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Dxe

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kemiljk
dxe

Review a repository or UI surface for design engineering quality. Use for a DXE pass or broad interface review; fix only when requested and load specialist guidance from evidence.

Overview

Publisherkemiljk
Repositoryskills
Skill namedxe
Stars
64
Forks
3
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kemiljk on GitHub. Read the source before you install it.

Installation

Install the Dxe AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kemiljk/skills/tree/main/skills/dxe
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kemiljk/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/dxe .claude/skills/dxe
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dxe in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dxe on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dxe is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

dxe

Review interface work in this precedence order:

  1. Intent before decoration.
  2. Correct semantics and accessibility before visual convenience.
  3. Interaction feel before implementation technique; use the most native implementation that preserves the intended feel.
  4. Production trust before prototype cleverness.

Read references/dxe-core.md before starting. It is the shared rubric for every mode. Do not load every sibling skill by default.

Modes

Infer the mode from the request; use review when unspecified.

A path after dxe scopes any mode to that path. Treat “apply all skills” as dxe exhaustive.

ModeScopeMutation
dxe quickShared rubric on one representative surface; load no sibling unless essentialNo
dxe / dxe reviewFull evidence matrix and representative repo sampling; progressively load triggered siblingsNo
dxe fixReview, then implement and verify bounded high-confidence fixesYes, within the requested scope
dxe exhaustiveInspect every available surface and read the full sibling suiteNo unless the user also asks to fix

Review mode is strictly non-mutating. Do not edit files, install dependencies, start external mutations, or change production systems. Read-only inspection and local verification are allowed. In fix mode, preserve unrelated work and existing design-system materials.

Scope and sampling

Identify the repository or paths, primary user tasks, stack, tokens, primitives, and interaction/runtime libraries. Prefer existing materials over invented replacements.

For a repository review, select representative surfaces rather than implying equal inspection of every screen:

  • primary landing or task page;
  • content/detail and list/index pages when present;
  • one form or mutation flow;
  • one overlay, dialog, or menu;
  • an available loading, empty, or error state;
  • primary and contrasting target form factors, relevant input methods, and reduced-motion variants.

State omissions in the report. In exhaustive mode, enumerate all discoverable surfaces and states before inspection.

Evidence protocol

Write a short hypothesis and non-goals, then collect the minimum evidence appropriate to the mode. For normal review/fix, record:

EvidenceMinimum
MaterialsStack, tokens, primitives, motion/runtime libraries
SourceRepresentative routes and shared components
VerificationExisting typecheck/tests and production build, when safe and available
Form factorsRendered primary and contrasting layouts, clipping/overflow, and task hierarchy
InteractionFirst meaningful input sequence; overlay/menu/sheet state, return, and touch/pointer/keyboard parity where relevant
AccessibilityPlatform names, roles/traits/semantics, states, focus/return, and reduced motion
ProductionLoading/error/empty behaviour, platform metadata, and platform-specific runtime logs
Confidenceconfirmed-rendered, confirmed-source, likely, unverified, or environment-only

Do not claim a rendered defect from source inspection alone. If rendering or a check is unavailable, label the limit instead of guessing.

Separate observations into:

  1. confirmed product defects;
  2. source-level risks;
  3. environment or tooling failures;
  4. unverified suspicions.

Reproduce a user-facing failure before promoting tooling noise to a product finding. Distinguish browser errors, server errors, expected local-service failures, and framework/tooling warnings.

For checks that trigger a state change, wait for an observable settled condition before recording state. Prefer the target platform accessibility/semantics value, focus destination, transition completion, or animation settlement over an arbitrary delay. Do not report the immediate post-action sample as final evidence when rendering or animation is still in flight.

Choose the verification lane that matches the delivered platform:

PlatformRequired runtime evidence
WebBrowser console.error, uncaught pageerror, server logs, responsive overflow, keyboard sequence
Apple nativeXcode build/tests when available, simulator/device diagnostics, accessibility inspection, Dynamic Type, target form factors
Android nativeGradle build/tests when available, Logcat errors/fatals, semantics/TalkBack checks, font scale, target window sizes

Compact cross-phase pass

Consider every phase, but do not manufacture a finding for each one.

Intent

  • State what the interface helps the user accomplish.
  • Mark duplicate actions, unexplained chrome, and generated residue for removal.
  • Preserve elements whose purpose is clear and useful.

Platform

  • Prefer native elements and state contracts over div plus ARIA reconstructions.
  • Check navigation/focus order, names, roles, states, disabled behaviour, and form participation.
  • Prefer native CSS/HTML where it can express the behaviour without weakening it.

Feel

  • Check input discoverability across keyboard, touch, and fine pointers.
  • Motion should be responsive, interruptible where needed, and have a reduced-motion path.
  • Delight should improve anticipation, reliability, or care—not add novelty without purpose.

Bridge

  • Check that design structure maps deliberately to code, tokens, primitives, rendering, and accessibility.
  • Flag one-off values or abstractions that fight the existing system.

