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/amdresearch/ai4science-studio/list-modelsgit clone --depth 1 https://github.com/AMDResearch/ai4science-studioWrote 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/amdresearch/ai4science-studio/list-models)<a href="https://agentmods.dev/commands/amdresearch/ai4science-studio/list-models"><img src="https://agentmods.dev/badge/commands/amdresearch/ai4science-studio/list-models.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 | $0.00000 | $0.00288 |
| Opus 5 | $0.00000 | $0.00144 |
| Sonnet 5 | $0.00000 | $0.00058 |
| Haiku 4.5 | $0.00000 | $0.00029 |
Grade A, and why
list-models 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 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.
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.
What it actually says
List and filter models in AI4Science Studio
Discover what models are available in this repository.
How to respond
- Read
models.yamlat the repo root. - Parse the YAML and present the models in a table.
- If the user provided filter criteria (in $ARGUMENTS), apply them.
Default output (no filter)
Present ALL models in a table:
| Model | Domain | HF id | License | Tasks | Path |
|---|---|---|---|---|---|
| ... | ... | ... | ... | ... | ... |
Filter examples
- By domain: "earth_science" → show only earth_science models
- By task: "finetune" → show only models with
finetuneintasks_available - By license: "MIT" → show only MIT-licensed models
- By HF availability: "has HF weights" → filter where
hf_id != N/A - By hardware: read individual
model.yamlfiles and filter byvalidated_hardware
Deep dive
If the user names a specific model, read its model.yaml and present:
- Full metadata (HF id, license, upstream code, paper)
- Available recipes with descriptions
- Environment variables with defaults
- Container image and validated hardware
Arguments
$ARGUMENTS
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 · 38 lines · 0 tokens per session scan A d0132b1969e8
list-models is a command published in the GitHub repository AMDResearch/ai4science-studio (4 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 288 tokens. 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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