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/task-logbooknpx skills add marin-community/marin --skill task-logbookgit 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/task-logbook)<a href="https://agentmods.dev/skills/marin-community/marin/task-logbook"><img src="https://agentmods.dev/badge/skills/marin-community/marin/task-logbook.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 | $0.00038 | $0.01326 |
| Opus 5 | $0.00019 | $0.00663 |
| Sonnet 5 | $0.00008 | $0.00265 |
| Haiku 4.5 | $0.00004 | $0.00133 |
Grade A, and why
task-logbook 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task logbook
Concepts
Keep the detailed append-only record in the logbook. If a coordinating issue exists, publish significant updates as comments and maintain its body as the current summary.
Location and Naming
- Store logbooks under
.agents/logbooks/<topic>.md. - Use a short, stable topic slug. Including the GitHub issue number is often a good choice.
- Link to the coordinating GitHub issue or PR near the top of the logbook when one exists.
Logbook
New logbooks are Markdown files with YAML frontmatter.
Logbook Template
---
topic:
issue:
description:
author:
---
# <Topic>: Task Logbook
## Scope
- Goal:
- Primary metric(s):
- Constraints:
- Coordinating issue/PR:
## Baseline
- Date:
- Code refs:
- Baseline numbers:
## Entry Log
### YYYY-MM-DD HH:MM - <short label>
- Hypothesis:
- Commit Hash:
- Command:
- Config:
- Result:
- Interpretation:
- Next action:
When code changes affect reproducibility, make a lightweight WIP commit and
record its hash. Stage only the files needed for the result. Apply the full
commit workflow when promoting the work to a production PR.
For a research series, use one short experiment ID consistently in logbook entries, W&B runs, and issue comments.
Use the requesting user as author. Omit issue when none exists.
Write Rules
- Append entries; do not rewrite history except to fix formatting or broken links or to add a coordinating issue reference.
- Each non-trivial result needs exact commands, hash, config, key output, and the decision it caused.
- Record failures and negative results with enough detail to avoid rediscovery.
- Prefer terse tables for comparable numeric results.
- Link large artifacts, W&B runs, dashboards, pinned GitHub paths, etc., instead of pasting dense output.
- Keep claims scoped and falsifiable.
- Label major claims when useful:
exploratory: single run or weak evidence.replicated: repeated and consistent.stable: held across relevant shape, seed, hardware, or workflow variants.
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 · 181 lines · 38 tokens per session scan A fd7b5624e8c3
task-logbook is a skill published in the GitHub repository marin-community/marin (3,403 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 1,326 once invoked, about $0.0002 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.
Other skills, from other repositories
media-ingest
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
agent-memory
../../../engineering/agent-memory/skills/agent-memory/SKILL.md.
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…