Shipping

  • Exercise relevant loading, empty, error, auth, validation, focus, metadata, and repeat-use paths.
  • Separate prototype shortcuts from confirmed release risks.
  • Look for tests around critical contracts rather than requiring blanket coverage.

Progressive sibling routing

Load a sibling SKILL.md only when the materials map, initial evidence, or requested depth triggers it. Collection siblings are installed adjacent to this skill, so first resolve ../<skill-name>/SKILL.md before treating a lens as unavailable. If a sibling exists and is readable, use its content even when this session’s discovery registry omits it. A registry refresh is needed only to expose it as a separately invocable skill, not to continue the current pass. If neither discovery nor the adjacent path provides a readable skill, continue with the shared rubric, disclose the missing lens, and avoid inventing its detailed guidance.

TriggerRead
Product intent is unclear or decisions need rationalewrite-first-design
Decorative, redundant, or cognitively heavy UIsubtractive-design
A Danish functionalist design lens is requestedhumane-functionalism
Web forms, menus, dialogs, custom controls, or semantic failuressemantic-html-first
Web responsive layout or CSS/HTML implementation questionsmodern-css-html
SwiftUI, UIKit, AppKit, Xcode, or Apple-platform UIapple-native-ui
Kotlin, Android Gradle, Jetpack Compose, Android Views, or Compose Multiplatformandroid-native-ui
Gestures, springs, interruption, or shared-element motionfluid-design
Weak discoverability, hover-only actions, or input mismatchinterface-affordances
A specific opportunity for useful polishproduct-delight
Design/code structure or token translation is breaking downdesign-engineering
Consequential interaction behaviour is unspecified or inconsistentinteraction-contracts
A shared component or token model needs a compatibility or migration decisiondesign-system-evolution
Recurring setup, waiting, or interruption makes repeated work costlydesign-for-repetition
Placeholder or uniform content obscures hierarchy or layout decisionsreal-content-design
Template-like or AI-generated residue needs judgementai-output-judgement
Auth, validation, state, resilience, or release concernsprototype-to-production

dxe exhaustive is the only mode that requires reading every sibling skill in the routing table.

Severity and confidence

  • Critical: blocks a primary task, risks data/security loss, causes a confirmed serious accessibility failure, or breaks production rendering.
  • Should fix: confirmed damage to comprehension, navigation, semantics, resilience, or trust with a bounded correction.
  • Nice to have: polish, consistency, dormant primitive risk, or non-blocking cleanup.

Every finding must include one confidence tag. Never classify unverified or environment-only observations as Critical. Quote only the smallest useful source snippet and anchor findings to a path or rendered surface.

Report

markdown
# dxe pass — [scope]

## Hypothesis
## Evidence collected
## Verified strengths
- [skill or core] path/surface — what should be preserved

## Findings
### Critical
- [confidence] [skill or core] path/surface — issue — bounded fix
### Should fix
### Nice to have

## Cuts
## Proposed changes
## Verification status
## Limits and unresolved observations

Verified strengths are evidence, not consolation: record systems that work and should survive later changes. An empty severity section is acceptable. Findings should never be failure quotas.

Verification recipe

When tools and repository scripts permit:

  1. Run the existing typecheck, tests, and production build.
  2. Render the primary target and one contrasting viewport, window, device, or form factor.
  3. Check clipping/overflow, safe areas/insets, and primary task hierarchy as the platform requires.
  4. Record the first meaningful keyboard, touch, pointer, remote, or assistive-technology sequence.
  5. Exercise one menu, dialog, sheet, or navigation transition; wait for settled state, then verify state and focus/return.
  6. Repeat one motion-bearing task with reduced motion; wait for settlement before recording animation state or counts.
  7. Capture the platform's runtime errors: browser console.error/pageerror, Apple simulator/device diagnostics, or Android Logcat errors/fatals. Classify them separately from tooling noise.
  8. In fix mode, repeat affected checks and report exact results.

Never mutate production or external systems as part of a review.

Done when

  • Scope, hypothesis, non-goals, sampling, and limits are explicit.
  • Every phase was considered; only triggered sibling skills were loaded.
  • Claims are tied to evidence, severity, confidence, and a path or surface.
  • Confirmed product defects are separated from tooling noise and suspicions.
  • Existing strengths and subtractive cuts are recorded when warranted.
  • Fixes, when authorized, are bounded and verified in proportion to risk.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dxe AI skill do?

Review a repository or UI surface for design engineering quality. Use for a DXE pass or broad interface review; fix only when requested and load specialist guidance from evidence.

Why use Dxe on TypingMind?

Because you install it once and use it with any model. Dxe is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dxe in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kemiljk/skills/tree/main/skills/dxe. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dxe?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dxe?

As many as you like. As long as a model supports skills, you can use Dxe with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dxe AI skill free?

Yes. It is published on GitHub by kemiljk under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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