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/fix-issuenpx skills add marin-community/marin --skill fix-issuegit 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/fix-issue)<a href="https://agentmods.dev/skills/marin-community/marin/fix-issue"><img src="https://agentmods.dev/badge/skills/marin-community/marin/fix-issue.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 | $0.00021 | $0.00554 |
| Opus 5 | $0.00010 | $0.00277 |
| Sonnet 5 | $0.00004 | $0.00111 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
fix-issue 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 5d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fix a GitHub issue
Read AGENTS.md and the issue guidance in writing-style. Keep issue comments
terse: omit filler and repetition, use annotated links, and keep each prose
section to three or four sentences. Complete the workflow in order. If blocked,
comment on the issue with the last completed state and the blocker.
Research
Use gh to fetch the issue. Read the codebase for all relevant source files.
Post one concise comment with Research and Proposed Fix sections.
Before writing code, run a duplicate-work preflight:
- Check open PRs for the same issue or subsystem (
gh pr list --state open --search "<issue-id or keyword>"). - Check open issues for the same root cause (
gh issue list --state open --search "<keyword>"). - If overlapping work exists, do not open a parallel implementation PR. Add a short issue comment summarizing what you found, and either hand off to the existing PR or scope your change to non-overlapping follow-up work.
In Research, state the cause in two or three sentences and link at most five
relevant code locations with short annotations. In Proposed Fix, name the
smallest fix and include the failing call chain or a snippet only when needed.
Implementation
Implement on agent/{YYYYMMDD}-fix-{issue-id}.
Testing
Follow write-tests and TESTING.md. Prefer a regression test that fails before
the fix, extend an existing file, run the narrow test and affected safe suite,
then run ./infra/pre-commit.py --all-files --fix. Do not override the configured
marker expression.
Uploading
When all tests pass, upload your branch and open a PR following
.agents/skills/commit/SKILL.md exactly — use the plain-text format it
specifies (no markdown headers, bullet lists, or ## Summary sections;
violations are rejected). Attach a comment to the Github issue summarizing the fix.
Monitor the PR
After opening the PR, follow step 9 of .agents/skills/commit/SKILL.md
exactly. Its wait_for.py loop owns CI, review feedback, and lifecycle events.
Do not start a separate gh pr view or gh pr checks polling loop. Investigate
failures, push fixes, and re-arm the wait as that skill specifies.
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.
- 5d ago First seen · 56 lines · 21 tokens per session scan A 330ce6bd9133
fix-issue is a skill published in the GitHub repository marin-community/marin (3,403 stars, last pushed today), licensed Apache-2.0. It adds 21 tokens to every session and 554 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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