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ai-stress-test

Test system behavior under extreme load to find breaking points, capacity limits, and failure modes. Covers stress, spike, and breakpoint testing with k6 (prima

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Harga belum dikonfirmasi★ 55 Star GitHubDirektori diperbarui · 9 Okt 2026agent-skill

Ringkasan

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.

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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-only mode 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, 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

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)

Lihat sumber ↗

Metadata berkas

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

Lihat teks asli

---
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)

Tinjau sumber

Harga dan biaya penggunaan

Dapatkan skill: Harga belum dikonfirmasi
Jalankan: Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
Lisensi: Apache-2.0
Harga belum dikonfirmasi: Harga belum dikonfirmasi. Tautan sumber dan instalasi yang ada tetap tersedia.

Gratis diperoleh bukan berarti gratis dijalankan. Harga bukan penilaian keamanan. Kirim informasi harga →

Sumber skill tercatat

Jalur instruksi telah dicatat. Ini bukan uji eksekusi, jaminan keamanan, atau sertifikasi kompatibilitas.

Tinjau sebelum memasang: Hindari pemasangan otomatis

Lisensi: 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
Persetujuan tinjauan AI belum ada
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

Buka audit lengkap

Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.

Mulai dengan tugas kecil

1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.

Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

Sumber dan catatan penggunaan

TerindeksDiperiksa statis

Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.

Repositori sumber: arcasilesgroup/ai-engineering
Lisensi: Apache-2.0
Versi: Unknown
Push GitHub terakhir: 16 Sep 2026
Direktori diperbarui: 9 Okt 2026
Jalur instruksi: skills/ai-stress-test/SKILL.md @ 83858b7c8746

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

59/100

Menjanjikan

Kepercayaan

62/100

Hanya sandbox

Audit

74/100

Perlu ditinjau

Buka audit lengkap Lihat laporan evaluasi

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
Persetujuan tinjauan AI belum ada
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: —
Hasil: —

Menyalin bukan memasang. Jumlah instalasi memerlukan laporan berhasil dan bukan jaminan kualitas menyeluruh.

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Detail lainnya

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    "label": "Manual review",
    "version": "trust-score-v4",
    "install_policy": "block",
    "evidence": {
      "stars": "55 GitHub stars",
      "repoActivity": "55 stars, 3 forks",
      "lastPushed": "28d since push",
      "license": "Apache-2.0",
      "repository": "https://github.com/arcasilesgroup/ai-engineering/tree/main/skills/ai-stress-test",
      "install": "npx skills add arcasilesgroup/ai-engineering --skill ai-stress-test",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "coding-agents",
      "agent-skill"
    ],
    "known_risks": [
      "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"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 74,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "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"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 59,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "28d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "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"
  ],
  "agent_contract": {
    "task_input": "Use ai-stress-test in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 70/100 Manual review",
      "Audit: 74/100 Needs review",
      "Safety: 34/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "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"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: arcasilesgroup
Sumber: arcasilesgroup/ai-engineering
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan arcasilesgroup, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/arcasilesgroup-ai-stress-test?metric=listed&label=Listed)](https://www.openagentskill.com/skills/arcasilesgroup-ai-stress-test?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/arcasilesgroup-ai-stress-test?metric=trust&label=Trust)](https://www.openagentskill.com/skills/arcasilesgroup-ai-stress-test?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/arcasilesgroup-ai-stress-test?metric=audit&label=Audit)](https://www.openagentskill.com/skills/arcasilesgroup-ai-stress-test/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/arcasilesgroup-ai-stress-test?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/arcasilesgroup-ai-stress-test?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.