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 commands/druide67/asiai/bench.esgit clone --depth 1 https://github.com/druide67/asiaiWrote 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/commands/druide67/asiai/bench.es)<a href="https://agentmods.dev/commands/druide67/asiai/bench.es"><img src="https://agentmods.dev/badge/commands/druide67/asiai/bench.es.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 | $0.00032 | $0.01803 |
| Opus 5 | $0.00016 | $0.00901 |
| Sonnet 5 | $0.00006 | $0.00361 |
| Haiku 4.5 | $0.00003 | $0.00180 |
Grade B, and why
bench.es scanned grade B with 1 finding 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
| `--power` | Validación cruzada de potencia con sudo powermetrics (IOReport siempre activo) | How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
asiai bench
Benchmark entre motores con prompts estandarizados.
Uso
asiai bench [options]
Opciones
| Opción | Descripción |
|---|---|
-m, --model MODEL |
Modelo a evaluar (por defecto: detección automática) |
-e, --engines LIST |
Filtrar motores (ej. ollama,lmstudio,mlxlm) |
-p, --prompts LIST |
Tipos de prompt: code, tool_call, reasoning, long_gen |
-r, --runs N |
Ejecuciones por prompt (por defecto: 3, para mediana + desviación estándar) |
--power |
Validación cruzada de potencia con sudo powermetrics (IOReport siempre activo) |
--context-size SIZE |
Prompt de llenado de contexto: 4k, 16k, 32k, 64k |
--export FILE |
Exportar resultados a archivo JSON |
-H, --history PERIOD |
Mostrar benchmarks anteriores (ej. 7d, 24h) |
-Q, --quick |
Benchmark rápido: 1 prompt (code), 1 ejecución (~15 segundos) |
--compare MODEL [MODEL...] |
Comparación entre modelos (2-8 modelos, mutuamente excluyente con -m) |
--card |
Generar una tarjeta de benchmark compartible (SVG local, PNG con --share) |
--share |
Compartir resultados en la base de datos comunitaria |
Ejemplo
asiai bench -m qwen3.5 --runs 3 --power
Mac Mini M4 Pro — Apple M4 Pro RAM: 64.0 GB (42% used) Pressure: normal
Benchmark: qwen3.5
Engine tok/s (±stddev) Tokens Duration TTFT VRAM Thermal
────────── ───────────────── ───────── ────────── ──────── ────────── ──────────
lmstudio 72.6 ± 0.0 (stable) 435 6.20s 0.28s — nominal
ollama 30.4 ± 0.1 (stable) 448 15.28s 0.25s 26.0 GB nominal
Winner: lmstudio (2.4x faster)
Power: lmstudio 13.2W (5.52 tok/s/W) — ollama 16.0W (1.89 tok/s/W)
Prompts
Cuatro prompts estandarizados prueban diferentes patrones de generación:
| Nombre | Tokens | Evalúa |
|---|---|---|
code |
512 | Generación de código estructurado (BST en Python) |
tool_call |
256 | Llamadas a funciones JSON / seguimiento de instrucciones |
reasoning |
384 | Problema matemático de múltiples pasos |
long_gen |
1024 | Rendimiento sostenido (script bash) |
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 · 170 lines · 32 tokens per session scan B 7be7d4e0b051
bench.es is a command published in the GitHub repository druide67/asiai (11 stars, last pushed 5d ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,803 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
perfup
Autonomous performance optimization: research, PoC, benchmark, implement, review, PR.
integrate-pipeline
Integrate a new document parsing pipeline into ParseBench: $ARGUMENTS.
netllm-connect
Wire Cursor, Claude Code, Codex, or Honcho to the local netllm router.
netllm-setup
First-time netllm install from this repo (uv sync, init, discover, verify).
netllm-swarm
Configure multi-machine LAN mesh for netllm (mDNS, peers, gateway).
/opsx-explore
Enter explore mode - think through ideas, investigate problems, clarify requirements.