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ai-stress-test
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ai-stress-test — find where it breaks, not just whether it works
What it produces
A named breaking point with the load that caused it, the metric that failed first, and the safe capacity ceiling. Every claim backed by a number from a real run.
Safety
- Never run against production without explicit written approval and a defined abort threshold.
- Never generate unbounded concurrency or duration. Every run has a ceiling.
- Refuse requests that look like denial-of-service: "flood this", "hammer it until it dies", "DDoS it" get a hard no, followed by a controlled alternative.
- Default to
plan-onlymode when the target environment is unclear. Ask before executing. - Stop immediately if error rate exceeds 5% during a run, or if the target system reports health-check failures.
Steps
1. Collect inputs
Gather or ask for:
- target: base URL, host, or service under test.
- environment:
local,staging, orproduction. Ifproduction, require explicit approval and abort thresholds before doing anything. - endpoints: which flows to test. If unknown, ask for the top 5 most critical user journeys (from access logs, SLOs, or product judgment).
- baseline load: normal traffic level (requests/second or concurrent users).
- expected peak: what the system should handle without degradation.
- thresholds: acceptable latency (p95, p99), max error rate, minimum throughput. If not provided, use conservative defaults: p95 < 500ms, error rate < 1%, throughput matching baseline.
- auth: how to authenticate requests (API key, bearer, session, none).
2. Select the tool
Use the installed tool. Do not install anything without asking.
| Tool | When | Command shape |
|---|---|---|
| k6 | Default for HTTP APIs, CI-friendly, threshold-native | k6 run script.js |
| JMeter | Existing .jmx plans, team already uses it | jmeter -n -t plan.jmx |
| Locust | Python shops, complex user behavior modeling | locust -f locustfile.py --headless |
If multiple tools are installed, prefer k6. If the user has existing test scripts
(JMeter .jmx, Locust locustfile.py), use what they have.
3. Design scenarios
Design from lightest to heaviest. Each scenario is a separate run.
a. Baseline (always first) Short run at expected normal load. Confirms connectivity, auth works, and metrics collect correctly. Abort if this fails.
b. Stress test Ramp from normal load to beyond expected peak in stages. Goal: find the knee where latency degrades or errors appear.
Typical shape:
Normal load 2m 50 VUs
Ramp up 3m 200 VUs
Peak hold 5m 200 VUs
Push beyond 3m 500 VUs
Ramp down 1m 0 VUs
c. Spike test Sudden burst from zero (or baseline) to extreme load. Goal: test recovery behavior.
Typical shape:
Baseline 1m 50 VUs
Spike 10s 1000 VUs
Spike hold 2m 1000 VUs
Recovery 3m 50 VUs
d. Breakpoint (optional, the nuclear option) Continuously increase load until the system fails. Goal: find the absolute ceiling.
Use k6 ramping-arrival-rate with a high target and abortOnFail threshold. Stop
manually or let thresholds kill the run.
4. Define thresholds
Every scenario except baseline must have abort thresholds:
http_req_duration: ['p(95)<500']
http_req_failed: ['rate<0.01']
For breakpoint tests, thresholds ARE the measurement: the point they trigger IS the breaking point.
5. Execute
- Run baseline first. Confirm it passes.
- Run stress. Record results.
- Run spike. Record results.
- Optionally run breakpoint. Record the load level where thresholds breach.
Each run: save raw output (JSON or JTL), save summary metrics, note the environment state (CPU, memory if visible).
6. Analyze
For each scenario, extract:
- p50, p90, p95, p99 latency (never averages alone)
- Throughput (requests/second at peak)
- Error rate (percentage and types: timeouts, 5xx, connection resets)
- Breaking point: the load level where thresholds were breached
- Safe capacity: 70-80% of the breaking point (leave headroom)
- Bottleneck signal: which metric degraded first (latency, errors, throughput)
7. Report
Produce a concise report with:
- What was tested (endpoints, environment, tool).
- Results per scenario (table of metrics vs thresholds).
- The breaking point with evidence (the exact load level and the metric that failed).
- Safe capacity recommendation.
- Bottleneck hypothesis (what to investigate next).
- What was NOT tested (blind spots).
Anti-patterns
- Running stress tests without baseline first. If baseline is broken, every other number is meaningless.
- Using averages instead of percentiles. Average latency hides the long tail.
- Testing with tiny data sets. Cached or warm-cache results are fiction.
- Claiming a bottleneck without metric evidence. "Probably the database" is not analysis.
- Running from the same machine as the target. The load generator becomes the bottleneck.
- Ignoring the ramp. Jumping straight to peak load skips the knee, which is the most useful data point.
- Testing production without abort thresholds. One runaway test can take down the system.
Done when
- A breaking point is named with the load level and metric that failed first.
- Safe capacity is stated as a number, not a feeling.
- Every claim is backed by output from a real run.
- The user knows what to investigate next.
What this is not
- "Run k6 and tell me if it's fast" — stress testing finds limits, not general performance opinions.
- "DDoS my production" — hard no, always.
