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.
Borrowing it
Nothing to install: this file belongs to marin-community/marin. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/marin-community/marin/main/.agents/skills/run-ferries/SKILL.mdgit 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/run-ferries)<a href="https://agentmods.dev/skills/marin-community/marin/run-ferries"><img src="https://agentmods.dev/badge/skills/marin-community/marin/run-ferries/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/marin-community/marin/run-ferries"><img src="https://agentmods.dev/badge/skills/marin-community/marin/run-ferries.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 32 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 90 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Agent Snooping · line 141 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 168 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00022 | $0.02086 |
| Opus 5 | $0.00011 | $0.01043 |
| Sonnet 5 | $0.00004 | $0.00417 |
| Haiku 4.5 | $0.00002 | $0.00209 |
Grade A, and why
run-ferries 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 9d 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ferries
experiments/ferries/canary_ferry.py(MoE canary, TPU and GPU viaCANARY_ACCELERATOR)experiments/ferries/daily.py
Canary is the stable, low-cost health check. Daily exercises a larger envelope with one or two explicit changes.
Shared baseline:
- data: shared
nemotron_mixbaseline - default cluster:
us-central1(zoneus-central1-a) - run log:
docs/experiments/daily-ferry-log.md
Daily defaults to llama_150m, sequence length 4096, batch size 512, and about
1e19 FLOPs unless its configuration says otherwise.
Inputs Before Proposing (Daily Only)
Canary runs normally do not require a proposal cycle or PR. For daily, collect:
- Last ferry references: issue URL, PR/commit URL, W&B run URL and Iris job ID
- Human objective for this interval: standard integration pass, or explicit regression investigation
- Interval boundary: use "since last ferry run", not fixed wall-clock day boundaries
If objective is ambiguous, ask before editing.
Operating Policy
General
- Hard launch gate: get explicit requester approval before launching any ferry job. Only exception: the requester explicitly says to launch without asking.
- Follow the
use-irisskill's job-monitoring workflow until the run reaches a terminal state (SUCCEEDED/FAILED/STOPPED); do not stop early. Full ferry monitoring often takes 4-5 hours. - Never restart/recreate/mutate cluster without explicit human consent in-thread. Keep cluster mutation guardrails aligned with the Iris monitoring workflow, including the debug exception path.
- Use major-event updates (not spam): launch, first eval, major incident, terminal state.
- Seal each completed daily run with a pushed git tag pointing to the exact launch commit.
- Canonical run-closure PR labels:
ferry,ferry-daily,ferry-log-only,ferry-sealed. - Canonical seal-tag format (daily):
ferry/daily/YYYYMMDD/<run_slug>
Canary
- Keep canary stable; only change it for explicit reliability fixes, and only when diagnosing/fixing a concrete failure mode.
- Canary launches usually do not require a PR if the script/config is unchanged.
- If canary fails, treat as urgent infrastructure/training-health triage.
- Canary is run-only by default (W&B + issue updates); no sealing tag or run-closure PR in the normal path.
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.
- 9d ago First seen · 184 lines · 22 tokens per session scan A 5cf728eff87e
run-ferries is a skill published in the GitHub repository marin-community/marin (3,512 stars, last pushed today), licensed Apache-2.0. It adds 22 tokens to every session and 2,086 once invoked, about $0.0001 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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