the original hard requirements with two clear blocks, `Preserve` and `Change`, then run:
python <skill-dir>/scripts/generate_reference.py --input-image <workspace>/design-previews/<page>/reference-v01.png --brief "Preserve: <accepted elements>. Change: <requested edits>." --output <workspace>/design-previews/<page>/reference-v02.png --size 1536x1024
Add `--mask <path>` only when a compatible mask is available and the configured endpoint supports it. Display the new version and return to this approval gate. If the edits endpoint rejects the request, surface its error; do not silently fall back to text-only generation because that loses image-conditioned fidelity.
5. On a redo request, preserve only the original product and content constraints, choose a materially different visual direction, and call the generations endpoint again without `--input-image`. Save the next numbered version, display it, and return to this approval gate. 6. If dissatisfaction is vague, ask one concise direction question before spending another request. Each explicit revise or redo request authorizes one new image, not a batch of alternatives. 7. Keep all preview versions for comparison. Do not delete, import into the application, or add them to version control unless the user asks.
Read the approved reference image and record the following in working notes:
Estimate measurements from pixels rather than replacing the design with generic defaults. Treat garbled image-generated text as a visual placeholder; correct user and product copy takes precedence.
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.