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OneWave-AI

API Load Tester skill

Load tests API endpoints with progressive concurrency. Measures response times, error rates, throughput, and identifies breaking points. Generates a detailed report with latency percentiles, throughput curves, bottleneck analysis, and optimization recommendations.

$npx skills add OneWave-AI/claude-skills --skill api-load-tester
View this skill on GitHubOneWave-AI/claude-skills/api-load-tester/SKILL.md
Pack stars
423
Pack installs
72k
Alyn Score
58/99
Updated
22 days ago
License
MIT
– views – installs via Alyn

OneWave-AI

API Load Tester

Install API Load Tester

Pick your agent. Every command is ready to paste.

$npx skills add OneWave-AI/claude-skills --skill api-load-tester

The open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.

01

Install it

Run the command above, or upload the folder in Claude.

02

Ask naturally

Describe the task. Your agent loads api-load-tester when the request matches its description.

03

Name it if needed

If it does not trigger, say “use the api-load-tester skill”. In Codex, type $api-load-tester.

The skill itself

SKILL.md

FolderGitHub

API Load Tester

Stress-test HTTP endpoints under increasing load, identify breaking points, and produce a report with actionable recommendations.

Contents

  • references/tool-commands.md -- tool invocations (hey/wrk/ab/curl), default concurrency stages, per-stage data to capture.
  • references/metrics-interpretation.md -- latency, throughput, error, breaking-point, and bottleneck classification.
  • references/output-template.md -- exact structure for api-load-report.md, including ASCII charts and scaling table.
  • references/rules-and-examples.md -- safety rules, error handling, and example invocations.

Inputs

Collect from the user. Ask before proceeding if a required input is missing.

Required: endpoint URL(s) (with method, headers, body as needed); expected latency thresholds. Default thresholds if unspecified: p50 < 100ms, p95 < 300ms, p99 < 1000ms.

Optional: concurrent users or range (default ramp 1 to 100); authentication; request payloads; custom headers; test duration (default 10s per stage); ramp pattern (default step ramp, doubling each stage); success criteria (default 2xx); known rate limits; environment label (prod/staging/dev).

Workflow

Follow these steps in order.

  1. Select a tool. Check in priority order: which hey, which wrk, which ab, which curl. If none of hey/wrk/ab exist, install hey (brew install hey on macOS, go install github.com/rakyll/hey@latest on Linux with Go) or fall back to curl with bash background processes and wait. Verify with a single trivial request against a provided endpoint; diagnose connectivity or auth before continuing.

  2. Validate endpoints. Send one request per endpoint with the specified method, headers, auth, and body. Confirm the status matches the success criteria and record baseline single-request latency. On failure, surface the error and ask whether to skip or fix.

  3. Design the test plan. Build progressive concurrency stages (see references/tool-commands.md for the default progression), trimming or extending to the user's concurrency range. Define per-endpoint method, URL, headers, body, success codes, and timeout (default 30s). Print the plan for review before executing.

  4. Execute stages. For each endpoint, run every concurrency stage sequentially with the selected tool, waiting 2 seconds between stages. Capture and store the per-stage metrics. See references/tool-commands.md for commands, request-count formula, and the metrics list.

  5. Interpret metrics. Compute latency percentiles and profile, throughput curve and ceiling, error rates and onset, the breaking point, and the bottleneck classification. See references/metrics-interpretation.md.

  6. Generate the report. Write api-load-report.md to the current working directory following references/output-template.md exactly, including ASCII throughput and latency charts.

  7. Post-report actions. Print a 3-5 line summary to the console, state the report path, explicitly highlight any critical issues, and offer to re-run specific stages with different parameters.

Rules

Apply the safety rules, error handling, and example invocations in references/rules-and-examples.md. Key constraints: never load-test production without explicit confirmation, only test GET by default, mask auth tokens, respect 429 rate limits, count timeouts as failures, and never extrapolate beyond tested ranges.

Tips for API Load Tester

  • • Start a fresh session after installing so your agent picks up the new skill.
  • • It bundles reference files. The agent reads them only when needed, so they cost no context until then.
  • • It is part of a pack with 30 skills. Install only the ones you need with --skill to keep triggers precise.
  • • If two skills overlap, mention the one you want by name.

Questions about API Load Tester

What does the API Load Tester skill do?

Load tests API endpoints with progressive concurrency. Measures response times, error rates, throughput, and identifies breaking points. Generates a detailed report with latency percentiles, throughput curves, bottleneck analysis, and optimization recommendations.

How do I install API Load Tester?

Run "npx skills add OneWave-AI/claude-skills --skill api-load-tester" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.

Does API Load Tester work in Cursor and Codex?

Yes. API Load Tester uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.

Is API Load Tester free?

API Load Tester is open source on GitHub under the MIT license. It is free to install; check the license before commercial use.

Who made API Load Tester?

API Load Tester is published by OneWave-AI in the OneWave-AI/claude-skills repository, together with 29 other skills.

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