Audit and improve UI surfaces while preserving product identity.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionimprove-uiExecute the skills CLI command in your project's root directory to begin installation:
Fetches improve-ui from ibelick/ui-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate improve-ui. Access via /improve-ui in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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| name | improve-ui |
| description | Audit an existing product surface against its own design evidence, identify verified UI problems, and write self-contained implementation plans for another agent. Strictly read-only on product source. Use when asked to review, refine, improve, or clean up an interface without replacing its identity; investigate design-system drift; or prepare a design handoff. |
Audit one coherent product surface against the system that actually governs it. Preserve the product's identity, reuse existing owners, and prefer no finding to an unsupported one. Write plans only for changes the user selects; another agent executes them.
design-plans/.Honor the user's scope. If the request is broad, select one deployable application and one coherent surface family representing a primary product task. State the selection; do not synthesize the whole repository into one product.
Start from the surface's routes and layouts. Trace the rendered path through compositions, shared components, variants, resolved tokens, and styles. Do not begin with a repository-wide search for inconsistencies.
A connection exists only when it is proven through rendering, imports, props, resolved configuration, CSS inheritance, or a generated artifact loaded by the surface. Shared names, similar tokens, repository proximity, and conceptual relationships do not establish a connection. Exclude other applications, previews, configurators, generated registries, legacy systems, and enterprise variants unless they participate in the traced path.
Check for DESIGN.md, repository guidance, and surface-local design documentation. Use a source only after proving it is current and governs the selected surface; drafts, proposals, migrations, and task lists describe future intent unless explicitly accepted and current. Absence of design documentation is not a finding.
Inspect only the tokens, variables, themes, primitives, variants, and compositions relevant to the traced path. Resolve aliases and variants to their definitions. Classify an implementation as local or legacy only when the repository says so.
Record:
## Design language
- Audited surface:
- Design sources:
- Documented decisions:
- Governing owners and consumers:
- Explicit exceptions:
Write None documented under Explicit exceptions unless a cited source explicitly identifies the exception.
Before applying the proof gate, inspect every traced surface's user-facing labels, active-state presentation, responsive branches, and sibling variants for internal contradictions. Treat the results only as candidates.
A finding is in scope only when its correction primarily changes visual presentation, interface copy, layout, component styling, or conformance to a documented design rule. If the correction primarily changes whether product behavior works, reject it.
Search results, repetition, and implementation differences produce candidates, not findings. Keep a candidate only when all three proofs exist:
Source can prove token, typography, color, spacing, layout, copy, component-variant, responsive-presentation, and explicit design-contract violations. It cannot turn functional behavior, state management, or interaction correctness into design findings. Hierarchy, prominence, density, clarity, discoverability, usability, and perceived coherence require rendered or user evidence.
Discard accessibility and HTML/ARIA semantic findings unless the user explicitly requests them. Discard broken routes, redirects, data wiring, action failures, metadata, package API, performance, architecture, and code-quality findings unless the user requested them or a product-specific design contract governs them.
Assign confidence only after all proofs pass. Reuse an existing owner when the evidence supports it; do not create a shared primitive from repetition alone.
Before reporting, re-open every cited source and try to falsify each candidate. Delete it when:
Only findings that survive this pass may enter the table.
Order surviving findings by confidence, user impact, reach, and correction cost. Stop at three.
Use this structure:
## Design language
- Audited surface:
- Design sources:
- Documented decisions:
- Governing owners and consumers:
- Explicit exceptions:
## Findings
| # | Problem | Evidence | Proposed change | Scope | Confidence |
| --- | --- | --- | --- | --- | --- |
## Improve first
<Highest-leverage finding and why, or no supported recommendation.>
Evidence must establish the contract, runtime relationship, and deterministic interface consequence. Proposed change must contain one correction. Delete unsupported or overlapping rows before returning.
Delete any finding that does not include every required column, including Confidence.
Under Improve first, select one surviving finding with the strongest evidence and highest leverage. Never combine findings.
If no candidate survives, write No supported findings were found. under ## Findings and No supported recommendation. under ## Improve first.
If findings survive, stop and ask which to turn into plans. If the user already selected a finding or explicitly requested a plan for a described improvement, continue with that scope. If asked to fix or improve directly, offer a plan; never implement it.
Read references/plan-template.md. Write one plan per selected change, never one per symptom.
Before writing, re-open every cited source, record the current commit when available, identify exact reusable primitives and exemplars, and trace affected surfaces. Reconcile an existing plan instead of duplicating it.
Do not invent values when the repository provides a token or component contract. Introduce a primitive only after proving why the existing system cannot express the decision and which consumers should share it.
If asked to reconcile, recheck existing plans against current source and documented decisions; update stale evidence, affected surfaces, and status.
https://github.com/ibelick/ui-skills/blob/main/skills/improve-ui/SKILL.md - this is the repo link
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
your-github-username/design-md
shadcn/improve
diffusionstudio/lottie
anthropics/claude-code
sickn33/antigravity-awesome-skills
leonxlnx/taste-skill
improve-ui fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in improve-ui — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for improve-ui matched our evaluation — installs cleanly and behaves as described in the markdown.
improve-ui fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added improve-ui from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend improve-ui for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: improve-ui is focused, and the summary matches what you get after install.
Keeps context tight: improve-ui is the kind of skill you can hand to a new teammate without a long onboarding doc.
improve-ui is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
improve-ui has been reliable in day-to-day use. Documentation quality is above average for community skills.
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