Load Test Designer
Design load tests with realistic workload models and performance criteria
Best use case
Load Test Designer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Design load tests with realistic workload models and performance criteria
Teams using Load Test Designer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/load-test-designer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Load Test Designer Compares
| Feature / Agent | Load Test Designer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Design load tests with realistic workload models and performance criteria
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Load Test Designer Skill Design load tests with realistic workload models and performance criteria. ## Trigger Conditions - Pre-release performance gate - Performance baseline request - User invokes with "design load test" or "performance test" ## Input Contract - **Required:** System under test and target endpoints - **Required:** Performance targets (latency SLOs, throughput) - **Optional:** Production traffic profile, expected growth ## Output Contract - Load test configuration with workload model - Performance criteria (pass/fail thresholds) - Results analysis with regression detection - Capacity recommendations ## Tool Permissions - **Read:** Production access logs, performance baselines, infra configs - **Write:** Load test configs, results reports - **Execute:** Load test tools (k6, locust, artillery) ## Execution Steps 1. Build workload model from production traffic patterns 2. Define performance criteria (p50, p95, p99 latency targets) 3. Configure load test scenarios (ramp-up, sustained, spike) 4. Execute tests in staging environment 5. Collect and analyze results 6. Compare against baseline; flag regressions >5% p99 7. Generate report with capacity recommendations ## Success Criteria - Workload model reflects production patterns - All SLO targets met during sustained load - No regression >5% on p99 latency - Saturation point identified ## Escalation Rules - Escalate if p99 regression >20% on critical path - Escalate if saturation point is below expected peak traffic - Escalate if soak test reveals memory leak or connection exhaustion ## Example Invocations **Input:** "Design a load test for the /api/v1/search endpoint targeting 1000 RPS" **Output:** Load test config (k6): ramp 0→1000 RPS over 5min, sustain 1000 RPS for 15min, spike to 2000 RPS for 2min. Pass criteria: p50 <100ms, p95 <300ms, p99 <800ms, error rate <0.1%. Saturation test: ramp until p99 >1s. Soak: 500 RPS for 8 hours monitoring heap and FD count.
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