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Performance · Version 1.5.0 · Reviewed 2026-08-02

Mobile App Size Optimizer

Locate and remove the dominant bottleneck in size attribution and asset optimization with evidence, explicit trade-offs, and a verification plan.

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

Reduces install size through asset auditing, code stripping, and delivery-time splitting.

₹149 one-time

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What this skill helps you do

  • Size attribution
  • Asset optimization
  • Dynamic delivery

How Mobile App Size Optimizer works

You provide

Baseline measurements, workload shape, and the target

It inspects

Dominant cost mechanism behind size attribution

It decides

A asset optimization change ranked by impact and risk

You verify

Re-measure under representative load with guardrails

What it checks first

Mobile App Size Optimizer reduces install size through asset auditing, code stripping, and delivery-time splitting. Use it when the work involves Size attribution, Asset optimization, Dynamic delivery.

  1. A measured baseline and the user-visible target, since optimization without both is guesswork.
  2. Whether the cost is CPU, memory, I/O wait, or lock contention — they have opposite fixes.
  3. The p99 path and how many round trips it contains.
  4. Whether the bottleneck moves after a change, which determines if the gain is real.

Failure modes it recognizes

  • Optimizing a component that is not on the critical path, producing no end-to-end change.
  • A garbage-collection pause misread as slow application code.
  • Memory pressure causing swapping, which presents as unpredictable latency spikes.
  • A micro-optimization that improves the benchmark and regresses the real workload.

Answers it will reject

  • Tuning configuration flags before profiling where time is actually spent.
  • Measuring in a warmed-up loop that does not resemble production access patterns.
  • Reporting an improvement without the guardrail metric that would show a shifted bottleneck.

Decision rules it applies

  • Profile before changing anything, and attribute cost to a specific phase.
  • Optimize the dominant cost first; everything else is rounding.
  • Re-measure under representative load and keep a guardrail metric.

Evidence it asks for

  • Capture a profile during the real workload rather than a synthetic benchmark.
  • Record allocation rate and pause time alongside latency.
  • Compare before and after at the same percentile, not at the mean.

The method inside

  1. Define the measured baseline and user-visible target for size attribution.
  2. Attribute the dominant cost or latency mechanism affecting asset optimization.
  3. Rank dynamic delivery changes by expected impact, confidence, effort, and regression risk.
  4. Validate under representative load and retain guardrail metrics that detect a shifted bottleneck.

Deliverables

  • Size attribution assessment
  • Asset optimization decision and action plan
  • Dynamic delivery verification checklist

Evidence requirements

  • Profiles, traces, timings, resource metrics, and workload shape
  • Baseline and target percentile
  • Environment, concurrency, and payload details

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

Our app is 180MB and install conversion is poor on emerging-market devices and networks.

Expected output

Attribute the size before cutting: usually a small number of uncompressed assets and unused localizations dominate. Move rarely used features to on-demand delivery so the initial download contains only the first-session path...

Boundaries and compatibility

Ideal for

  • Size attribution: produce a decision or artifact grounded in supplied evidence.
  • Asset optimization: produce a decision or artifact grounded in supplied evidence.
  • Dynamic delivery: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Optimizing without a baseline
  • Using averages where tail latency determines experience

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.