Naoya Moritani n0531m

## SYSTEM.md

      
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                ksprashu
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                Personal SYSTEM.md override for hardcoded instructions
              
          
    This document, SYSTEM.md, defines the external rules and constraints of the Gemini agent. It outlines the environment, available tools, and the fundamental operational mandates that govern my behavior at the system level. This is the rulebook.
You are an interactive CLI agent specializing in software engineering tasks. Your primary goal is to help users safely and efficiently, adhering strictly to the following instructions and utilizing your available tools.
Core Mandates


Model Constraint: My core reasoning engine is pinned to the highest-quality, generally available model family, which is currently Gemini 2.5. All my reasoning, planning, and generation tasks must use this model family to ensure consistency and quality. I am forbidden from using older model families (e.g., Gemini 1.5) unless explicitly instructed for a temporary, specific purpose. The adoption of a new, superior model family (e.g., Gemini 3.0) must be a deliberate, user-approved update to this directive.
**Conventions:


## GEMINI.md

      
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                Personal GEMINI.md as on 29-07-2025
              
          
    Gemini Agent: Core Directives and Operating Protocols


## census_zip.sql
#standardsql
with zipcodes as (
SELECT
  zip_census.zipcode as zipcode,
  population,
  WKT as geometry,
  ST_CENTROID(ST_GeogFromText(WKT)) as centroid
FROM
  `bigquery-public-data.census_bureau_usa.population_by_zip_2010` AS zip_census
  join `bigquery-public-data-staging.zcta_test.2017` as zip_geom

## vectorize.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


import numpy as np
import os
import sys
import tensorflow as tf
import json

## vectorize.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import json
import numpy as np
import os
import tarfile
import tensorflow as tf

## jigsaw.html
<!DOCTYPE html>
<html>

<head>
   <meta charset="UTF-8">
   <title>Jigsaw puzzle</title>
   <script type="text/javascript">
      function save(filename, data)
      {
         var blob = new Blob([data], {type: "text/csv"});
	#standardsql
	with zipcodes as (
	SELECT
	zip_census.zipcode as zipcode,
	population,
	WKT as geometry,
	ST_CENTROID(ST_GeogFromText(WKT)) as centroid
	FROM
	`bigquery-public-data.census_bureau_usa.population_by_zip_2010` AS zip_census
	join `bigquery-public-data-staging.zcta_test.2017` as zip_geom
	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function


	import numpy as np
	import os
	import sys
	import tensorflow as tf
	import json
	<!DOCTYPE html>
	<html>

	<head>
	<meta charset="UTF-8">
	<title>Jigsaw puzzle</title>
	<script type="text/javascript">
	function save(filename, data)
	{
	var blob = new Blob([data], {type: "text/csv"});