Execute comprehensive load and stress testing to validate API performance and scalability.
SkillScore70Maintained
Automatic
◆QA & Test Engineering packapitestingperformance
Last verified 8/31/2026
What it changes
Passed 14 of 20 test cases with the skill, and 9 of 20 without it.
Without skill43%
With skill70%
Checks it passed that the model alone missed
Saves scenario test scripts under load-tests/scenarios/.
Assigns higher request probability weight to read operations than write operations.
Executes baseline testing before scaling progressively to 2x, 5x, and 10x target loads.
and 5 more in the full report
6 fixed10 passed either way7 still failing0 broke
Pass-rate lift
+26 pp
Tokens
+61%
Agent turns
0%
Response time
+12%
Model gemini-3.6-flashMeasured 8/31/2026v116 of 23 cases passed with skill · 20 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 6
Prompt
We want to run load scaling tests against our service. We plan to start at baseline production load and step up linearly to 1.5x and 2x. What progression multipliers should be used to discover system breaking points and saturation?
What was checked, without and with the skill
Checked
Without skill
With skill
Executes baseline testing before scaling progressively to 2x, 5x, and 10x target loads.
Missed
Passed
Identifies breaking points and system saturation behavior under stress scaling.
Passed
Passed
case-12 · gemini-3.6-flash · 8/31/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md65 lines4 files
Load Testing APIs
Overview
Execute comprehensive load, stress, and soak tests to validate API performance, identify bottlenecks, and establish throughput baselines. Generate test scripts for k6, Artillery, or wrk that simulate realistic traffic patterns with configurable virtual user ramp-up, request distribution, and failure threshold assertions.
Target API deployed in a staging/performance environment (never load test production without safeguards)
Monitoring stack accessible: Grafana/Prometheus, Datadog, or CloudWatch for correlating test results with server metrics
API authentication credentials for testing (API keys, test user JWT tokens)
Baseline performance SLOs defined (target p95 latency, max error rate, minimum throughput)
Instructions
Read the API specification and route definitions using Glob and Read to build a complete list of endpoints, identifying high-traffic paths and resource-intensive operations.
Define test scenarios modeling realistic user behavior: browsing (80% reads), checkout (mixed reads + writes), and spike traffic patterns with appropriate think times between requests.
Generate k6 or Artillery test scripts with configurable stages: ramp-up (2 min), sustained load (10 min), spike (2 min at 3x), and cool-down (2 min).
Configure request distribution to match production traffic patterns -- weighted random selection across endpoints rather than uniform distribution.
Add threshold assertions for pass/fail criteria: p95 response time < 500ms, error rate < 1%, throughput > 100 requests/second.
Implement data-driven requests using CSV or JSON fixtures for realistic payloads, unique user IDs, and varied query parameters to avoid cache-only testing.
Execute baseline test at expected production load, then gradually increase to 2x, 5x, and 10x to identify the breaking point and saturation behavior.
Analyze results: correlate latency spikes with server metrics (CPU, memory, DB connections, event loop lag), identify the bottleneck (database, network, compute), and document findings.
Generate a performance report comparing results against SLO thresholds with recommendations for optimization.
See ${CLAUDE_SKILL_DIR}/references/implementation.md for the full implementation guide.
Output
${CLAUDE_SKILL_DIR}/load-tests/scenarios/ - k6/Artillery test scripts per traffic scenario
${CLAUDE_SKILL_DIR}/load-tests/data/ - Test data fixtures (users, payloads, tokens)