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Architecture · Version 1.3.0 · Reviewed 2026-08-02

Micro-Frontend Decision Advisor

Make a defensible decision about fit assessment and boundary design with evidence, explicit trade-offs, and a verification plan.

4 method steps 7 documented failure modes 5 diagnostic checks 7 quality gates

Assesses whether independent frontend deployment justifies its runtime and consistency cost.

₹99 one-time

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What this skill helps you do

  • Fit assessment
  • Boundary design
  • Consistency cost

How Micro-Frontend Decision Advisor works

You provide

Component code, field metrics, and the failing interaction

It inspects

Render triggers and layout stability for fit assessment

It decides

A boundary design fix targeting the measured vital

You verify

Field Core Web Vitals and keyboard traversal re-checked

What it checks first

Micro-Frontend Decision Advisor assesses whether independent frontend deployment justifies its runtime and consistency cost. Use it when the work involves Fit assessment, Boundary design, Consistency cost.

  1. Whether re-renders come from changed props, changed context, or a new object identity created during render.
  2. Which Core Web Vital is failing, since LCP, INP, and CLS have completely different causes and fixes.
  3. Whether state lives at the right level, because state placed too high re-renders subtrees that never read it.
  4. Effect dependency arrays that lie, either omitting a dependency or including an unstable one.
  5. Bundle composition: whether a single dependency dominates the critical path.

Failure modes it recognizes

  • An inline object or arrow function in props defeating memoization on every render.
  • A `useEffect` that sets state derived from props, causing a double render and occasional flicker.
  • Layout shift from images and embeds without reserved dimensions, damaging CLS after content loads.
  • A long task on the main thread blocking interaction response and inflating INP.
  • Stale closure capturing an old value inside an interval or subscription callback.
  • Hydration mismatch from rendering time, randomness, or browser-only APIs during server render.
  • Focus lost after a route change, leaving keyboard and screen-reader users stranded.

Answers it will reject

  • Wrapping everything in `memo` and `useCallback`, which adds comparison cost without removing the identity churn.
  • Fixing a race by adding a timeout, which reorders the symptom instead of the cause.
  • Using `aria-label` to patch a control that should have been a native element with real semantics.
  • Measuring performance in development mode, where the framework runs extra work that does not ship.

Decision rules it applies

  • Move state down or split context before reaching for memoization.
  • Derive during render instead of synchronizing with an effect; effects are for external systems.
  • Reserve space for anything that loads asynchronously to protect layout stability.
  • Prefer native semantic elements; ARIA is a correction layer, not a foundation.

Evidence it asks for

  • Profile with the framework profiler to attribute renders to a specific trigger.
  • Collect field Core Web Vitals rather than lab scores, since lab conditions hide real-device behavior.
  • Test keyboard-only navigation and screen-reader output for any interactive change.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to fit assessment.
  2. Trace concrete failure or abuse paths for boundary design; do not report checklist items without a mechanism.
  3. Prioritize consistency cost findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Fit assessment assessment
  • Boundary design decision and action plan
  • Consistency cost verification checklist

Evidence requirements

  • Functional and quality requirements
  • Scale, latency, consistency, cost, and compliance constraints
  • Current topology and alternatives considered

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

We have four teams working in one frontend repo and merge conflicts are constant. Micro-frontends?

Expected output

Merge conflicts are a modularity problem inside the repo, and micro-frontends solve deployment independence at the cost of duplicated runtime and inconsistent UX. Fix module boundaries and ownership first; adopt micro-frontends only when release coupling is the actual pain...

Boundaries and compatibility

Ideal for

  • Fit assessment: produce a decision or artifact grounded in supplied evidence.
  • Boundary design: produce a decision or artifact grounded in supplied evidence.
  • Consistency cost: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Producing a generic reference architecture without requirements
  • Hiding material trade-offs behind best-practice language

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.