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KiloClaw

Performance Regression Guardrails

Catch perf regressions before users do

Introduce performance budgets and regression detection using benchmarks and/or production telemetry so regressions become reviewable events, not customer complaints.

CommunitySubmitted by CommunityWork18 min

INGREDIENTS

🐙GitHub

PROMPT

Create a skill called "Performance Regression Guardrails". Ask me for: - The top user journeys / hot paths - Current observability/benchmark tooling Output: - A minimal benchmark plan (scenarios + metrics) - Budget thresholds and alerting approach - CI integration strategy (PR vs nightly) - A PR checklist for performance-sensitive changes

How It Works

Performance regressions are common after upgrades and refactors. This recipe sets up

a small, stable performance suite that monitors key metrics over time.

Triggers

  • Users report "it's slower" after a release
  • Upgrades introduce noticeable regressions
  • Teams lack a baseline for performance expectations

Steps

  1. Choose 3–10 critical performance scenarios (hot paths).
  2. Define metrics and budgets (latency, CPU, memory, p95 where applicable).
  3. Add a benchmark lane:
  • nightly on fixed hardware when possible,
  • PR sampling for sensitive areas.
  1. Alert on statistically significant regressions (avoid noise).
  2. Require a "perf note" in PRs that touch hot paths.

Expected Outcome

  • Performance regressions are detected early and tied to specific changes.
  • Teams build a culture of measuring, not guessing.

Example Inputs

  • "Prevent regression in rendering/queries after upgrades."
  • "Add CI benchmarks for a core API endpoint."
  • "We need performance budgets for a mobile app."

Tips

  • Performance tests must be stable; treat "perf flakes" as seriously as test flakes.
Tags:#performance#testing#ci-cd#observability