Marin is an open-source research program, software platform, and community for developing foundation models such as large language models. Researchers use it for data preparation, tokenization, pretraining, posttraining, evaluation, and related experiments, including work on audio-text, DNA, and protein models. The catalogue entries are add-ons that support workflows around Marin.
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/marin-community/marin/use-irisnpx skills add marin-community/marin --skill use-irisgit clone --depth 1 https://github.com/marin-community/marinWrote 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/marin-community/marin/use-iris)<a href="https://agentmods.dev/skills/marin-community/marin/use-iris"><img src="https://agentmods.dev/badge/skills/marin-community/marin/use-iris.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 5.1 | $0.00079 | $0.00669 |
| Opus 5 | $0.00039 | $0.00334 |
| Sonnet 5 | $0.00016 | $0.00134 |
| Haiku 4.5 | $0.00008 | $0.00067 |
Grade A, and why
use-iris 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use Iris
Read only the material needed for the request:
- Normal jobs, tasks, scheduling, auth, or CoreWeave:
lib/iris/OPS.md. - Federation:
lib/iris/docs/federation.md. - Continuous job monitoring: references/monitor-job.md.
- Controller deploy or rollback: references/controller-rollout.md.
- Interactive GPU or TPU: references/dev-accelerators.md.
- Stuck terminating CoreWeave pod: references/stuck-pod.md.
- Temporary task outputs:
lib/iris/docs/task-outputs.md. - Logs or measurements: use
query-finelog.
Resolve cluster facts from lib/iris/config/<cluster>.yaml; do not copy live coordinates from memory.
Common reads
uv run iris --cluster=<cluster> job describe <job>
uv run iris --cluster=<cluster> task describe <task>
uv run iris --cluster=<cluster> task events <task>
uv run iris --cluster=<cluster> rpc controller list-backends
For a pending federated root, inspect all three parent-side views:
uv run iris --cluster=<parent> job list --prefix <root-job>
uv run iris --cluster=<parent> rpc controller list-peers
uv run iris --cluster=<parent> query \
"SELECT job_id, peer_id, handoff_state FROM federated_jobs WHERE job_id='<root-job>'"
Only root jobs federate; their whole tree stays on the peer. Parent job describe is the liveness source, while forwarded logs may lag. CoreWeave tasks normally read regional S3 and GCP tasks read GCS.
Temporary outputs
Write bounded diagnostics to $IRIS_OUTPUT_DIR. Iris preserves that directory as one outputs.tar.zst archive per attempt without changing the command outcome when capture fails. Find the archive URI and its uploaded, empty, failed, or unavailable state with:
uv run iris --cluster=<cluster> attempt describe <task>:<attempt>
Use direct object-storage writes for large or durable outputs. See lib/iris/docs/task-outputs.md for retention, limits, and data-access boundaries.
What ships with it
4 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 · 58 lines · 79 tokens per session scan A fc213846b93b
use-iris is a skill published in the GitHub repository marin-community/marin (3,403 stars, last pushed today), licensed Apache-2.0. It adds 79 tokens to every session and 669 once invoked, about $0.0004 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 2026-08-30.
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