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/benchgit 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)<a href="https://agentmods.dev/commands/druide67/asiai/bench"><img src="https://agentmods.dev/badge/commands/druide67/asiai/bench.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 | $0.00027 | $0.01633 |
| Opus 5 | $0.00014 | $0.00816 |
| Sonnet 5 | $0.00005 | $0.00327 |
| Haiku 4.5 | $0.00003 | $0.00163 |
Grade B, and why
bench 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 3d 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` | Cross-validate power with sudo powermetrics (IOReport always-on) | 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
Cross-engine benchmark with standardized prompts.
Usage
asiai bench [options]
Options
| Option | Description |
|---|---|
-m, --model MODEL |
Model to benchmark (default: auto-detect) |
-e, --engines LIST |
Filter engines (e.g., ollama,lmstudio,mlxlm) |
-p, --prompts LIST |
Prompt types: code, tool_call, reasoning, long_gen |
-r, --runs N |
Runs per prompt (default: 3, for median + stddev) |
--power |
Cross-validate power with sudo powermetrics (IOReport always-on) |
--context-size SIZE |
Context fill prompt: 4k, 16k, 32k, 64k |
--export FILE |
Export results to JSON file |
-H, --history PERIOD |
Show past benchmarks (e.g., 7d, 24h) |
-Q, --quick |
Quick benchmark: 1 prompt (code), 1 run (~15 seconds) |
--compare MODEL [MODEL...] |
Cross-model comparison (2–8 models, mutually exclusive with -m) |
--card |
Generate a shareable benchmark card (SVG locally, PNG with --share) |
--share |
Share results to community benchmark database |
Example
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
Four standardized prompts test different generation patterns:
| Name | Tokens | Tests |
|---|---|---|
code |
512 | Structured code generation (BST in Python) |
tool_call |
256 | JSON function calling / instruction following |
reasoning |
384 | Multi-step math problem |
long_gen |
1024 | Sustained throughput (bash script) |
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.
- 3d ago First seen · 170 lines · 27 tokens per session scan B 9cb498b496ee
bench is a command published in the GitHub repository druide67/asiai (11 stars, last pushed 4d ago), licensed Apache-2.0. It adds 27 tokens to every session and 1,633 once invoked, about $0.0001 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
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memo
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netllm-connect
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netllm-doctor
Troubleshoot netllm misconfigurations (PATH, providers, mDNS, agent health).
check-order
Look up a single order by id through the ordersdb MCP server and report it in a strict pipe-separated format that downstream parsers can consume.
verify
Spawn a fresh-context verifier subagent to check completed work against its specification before trusting it.