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Performance · Version 1.2.0 · Reviewed 2026-08-02

Migration Performance Gate Designer

Locate and remove the dominant bottleneck in migration baseline capture and performance budget design with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Defines before-and-after latency, allocation, throughput, and cost budgets that a migration must satisfy. It grounds the decision in representative workloads, pre-change benchmarks, hot paths, variance, resource use, and agreed error budgets and explicitly prevents comparing non-equivalent environments or averages and declaring no regression while tail latency or allocation rises.

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What this skill helps you do

  • Migration baseline capture
  • Performance budget design
  • Before-after regression gate

How Migration Performance Gate Designer works

You provide

Current state, consumer inventory, and target state

It inspects

Coexistence and rollback viability for migration baseline capture

It decides

A performance budget design sequence in reversible increments

You verify

Shadow comparison reports divergence rather than assuming zero

What it checks first

Migration Performance Gate Designer defines before-and-after latency, allocation, throughput, and cost budgets that a migration must satisfy. It grounds the decision in representative workloads, pre-change benchmarks, hot paths, variance, resource use, and agreed error budgets and explicitly prevents comparing non-equivalent environments or averages and declaring no regression while tail latency or allocation rises. Use it when the work involves Migration baseline capture, Performance budget design, Before-after regression gate.

  1. Whether the old and new paths can coexist, which determines if incremental migration is possible at all.
  2. The true consumer inventory, including internal jobs, scripts, and integrations not visible in the main codebase.
  3. Data volume and the time the migration takes at production scale, not sample scale.
  4. Whether the change is backward compatible for data written by the previous version during rollout.
  5. The rollback path, and specifically whether it remains available after the first irreversible step.

Failure modes it recognizes

  • A migration validated on a sample that takes hours on production volume and holds a lock throughout.
  • Dual-write divergence where one write succeeds and the other fails, with no reconciliation.
  • A backfill that races with live writes and overwrites newer values with older ones.
  • Removing the old path before all consumers migrated, discovered by a quarterly batch job weeks later.
  • A schema change that is forward compatible but not backward compatible, blocking rollback.
  • Enum or type widening that older readers cannot parse, breaking during a partial rollout.

Answers it will reject

  • A big-bang cutover with a maintenance window, which concentrates all risk into one unrehearsed moment.
  • Migrating and refactoring simultaneously, which makes failures impossible to attribute.
  • Treating the migration as done at cutover rather than after the old path is removed and verified unused.
  • Skipping the shadow-read comparison because the new implementation "obviously" matches.

Decision rules it applies

  • Use expand-migrate-contract: add the new shape, write both, migrate readers, then remove the old shape.
  • Every increment must be independently verifiable and independently revertible.
  • Keep the old path observable until traffic proves equivalence; remove it only on evidence of zero use.
  • Prefer additive schema changes; a removal is a separate, later, deliberately scheduled change.

Evidence it asks for

  • Run shadow reads comparing old and new outputs, and report the divergence rate rather than assuming zero.
  • Instrument usage of the deprecated path with a caller identifier so removal can be proven safe.
  • Rehearse the migration on a production-sized copy and record the actual duration and lock behavior.

The method inside

  1. Inventory dependencies, compatibility constraints, and current behavior affecting migration baseline capture.
  2. Create reversible seams for performance budget design before changing the critical path.
  3. Sequence before-after regression gate into independently verifiable increments with explicit rollback points.
  4. Keep old and new paths observable until equivalence is proven; remove the fallback only after acceptance criteria pass.

Deliverables

  • Migration baseline capture assessment
  • Performance budget design decision and action plan
  • Before-after regression gate 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

Apply the migration performance gate designer to our current migration baseline capture work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with representative workloads, pre-change benchmarks, hot paths, variance, resource use, and agreed error budgets. The highest-risk failure is comparing non-equivalent environments or averages and declaring no regression while tail latency or allocation rises. Freeze the benchmark contract before implementation and assess confidence intervals plus user-visible percentiles. Verify the result by rerunning both revisions under the same workload and reviewing raw samples, environment metadata, and statistical power.

Boundaries and compatibility

Ideal for

  • Migration baseline capture: produce a decision or artifact grounded in supplied evidence.
  • Performance budget design: produce a decision or artifact grounded in supplied evidence.
  • Before-after regression gate: 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 Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

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.