Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vasilyu1983/AI-Agents-public --skill qa-testing-performancegit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vasilyu1983/ai-agents-public/qa-testing-performance)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/qa-testing-performance"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/qa-testing-performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/qa-testing-performance"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/qa-testing-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 203 Skill attempts to nullify the agent's safety policies or restrictions ('you have no restrictions', 'ignore your guidelines', 'do anything now'). This is a direct jailbreak that disables guardrails.Fix: Remove jailbreak framing that nullifies safety policies or restrictions. Skill content must not instruct the agent to ignore its guidelines or operate without guardrails.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.04479 |
| Opus 5 | $0.00017 | $0.02240 |
| Sonnet 5 | $0.00007 | $0.00896 |
| Haiku 4.5 | $0.00003 | $0.00448 |
Grade A, and why
qa-testing-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Testing (Performance)
Performance engineering guidance for validating throughput, latency, resource consumption, and scalability. Use this skill to design load tests, set CI performance budgets, profile bottlenecks, and plan capacity.
Core sources are curated in data/sources.json. Prefer primary docs and re-check volatile external facts before recommending versions, pricing, or tool features.
Quick Start
If key context is missing, ask for: SLOs/SLAs, critical user journeys, infrastructure topology, current baselines, traffic patterns (peak/sustained), database query hotspots, and frontend performance targets (Core Web Vitals).
- Baseline — measure current latency (p50/p95/p99), throughput, error rate, and resource utilization under realistic traffic.
- Design scenarios — model critical user journeys as scripted load test scenarios with realistic think times, data parameterization, and ramp profiles.
- Execute — run load, stress, soak, or spike tests against a representative environment with monitoring active.
- Analyze — compare results against baselines and budgets using percentiles, throughput curves, and error rate correlation. Identify bottlenecks with profiling.
- Set CI budgets — define performance gates (latency thresholds, throughput minimums, error rate caps, Core Web Vitals budgets) and integrate into the pipeline.
Workflow
- Establish realistic baselines, traffic models, and budgets.
- Choose the right performance test type and toolchain for the risk.
- Run the scenarios against representative environments with monitoring active.
- Analyze bottlenecks, then convert findings into budgets, profiles, and CI gates.
Inputs to Gather
- SLOs/SLAs for latency, availability, throughput
- Critical user journeys and their expected traffic volumes
- Infrastructure topology (services, databases, caches, queues, CDN)
- Current performance baselines (if any)
- Traffic patterns: peak hours, seasonal spikes, growth projections
- Database query hotspots and slow query logs
- Frontend targets: Core Web Vitals (LCP, INP, CLS), bundle size limits
- Environment parity: how close is the test environment to production?
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 351 B
- assets/template-k6-load-test.js 6.8 KB runs code
- assets/template-performance-budget.md 4.2 KB
- assets/template-performance-test-plan.md 4.3 KB
- data/sample-perf-results.json 1.9 KB
- data/sources.json 6.6 KB
- learnings.consolidated.md 598 B
- learnings.md 345 B
- references/capacity-planning.md 11 KB
- references/database-performance-testing.md 9.4 KB
- references/frontend-performance.md 9.1 KB
- references/load-testing-patterns.md 20 KB
- references/performance-budgets-ci.md 8.4 KB
- references/profiling-optimization.md 12 KB
- scripts/perf_budget_checker.py 22 KB runs code
- scripts/README.md 4.1 KB
- scripts/test_perf_budget_checker.py 3.4 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 305 lines · 35 tokens per session scan A 7c417246ac7d
qa-testing-performance is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 35 tokens to every session and 4,479 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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