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/organize-experiments/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/organize-experiments)<a href="https://agentmods.dev/skills/marin-community/marin/organize-experiments"><img src="https://agentmods.dev/badge/skills/marin-community/marin/organize-experiments/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/organize-experiments"><img src="https://agentmods.dev/badge/skills/marin-community/marin/organize-experiments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Privilege Escalation · line 32 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Agent Snooping · line 48 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 | $0.00563 |
| Opus 5 | $0.00011 | $0.00282 |
| Sonnet 5 | $0.00004 | $0.00113 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
organize-experiments 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Organize Experiment Reports
Overview
Curate docs/reports/index.md after new experiment issues are harvested: fold fresh entries into the right sections, refresh links, and leave ## Uncategorized empty.
Prerequisites
- Local checkout of the marin repository with write access.
- Ability to run
uvcommands. - Familiarity with the existing experiment categories in
docs/reports/index.md.
Guidelines for Humans
Standard Workflow
- Run
uv run scripts/pm/itemize_experiment_issues.pyto append new experiments. - Open the diff for
docs/reports/index.mdand identify the additions in## Uncategorized. - For each experiment:
- Match it to an existing section (e.g.,
Training and Performance,Data Experiments). - Merge any new resources (WandB links, Data Browser URLs) into the canonical entry in that section.
- Remove the placeholder entry from
## Uncategorized.
- Match it to an existing section (e.g.,
- If an experiment truly does not fit, leave it under
## Uncategorizedand add a note explaining why. - Proofread for duplicate bullets, broken Markdown, and consistent title casing.
- Commit the curated report, open a PR, or, if an interactive agent, signal to the user to look.
Link Hygiene
- Prefer direct
https://wandb.ai/...links when available; keep legacyapi.wandb.ailinks only if the direct share link does not exist. - Use
https://marin.communityData Browser URLs when the script surfaces them. - Preserve access tokens embedded in links.
Rules for Agents
- NEVER delete existing sections or conclusions.
- Keep badge styling consistent:
[]next to each experiment title. - When merging new content, update the canonical entry instead of duplicating it.
- Leave
## Uncategorizedempty whenever possible; a single placeholder sentence is fine. - Ask for guidance if an experiment does not map cleanly to known categories.
Validation
rg "^- " docs/reports/index.mdto confirm bullets exist only under curated sections, not under## Uncategorized.- Re-run
uv run scripts/pm/itemize_experiment_issues.pyif unsure that all experiments were captured. - Optional:
markdownlint docs/reports/index.mdto catch formatting drift.
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 · 49 lines · 22 tokens per session scan A d01c7cb1bbce
organize-experiments 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 563 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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