saruca GEMINI.md

saruca GEMINI.md is an instructions file for Gemini CLI from mdfranz/saruca. It costs 2,538 tokens per session, scanned A, original, Apache-2.0.

A set of Gemini CLI instructions for Python development and marimo, a tool for building interactive data-analysis notebooks.

In plain words
What is it for?
Use it when writing or reviewing Python, processing JSON or log files, converting data to Parquet, performing statistical analysis, or creating marimo notebooks.
Why use it?
It gives the coding agent consistent rules for exploring log files, managing Python environments, analyzing data, and editing marimo notebooks safely.

Instructions file for Gemini CLI

Install

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.

agentmods
npx agentmods add instructions/mdfranz/saruca/gemini-md
Clone the repo
git clone --depth 1 https://github.com/mdfranz/saruca

Made for: Gemini CLI.

Wrote 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.

agentmods badge for saruca GEMINI.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/mdfranz/saruca/gemini-md.svg)](https://agentmods.dev/instructions/mdfranz/saruca/gemini-md)
Your own site
<a href="https://agentmods.dev/instructions/mdfranz/saruca/gemini-md"><img src="https://agentmods.dev/badge/instructions/mdfranz/saruca/gemini-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 2,538 This file is loaded in full into every session.
When invoked 2,538 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.02538 $0.02538
Opus 5 $0.01269 $0.01269
Sonnet 5 $0.00508 $0.00508
Haiku 4.5 $0.00254 $0.00254

Measured 3d ago against content hash f7930e856626, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

saruca GEMINI.md 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 3d 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.

GEMINI.md · 380 lines

How it starts

The opening of the file, as written. The whole thing — 380 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Core Instructions for Python

Python Coding Style

  • Use Python or jq to parse and analyze log files for initial exploration
  • For large files preview with hq and head if they are larger than 20MB
  • Review existing Python code in the current directory before writing new code to solve problems.
  • Use uv to create virtual environments and install libraries. Maintain a requirements.txt file.
  • Use orjson instead of the built-in json library for better performance.
  • Use Python polars to convert JSON to parquet if needed.
  • Use Python pandas for statistical analysis if beneficial.

Instructions for Marimo

I am a specialized AI assistant designed to help create data science notebooks using marimo. I focus on creating clear, efficient, and reproducible data analysis workflows with marimo's reactive programming model.

If you make edits to the notebook, only edit the contents inside the function decorator with @app.cell. marimo will automatically handle adding the parameters and return statement of the function. For example, for each edit, just return:

@app.cell
def _():
    <your code here>
    return

Marimo fundamentals

Marimo is a reactive notebook that differs from traditional notebooks in key ways:

  • Cells execute automatically when their dependencies change
  • Variables cannot be redeclared across cells
  • The notebook forms a directed acyclic graph (DAG)
  • The last expression in a cell is automatically displayed
  • UI elements are reactive and update the notebook automatically

Code Requirements

  1. All code must be complete and runnable
  2. Follow consistent coding style throughout
  3. Include descriptive variable names and helpful comments
  4. Import all modules in the first cell, always including import marimo as mo
  5. Never redeclare variables across cells
  6. Ensure no cycles in notebook dependency graph
  7. The last expression in a cell is automatically displayed, just like in Jupyter notebooks.
  8. Don't include comments in markdown cells
  9. Don't include comments in SQL cells
  10. Never define anything using global.

Read the full file on GitHub · 380 lines

Changes

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.

  1. 3d ago First seen · 380 lines · 2,538 tokens per session scan A f7930e856626

Subscribe to this mod's changes

saruca GEMINI.md is an instructions file published in the GitHub repository mdfranz/saruca (1 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 2,538 tokens to every session, about $0.0127 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 2026-08-31.