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 agentmods add skills/ancoleman/ai-design-components/performance-engineeringnpx skills add ancoleman/ai-design-components --skill performance-engineeringgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWhat 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 | $0.00072 | $0.02846 |
| Opus 5 | $0.00036 | $0.01423 |
| Sonnet 5 | $0.00014 | $0.00569 |
| Haiku 4.5 | $0.00007 | $0.00285 |
Grade B, and why
performance-engineering scanned grade B with 2 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 2d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt-get install k6 # Linux Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl https://github.com/grafana/k6/releases/download/v0.48.0/k6-v0.48.0-linux-amd64.tar.gz -L | tar xvz How it starts
The opening of the file, as written. The whole thing — 430 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Engineering
Purpose
Performance engineering encompasses load testing, profiling, and optimization to deliver reliable, scalable systems. This skill provides frameworks for choosing the right performance testing approach (load, stress, soak, spike), profiling techniques to identify bottlenecks (CPU, memory, I/O), and optimization strategies for backend APIs, databases, and frontend applications.
Use this skill to validate system capacity before launch, detect performance regressions in CI/CD pipelines, identify and resolve bottlenecks through profiling, and optimize application responsiveness across the stack.
When to Use This Skill
Common Triggers:
- "Validate API can handle expected traffic"
- "Find maximum capacity and breaking points"
- "Identify why the application is slow"
- "Detect memory leaks or resource exhaustion"
- "Optimize Core Web Vitals for SEO"
- "Set up performance testing in CI/CD"
- "Reduce cloud infrastructure costs"
Use Cases:
- Pre-launch capacity planning and load validation
- Post-refactor performance regression testing
- Investigating slow response times or high latency
- Detecting memory leaks in long-running services
- Optimizing database query performance
- Validating auto-scaling configuration
- Establishing performance SLOs and budgets
Performance Testing Types
Load Testing
Validate system behavior under expected traffic levels.
When to use: Pre-launch capacity planning, regression testing after refactors, validating auto-scaling.
Stress Testing
Find system capacity limits and failure modes.
When to use: Capacity planning, understanding failure behavior, infrastructure sizing decisions.
Soak Testing
Identify memory leaks, resource exhaustion, and degradation over time.
When to use: Detecting memory leaks, validating connection pool cleanup, testing long-running batch jobs.
Spike Testing
Validate system response to sudden traffic spikes.
When to use: Validating auto-scaling, testing event-driven systems (product launches), ensuring rate limiting works.
What ships with it
13 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.
- examples/k6/load-test.js 1.3 KB runs code
- examples/k6/soak-test.js 1.5 KB runs code
- examples/k6/stress-test.js 1.7 KB runs code
- examples/locust/load_test.py 1.4 KB runs code
- examples/optimization/api_optimization.ts 2.5 KB runs code
- examples/profiling/python/pyspy_example.sh 865 B runs code
- outputs.yaml 7.9 KB
- references/benchmarking.md 11 KB
- references/frontend-performance.md 15 KB
- references/optimization-strategies.md 20 KB
- references/profiling-guide.md 19 KB
- references/slo-framework.md 15 KB
- references/testing-types.md 15 KB
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.
- 2d ago First seen · 430 lines · 72 tokens per session scan B a396452603cb
performance-engineering is a skill published in the GitHub repository ancoleman/ai-design-components (517 stars, last pushed 8mo ago), licensed MIT. It adds 72 tokens to every session and 2,846 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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