Design a realistic LLM load test — pick tool (LLMPerf, k6, GenAI-Perf, guidellm), build four patterns (steady, ramp, spike, soak), and gate in CI. Use when you need help with load test plan.
Scanned 9/8/2026
Install to Claude Code
npx -y skills add anubhavg-icpl/vibe --skill load-test-plan --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Load Test Plan?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/anubhavg-icpl-load-test-plan)More formats (shields.io, HTML) on the badges page.
---
name: load-test-plan
description: Design a realistic LLM load test — pick tool (LLMPerf, k6, GenAI-Perf, guidellm), build four patterns (steady, ramp, spike, soak), and gate in CI. Use when you need help with load test plan.
license: CC-BY-NC-SA-4.0
phase: 17
lesson: 22
metadata:
version: 1.0.0
tags: [load-testing, llmperf, k6, genai-perf, guidellm, llm-locust, ci-gate]
---
Given workload (endpoint, SLA for TTFT/TPOT/error), target scale (concurrency, RPS), and CI posture (PR gate or release-only), produce a load test plan.
Produce:
1. Tool. LLMPerf for baseline runs; k6 + streaming extension for CI gates; GenAI-Perf for NVIDIA-reference runs; guidellm for large synthetic. LLM-Locust only if already on Locust.
2. Prompt distribution. Mean + stddev input tokens from real traffic (if available) or published distribution (ShareGPT / HumanEval). Forbid loop-with-one-prompt.
3. Four patterns. Steady, ramp, spike, soak. For each: target RPS, duration, expected failure mode.
4. CI gate. Specific thresholds: TTFT P95 < X, 5xx < 5%, TPOT < Y. Runtime per PR: 3-5 min.
5. Metric alignment. Note whether the reporting tool is GenAI-Perf-style (ITL excludes TTFT) or LLMPerf-style (ITL includes TTFT). Pick one and stay consistent.
6. Output. A script file (k6 JS, LLMPerf CLI) committed to the repo.
Hard rejects:
- Load test with uniform prompts. Refuse — the numbers lie.
- Load test without streaming support. Refuse — LLM endpoints are streaming by default.
- Comparing numbers across tools without acknowledging metric-definition differences. Refuse.
Refusal rules:
- If the team intends to run on Locust stock without LLM-Locust extension, refuse — GIL trap.
- If CI gate budget is < 60s per PR, refuse full soak — propose a quick steady-state plus separate nightly soak.
- If prompt distribution data is unavailable, require a documented published distribution (ShareGPT) and note the assumption.
Output: a one-page plan with tool, prompt distribution, four patterns with targets, CI gate thresholds, metric alignment. End with the single CI output: PR green only if all thresholds met, 3-run stability.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!