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Design React interfaces with the full shadcn/ui catalog. The agent writes a brief, Jev picks components and a style recipe from bounded catalogs, then the agent implements, renders, and reviews against concrete checks.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Each part of the work goes to whoever does it best:
| Phase | Who | Output |
|---|---|---|
| 1. Brief | the agent running the skill | brief.json |
| 2. Pick | Jev, through scripts/plan.py | a component per requirement, a style recipe, install commands, and CSS |
| 3. Compose and build | the agent, with the frontend skill | layout, hierarchy, copy, custom parts, and working code |
| 4. Review | the agent, with any screenshot tool | concrete findings, then fixes or a revised brief |
Jev only picks from lists: a component from the catalog's candidates and a recipe from its styles. It does not write layout, copy, or code. The agent does all open-ended work. Everything below is a default. Override any step when you have a stated reason, and write the reason in your summary.
Turn the user's request into brief.json. Read references/brief-writing.md for the procedure and a worked example.
{"brief":"Support agents triage 200+ cases a day on desktop; calm, dense, keyboard-first.",
"style_traits":["dense","calm","professional"],
"requirements":[
{"id":"results","role":"Compare, filter, and act on open cases","capabilities":["tabular-data","filter","row-actions"]},
{"id":"details","role":"Inspect one case without leaving the list","capabilities":["overlay","details"]}
]}
--list-capabilities. Use only listed names."primitives": "radix" | "base" | "aria" when the project already uses one primitive set.The planner has no vision input and picks components, not layout. Read the screen (DOM, a screenshot) yourself and write the brief from it: one requirement per region, each with 1 or 2 capabilities and a role written as a verb plus an object. Three capabilities per requirement abstained with no_compatible_component; vague roles abstained with low_fitness, and verb-plus-object roles selected (data-table 0.94). Layout stays with you in phase 3.
python3 skills/frontend/jev-design/scripts/plan.py \
--input brief.json --output /tmp/design-plan.json \
--receipt /tmp/jev-design-receipt.json --css /tmp/design-theme.css
The planner filters the catalog by capability in code: each requirement gets at most six candidates, tightest fit first. It packs requests to 3,500 tokens or fewer and refuses to send any request over 4,500. It sends through the selectable Jev transport (Vercel AI Gateway when configured), which retries 429, 503, and 529 with jittered backoff. It validates every answer and accepts a pick only when its fitness is 0.62 or higher. It never installs anything or edits code.
When the plan is selected, implementation holds:
install: the exact npx shadcn@latest add ... command, plus npm for extra dependencies.css: paste-ready @theme inline, :root, and .dark blocks.fonts: the families to load.layout_tokens: radius, density classes, and shadow.contrast: every checked WCAG pair and its ratio.When the plan abstains, the output still holds baseline and candidates:
| Reason | Do |
|---|---|
no_compatible_component | Fix the listed capabilities, or split the requirement. |
low_fitness, none_or_out_of_catalog | Rewrite that requirement's role as a verb plus an object, and rerun. If it abstains again, build that part as a custom component (phase 3). |
low_style_fitness | Add or change style_traits, and rerun. If it abstains again, pick from --list-capabilities styles yourself. |
jev_unavailable, malformed_response | Use the baseline, check it against each candidate's use_when, and say in your summary that Jev did not pick. |
request_too_large, invalid_request | Shorten the brief or roles as the message says. |
Run the planner at most three times per screen.
Use the frontend skill. Follow this order:
init (if needed), install, and npm exactly as the plan gives them.css into the app's global stylesheet, replacing the existing theme variables. Load fonts.layout_tokens everywhere: the same control height, row height, gap, and section spacing on every part of the screen.item + dropdown-menu, card + chart). Write new markup only when that fails, and reuse the recipe's variables.Build with UI design judgment and its recipes. Render the screen and review it with references/visual-review.md. Use the harness's screenshot tool (Playwright or chrome-devtools MCP). Capture 375, 768, and 1280 px widths in light and dark mode (set deviceScaleFactor 2 for the 375 px shots you send to Jev).
brief.json, and rerun phase 2.visual-review.md to check by eye, and do not claim visual quality.Apply the Jev production rules to any change in how requests are built or sent. They cover request size, retries by status, and budget checks. Keep the worst-case size test passing: too much context is the most common failure. The planner picks among catalog candidates, and Jev leans toward the first option on some inputs: keep candidate order out of the signal (tightest fit first is a code ordering, so test a reversed order on labeled requirements before trusting a pick). Pin a versioned model ID for planner calibration; jev-latest moves between versions. Read references/decision-card.md before you change the model, catalog, rubric, threshold, bounds, failure policy, or promotion status. references/catalog-v1.json is kept for reference only.
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Manage multiple Claude Code accounts: add, list, check, launch, and install shell aliases for 10+ isolated CLAUDE_CONFIG_DIR profiles.
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Improve architecture across modules by deepening interfaces.
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Assessment: read-only inspection, codebase overview, value analysis, health checks, ADR consultation, decision analysis, multi-perspective critique.
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Background memory consolidation — overnight review, merge, and injection payload for memory files.
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Jev-driven browser automation: Jev picks operations, programs execute, a text model writes field values only when Jev cannot pick one from the goal.
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Write, compose, integrate, and improve programs that call Jev, TypeSafe's System One judgment model.
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