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 software-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/software-performance)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/software-performance"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/software-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/software-performance"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/software-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00031 | $0.03252 |
| Opus 5 | $0.00015 | $0.01626 |
| Sonnet 5 | $0.00006 | $0.00650 |
| Haiku 4.5 | $0.00003 | $0.00325 |
Grade A, and why
software-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 6d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Engineering
Use this skill for systematic performance work across APIs, web apps, services, and release gates. It owns profiling, load and stress testing, performance budgets, and regression detection. It does not replace SQL tuning, observability setup, or system-architecture design.
Quick Reference
| Task | Use |
|---|---|
| Profiling hot paths | flamegraph-first profiling with the right language toolchain |
| Load and stress testing | k6 by default; alternate tools only when the team has a clear reason |
| Frontend or backend budgets | CI thresholds plus representative benchmarks |
| Database-related slowdown | query and pool investigation first, then broader system review |
| Continuous regression detection | benchmark suites plus statistical comparison |
| CWV thresholds, k6 patterns, Lighthouse CI budget JSON, profiling tools | references/perf-budgets-and-cwv.md |
| assert CWV budgets from Lighthouse JSON | scripts/check_perf_budget.py |
When to Use
- Diagnose slow API responses, page loads, or jobs.
- Design and run load, stress, soak, or spike tests.
- Set and enforce performance budgets.
- Find CPU, memory, or I/O bottlenecks.
- Plan capacity or benchmark architectural changes.
- Catch regressions before they reach production.
Route Elsewhere
- SQL query tuning and indexing: use data-sql-optimization.
- System architecture and scaling design: use software-architecture-design.
- Frontend build-tool setup: use software-frontend.
- Observability and telemetry setup: use qa-observability.
- Resilience patterns such as retries and breakers: use qa-resilience.
- Mobile-specific performance: use software-mobile.
What ships with it
8 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.
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.
- 6d ago First seen · 213 lines · 31 tokens per session scan A 333fad2d4006
software-performance is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 7d ago), licensed MIT. It adds 31 tokens to every session and 3,252 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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