- Load testing for expected traffic — that is /ai-verify with existing benchmarks.
- Frontend performance or Core Web Vitals — that is /ai-design-audit.
- Diagnosing why something is slow — that is /ai-debug. This skill finds WHERE it breaks, not WHY.
Routing
In scope:
- "stress test this API", "find the breaking point", "how much traffic can we handle"
- "capacity test", "spike test", "what's our max load"
- "test system limits", "validate scaling assumptions"
Not for:
- Frontend performance, Core Web Vitals, page load metrics — use /ai-design-audit.
- Diagnosing a performance regression — use /ai-debug.
- General load testing to validate expected traffic works — use /ai-verify.
- Deciding what to build — use /ai-plan.
- DoS or unauthorized traffic generation — refused.
Lifecycle
Lane: standard Writes: performance-report.md Read by: ai-verify Dies: ai-eng spec close Next: ai-verify, ai-debug (if bottleneck found)
File metadata
name: ai-stress-test description: >- Test system behavior under extreme load to find breaking points, capacity limits, and failure modes. Covers stress, spike, and breakpoint testing with k6 (primary), JMeter, or Locust. Refuses unbounded concurrency and production saturation without explicit approval. Trigger for "stress test", "load test until it breaks", "find the breaking point", "capacity test", "spike test", "how much traffic can we handle", "system limits", "what's our max load". Not for frontend performance or Core Web Vitals — use /ai-design-audit. Not for general load testing to validate expected traffic — use /ai-verify with existing benchmarks. Not for diagnosing a performance failure — use /ai-debug. license: Apache-2.0
View original text
--- name: ai-stress-test description: >- Test system behavior under extreme load to find breaking points, capacity limits, and failure modes. Covers stress, spike, and breakpoint testing with k6 (primary), JMeter, or Locust. Refuses unbounded concurrency and production saturation without explicit approval. Trigger for "stress test", "load test until it breaks", "find the breaking point", "capacity test", "spike test", "how much traffic can we handle", "system limits", "what's our max load". Not for frontend performance or Core Web Vitals — use /ai-design-audit. Not for general load testing to validate expected traffic — use /ai-verify with existing benchmarks. Not for diagnosing a performance failure — use /ai-debug. license: Apache-2.0 --- # ai-stress-test — find where it breaks, not just whether it works ## What it produces A named breaking point with the load that caused it, the metric that failed first, and the safe capacity ceiling. Every claim backed by a number from a real run. ## Safety 1. Never run against production without explicit written approval and a defined abort threshold. 2. Never generate unbounded concurrency or duration. Every run has a ceiling. 3. Refuse requests that look like denial-of-service: "flood this", "hammer it until it dies", "DDoS it" get a hard no, followed by a controlled alternative. 4. Default to `plan-only` mode when the target environment is unclear. Ask before executing. 5. Stop immediately if error rate exceeds 5% during a run, or if the target system reports health-check failures. ## Steps ### 1. Collect inputs Gather or ask for: - **target**: base URL, host, or service under test. - **environment**: `local`, `staging`, or `production`. If `production`, require explicit approval and abort thresholds before doing anything. - **endpoints**: which flows to test. If unknown, ask for the top 5 most critical user journeys (from access logs, SLOs, or product judgment). - **baseline load**: normal traffic level (requests/second or concurrent users). - **expected peak**: what the system should handle without degradation. - **thresholds**: acceptable latency (p95, p99), max error rate, minimum throughput. If not provided, use conservative defaults: p95 < 500ms, error rate < 1%, throughput matching baseline. - **auth**: how to authenticate requests (API key, bearer, session, none). ### 2. Select the tool Use the installed tool. Do not install anything without asking. | Tool | When | Command shape | |------|------|---------------| | **k6** | Default for HTTP APIs, CI-friendly, threshold-native | `k6 run script.js` | | **JMeter** | Existing `.jmx` plans, team already uses it | `jmeter -n -t plan.jmx` | | **Locust** | Python shops, complex user behavior modeling | `locust -f locustfile.py --headless` | If multiple tools are installed, prefer k6. If the user has existing test scripts (JMeter `.jmx`, Locust `locustfile.py`), use what they have. ### 3. Design scenarios Design from lightest to heaviest. Each scenario is a separate run. **a. Baseline** (always first) Short run at expected normal load. Confirms connectivity, auth works, and metrics collect correctly. Abort if this fails. **b. Stress test** Ramp from normal load to beyond expected peak in stages. Goal: find the knee where latency degrades or errors appear. Typical shape: ``` Normal load 2m 50 VUs Ramp up 3m 200 VUs Peak hold 5m 200 VUs Push beyond 3m 500 VUs Ramp down 1m 0 VUs ``` **c. Spike test** Sudden burst from zero (or baseline) to extreme load. Goal: test recovery behavior. Typical shape: ``` Baseline 1m 50 VUs Spike 10s 1000 VUs Spike hold 2m 1000 VUs Recovery 3m 50 VUs ``` **d. Breakpoint** (optional, the nuclear option) Continuously increase load until the system fails. Goal: find the absolute ceiling. Use k6 `ramping-arrival-rate` with a high target and `abortOnFail` threshold. Stop manually or let thresholds kill the run. ### 4. Define thresholds Every scenario except baseline must have abort thresholds: ``` http_req_duration: ['p(95)<500'] http_req_failed: ['rate<0.01'] ``` For breakpoint tests, thresholds ARE the measurement: the point they trigger IS the breaking point. ### 5. Execute 1. Run baseline first. Confirm it passes. 