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Performance · Version 1.2.0 · Reviewed 2026-08-02

JVM GC Analyzer

Locate and remove the dominant bottleneck in GC log analysis and heap sizing with evidence, explicit trade-offs, and a verification plan.

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

Interprets JVM garbage-collection logs, allocation pressure, pause causes, heap sizing, promotion failures, and collector-specific behavior.

₹99 one-time

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What this skill helps you do

  • GC log analysis
  • Heap sizing
  • Pause diagnosis

How JVM GC Analyzer works

You provide

Baseline measurements, workload shape, and the target

It inspects

Dominant cost mechanism behind GC log analysis

It decides

A heap sizing change ranked by impact and risk

You verify

Re-measure under representative load with guardrails

What it checks first

JVM GC Analyzer interprets JVM garbage-collection logs, allocation pressure, pause causes, heap sizing, promotion failures, and collector-specific behavior. Use it when the work involves GC log analysis, Heap sizing, Pause diagnosis.

  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. Establish the current state and the constraint that actually limits GC log analysis.
  2. Separate the requested solution from the underlying problem in heap sizing, and name the assumptions carrying the most risk.
  3. Compare only viable options for pause diagnosis against weighted constraints, cost of reversal, and operational ownership.
  4. Commit to a sequenced recommendation with success criteria, guardrails, and the observation that would reverse it.

Deliverables

  • GC log analysis assessment
  • Heap sizing decision and action plan
  • Pause diagnosis 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 Java service has ten-second pauses twice per hour even though average heap use looks stable.

Expected output

The log shows humongous allocations fragmenting G1 regions until a full compaction occurs. Average occupancy hides the region-layout problem; reduce the large temporary buffers or adjust region sizing...

Boundaries and compatibility

Ideal for

  • GC log analysis: produce a decision or artifact grounded in supplied evidence.
  • Heap sizing: produce a decision or artifact grounded in supplied evidence.
  • Pause diagnosis: 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.