performance-test-gatling (EN)

SkillDev tools

Use this skill when you need Gatling performance scope, simulations, or runnable entry points; triggers include Gatling, Gatling simulations, and Gatling performance testing.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the performance-test-gatling (EN) skill

What this skill tells your AI

The instructions your AI receives, as published by naodeng/awesome-qa-skills in skills/en/testing-types/performance-test-gatling/SKILL.md and read by ahel’s review.

Chinese version: See the corresponding Chinese skill.

When to Use

  • Need performance outputs that should land in Gatling structure.
  • The project already uses Gatling or wants Gatling-ready scenarios.

Workflow

  1. Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
  2. Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
  3. If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
  4. Default to Markdown; switch formats only when the user asks.

Core Constraints

  • Prioritize by risk / business impact — do not treat everything equally.
  • Separate confirmed facts from current assumptions; with no SLA/traffic, label every number as Assumption and list Open Questions.
  • Do not invent endpoints, fields, environments, or root causes the user did not provide; secrets are placeholders / env vars only — never real tokens.
  • Keep output executable: concrete scenarios, clear priority, clear next steps.
  • Default to only the most critical 1–2 scenario types — do not run baseline/load/stress/spike/soak all by default.

Progressive Disclosure

  • Before producing output, read and follow prompts/performance-test-gatling.md (minimum coverage, output structure, quality bar).
  • When a ready-made template fits: use matching files under output-templates/.
  • When the user wants examples or alignment with existing assets: read relevant examples/.
  • For deep framework/troubleshoot/schema notes: read only the relevant file(s) under references/, do not load the whole directory.
  • For format conversion or helper checks: prefer existing scripts/ over reinventing.
  • For evaluating/regressing this skill: use evals/ with skill-up.

Pre-delivery Checklist

  • Followed the main prompt's output structure
  • Minimum coverage focus: target scenarios, load model, test data or feeder needs, ramp profile, thresholds, environment and monitoring, priority bottlenecks, reporting needs, ... (details in main prompt)
  • Covered the minimum checklist, or explained omissions
  • High-risk items have explicit priority
  • Did not invent details the user did not provide
  • Assumptions and gaps are marked

Common Pitfalls

  • Do not pretend completeness when scope/context is missing.
  • Do not treat every item as equally important.
  • Do not skip assumptions and information gaps.
  • Do not dump generic theory unrelated to the current toolchain.

Signals

GitHub stars
210
Forks
29
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
performance-test-gatling
Source
github.com/naodeng/awesome-qa-skills