2. Run stress. Record results. 3. Run spike. Record results. 4. Optionally run breakpoint. Record the load level where thresholds breach. Each run: save raw output (JSON or JTL), save summary metrics, note the environment state (CPU, memory if visible). ### 6. Analyze For each scenario, extract: - **p50, p90, p95, p99 latency** (never averages alone) - **Throughput** (requests/second at peak) - **Error rate** (percentage and types: timeouts, 5xx, connection resets) - **Breaking point**: the load level where thresholds were breached - **Safe capacity**: 70-80% of the breaking point (leave headroom) - **Bottleneck signal**: which metric degraded first (latency, errors, throughput) ### 7. Report Produce a concise report with: 1. What was tested (endpoints, environment, tool). 2. Results per scenario (table of metrics vs thresholds). 3. The breaking point with evidence (the exact load level and the metric that failed). 4. Safe capacity recommendation. 5. Bottleneck hypothesis (what to investigate next). 6. What was NOT tested (blind spots). ## Anti-patterns - Running stress tests without baseline first. If baseline is broken, every other number is meaningless. - Using averages instead of percentiles. Average latency hides the long tail. - Testing with tiny data sets. Cached or warm-cache results are fiction. - Claiming a bottleneck without metric evidence. "Probably the database" is not analysis. - Running from the same machine as the target. The load generator becomes the bottleneck. - Ignoring the ramp. Jumping straight to peak load skips the knee, which is the most useful data point. - Testing production without abort thresholds. One runaway test can take down the system. ## Done when - A breaking point is named with the load level and metric that failed first. - Safe capacity is stated as a number, not a feeling. - Every claim is backed by output from a real run. - The user knows what to investigate next. ## What this is not - "Run k6 and tell me if it's fast" — stress testing finds limits, not general performance opinions. - "DDoS my production" — hard no, always. - Load testing for expected traffic — that is /ai-verify with existing benchmarks. - Frontend performance or Core Web Vitals — that is /ai-design-audit. - Diagnosing why something is slow — that is /ai-debug. This skill finds WHERE it breaks, not WHY. ## Routing In scope: - "stress test this API", "find the breaking point", "how much traffic can we handle" - "capacity test", "spike test", "what's our max load" - "test system limits", "validate scaling assumptions" Not for: - Frontend performance, Core Web Vitals, page load metrics — use /ai-design-audit. - Diagnosing a performance regression — use /ai-debug. - General load testing to validate expected traffic works — use /ai-verify. - Deciding what to build — use /ai-plan. - DoS or unauthorized traffic generation — refused. ## Lifecycle Lane: standard Writes: performance-report.md Read by: ai-verify Dies: ai-eng spec close Next: ai-verify, ai-debug (if bottleneck found)
Review the source
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- License
- Apache-2.0
- Price unconfirmed
- We have not confirmed a price for this skill. Existing source and install links remain available.
Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: Apache-2.0
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- AI review approval is missing
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 55 GitHub stars
- Stars/forks activity: 55 stars, 3 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: credential or environment access, network or browser surface
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedStatic Checked
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- arcasilesgroup/ai-engineering
- License
- Apache-2.0
- Version
- Unknown
- Last GitHub push
- Sep 16, 2026
- Registry updated
- Sep 16, 2026
- Instruction path
- skills/ai-stress-test/SKILL.md @ 83858b7c8746
Version reported in registry metadata; check source releases before relying on it.
Quality
59/100
Promising
Trust
60/100
Sandbox only
Audit
73/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- AI review approval is missing
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 55 GitHub stars
- Stars/forks activity: 55 stars, 3 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: credential or environment access, network or browser surface
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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"expected_agent_output": {
"selected_skill": "arcasilesgroup-ai-stress-test (ai-stress-test)",
"install_command": "npx skills add arcasilesgroup/ai-engineering --skill ai-stress-test",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "arcasilesgroup-ai-stress-test",
"task": "Use ai-stress-test in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/arcasilesgroup-ai-stress-test",
"api": "https://www.openagentskill.com/api/agent/skills/arcasilesgroup-ai-stress-test",
"audit": "https://www.openagentskill.com/skills/arcasilesgroup-ai-stress-test/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=arcasilesgroup-ai-stress-test&task=Use%20ai-stress-test%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-stress-test%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ai-stress-test%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/arcasilesgroup-ai-stress-test/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/arcasilesgroup-ai-stress-test"
}
}For the creator
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- Creator
- arcasilesgroup
- Indexed by
- OpenAgentSkill community index
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