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/scan-logs/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/scan-logs)<a href="https://agentmods.dev/skills/marin-community/marin/scan-logs"><img src="https://agentmods.dev/badge/skills/marin-community/marin/scan-logs.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 14 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.
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 | $0.00026 | $0.00411 |
| Opus 5 | $0.00013 | $0.00205 |
| Sonnet 5 | $0.00005 | $0.00082 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
scan-logs 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 8d 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.
What it actually says
Scan large logs
grep finds matching original lines; summarize produces a Markdown report.
They can run independently or as a pipeline.
Prerequisites
GEMINI_API_KEY must be set. The log is sent to an external Gemini API and
incurs model usage; do not send secrets or sensitive logs without approval.
Modes
grep — find matching lines
Returns line-numbered original lines matching a natural-language query.
uv run scripts/logscan.py grep <logfile> "<query>"
summarize — produce a markdown report
Summarizes the log around the query, hierarchically reducing large inputs.
uv run scripts/logscan.py summarize <logfile> "<query>"
Output is a markdown report on stdout.
Piping modes together
Narrow first, then summarize through --stdin:
uv run scripts/logscan.py grep log.txt "errors" \
| uv run scripts/logscan.py summarize --stdin "summarize these errors"
Behavior-changing options are --chunk-tokens, --concurrency, --model,
--stdin, and --verbose. The defaults are 5,000-token grep chunks,
50,000-token hierarchical summarize chunks, concurrency 16, and
gemini-2.5-flash-lite. Smaller grep chunks favor precise line matching;
large logs need no manual pre-split. Inspect --help before overriding them.
Output
- grep: Line-numbered matching lines to stdout. Progress to stderr.
- summarize: Markdown report to stdout. Progress and token usage to stderr.
Both modes report progress and token usage to stderr. Read the matched source lines before accepting a generated diagnosis.
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.
- 8d ago First seen · 58 lines · 26 tokens per session scan A caa5ad37d895
scan-logs is a skill published in the GitHub repository marin-community/marin (3,512 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 411 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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