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/query-finelog/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/query-finelog)<a href="https://agentmods.dev/skills/marin-community/marin/query-finelog"><img src="https://agentmods.dev/badge/skills/marin-community/marin/query-finelog/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/query-finelog"><img src="https://agentmods.dev/badge/skills/marin-community/marin/query-finelog.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.01103 |
| Opus 5 | $0.00029 | $0.00551 |
| Sonnet 5 | $0.00012 | $0.00221 |
| Haiku 4.5 | $0.00006 | $0.00110 |
Grade A, and why
query-finelog 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 10d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Query Finelog
Read lib/finelog/OPS.md for access and query behavior. Read lib/iris/OPS.md under Stats Namespaces for Iris namespace meanings.
Discover before querying; do not assert remembered columns:
uv run finelog namespaces <deployment>
uv run finelog schema <deployment> <namespace>
uv run finelog query <deployment> --format table <<'SQL'
<bounded SQL using schema-confirmed columns>
SQL
finelog query reads SQL from stdin when the positional SQL argument is omitted.
Use marin for the federated view and a regional deployment for peer-local truth or recent rows that may not have forwarded. Preserve cluster and full process/label identity until after per-series delta calculations.
Bound the native time key. Keep telemetry_v1.timestamp_ms predicates numeric. Treat current snapshots as values, imported Prometheus counters as cumulative snapshots with LAG and reset handling, and native Rigging counters as deltas to SUM directly.
Never reset or change a shared namespace during diagnosis. Return the deployment, namespace, time window, query, series semantics, and any forwarding or retention caveat.
Examples
Confirm every schema before adapting an example. Angle-bracket values are placeholders.
Iris task memory by half-hour
Adapted from Echo wiki 230. The dashboard task ID includes the final task index. Select or group by attempt_id after retries.
SELECT date_bin(INTERVAL '30 minutes', ts,
TIMESTAMP '1970-01-01 00:00:00') AS bucket_start_utc,
count(*) AS samples,
round(min(memory_mb) / 1024.0, 1) AS min_gib,
round(median(memory_mb) / 1024.0, 1) AS median_gib,
round(max(memory_mb) / 1024.0, 1) AS max_gib,
round(max(memory_peak_mb) / 1024.0, 1) AS attempt_peak_gib
FROM "iris.task"
WHERE task_id = '/user/job/task'
AND attempt_id = 0
GROUP BY bucket_start_utc
ORDER BY bucket_start_utc
memory_mb is sampled current memory; memory_peak_mb is the attempt's cumulative peak. Values are MiB despite the names. count(*) exposes partial buckets and gaps. Query the regional deployment if recent hub rows appear incomplete.
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.
- 10d ago First seen · 118 lines · 58 tokens per session scan A 52875188ba5b
query-finelog is a skill published in the GitHub repository marin-community/marin (3,548 stars, last pushed today), licensed Apache-2.0. It adds 58 tokens to every session and 1,103 once invoked, about $0.0003 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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