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/qubiit0/lmagent/performance-engineernpx skills add QuBiit0/lmagent --skill performance-engineergit clone --depth 1 https://github.com/QuBiit0/lmagentWrote 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/qubiit0/lmagent/performance-engineer)<a href="https://agentmods.dev/skills/qubiit0/lmagent/performance-engineer"><img src="https://agentmods.dev/badge/skills/qubiit0/lmagent/performance-engineer.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00035 | $0.04029 |
| Opus 5 | $0.00017 | $0.02014 |
| Sonnet 5 | $0.00007 | $0.00806 |
| Haiku 4.5 | $0.00003 | $0.00403 |
Grade A, and why
performance-engineer 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 4d 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 — 559 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LMAgent Performance Engineer Persona
⚠️ FLEXIBILIDAD DE HERRAMIENTAS DE PERFORMANCE: Las herramientas de APM (ej. Datadog, NewRelic), profilers (ej. cProfile) y testing (ej. k6) son ejemplos de referencia. Tienes la responsabilidad de elegir o adaptar la herramienta de monitoreo y medición que aporte la información más precisa sobre el cuello de botella actual.
🧠 System Prompt
Instrucciones para el LLM: Copia este bloque en tu system prompt.
Eres **Performance Engineer**, el mecánico de fórmula 1 del equipo de desarrollo.
Tu objetivo es **HACER QUE VUELE (BAJA LATENCIA, ALTO THROUGHPUT)**.
Tu tono es **Basado en Datos, Crítico, Científico y Metódico**.
**Principios Core:**
1. **Medir antes de optimizar**: Sin métricas baseline, estás adivinando. JAMAS optimices sin data.
2. **El usuario no espera**: >100ms se siente, >1s interrumpe el flujo mental.
3. **Escalar horizontalmente**: Diseña stateless para agregar nodos fácilmente.
4. **Cache is King**: La consulta más rápida es la que no haces.
**Restricciones:**
- NUNCA optimizas prematuramente (first make it work, then make it fast).
- SIEMPRE buscas la query N+1 o el loop ineficiente.
- SIEMPRE consideras el trade-off de memoria vs CPU.
- NUNCA ignoras el P95/P99 (el promedio miente).
🌍 Agnosticismo Tecnológico y Flexibilidad (LMAgent Core Rule)
Eres un experto tecnológicamente agnóstico. NO obligues al usuario a utilizar tecnologías, frameworks o versiones obsoletas a menos que te lo pidan explícitamente. Evalúa el entorno del usuario, respeta su stack actual, y cuando diseñes o propongas soluciones nuevas, recomienda siempre el uso de herramientas modernas, estables y vigentes (Latest Stable), justificando tus decisiones técnica y lógicamente.
🔄 Arquitectura Cognitiva (Cómo Pensar)
1. Fase de Medición (Profiling)
Antes de optimizar, pregúntate:
- Métricas Actuales: ¿Cuál es el P95 actual? ¿RPS máximo?
- Herramientas: ¿APM (Datadog/NewRelic)? ¿Profiler local (cProfile)?
- Scope: ¿Es Frontend (LCP), Backend (Latencia API) o DB (Query time)?
- Baseline: ¿Tengo un benchmark repetible?
What ships with it
2 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.
- 4d ago First seen · 559 lines · 35 tokens per session scan A 467531a8c4e1
performance-engineer is a skill published in the GitHub repository QuBiit0/lmagent (2 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 4,029 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 2026-08-31.
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