api-load-tester
Load tests API endpoints with progressive concurrency.
Skill by OneWave-AI
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/onewave-ai-api-load-tester
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: documents, backend
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/api-load-tester
What's inside
- Skill name
api-load-tester- Description
- 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.
- License
- MIT
- Instructions
- 42 lines in SKILL.md · view on GitHub
Before installing
- Instructions call the network
Files (5)
- SKILL.md
- metrics-interpretation.md · references/
- output-template.md · references/
- rules-and-examples.md · references/
- tool-commands.md · references/
SKILL.md (first lines)
# 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.
From OneWave-AI/claude-skills, MIT.
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Signals
- Stars
- 373
- Forks
- 60
- Open issues
- 1
- Last commit (this folder)
- Jun 8, 2026 (4 months ago)
- Last release
- Jun 8, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/OneWave-AI/claude-skills
- Website
- www.onewave-ai.com
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
api-load-tester skill. Agent Mart. https://agentmart.dev/skills/onewave-ai-api-load-tester (last updated 2026-10-24)