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What is scratch-food-rubrics?

kyleamathews/scratch-food-rubrics — explained in plain English

Analysis updated 2026-05-18

3PythonAudience · generalComplexity · 3/5LicenseSetup · moderate

In one sentence

AI agent skills that score restaurants and bakeries on how much of their food is genuinely made from scratch.

Mindmap

mindmap
  root((scratch-food-rubrics))
    What it does
      Scores restaurants and bakeries
      Detects par-bake shortcuts
      Gathers evidence quotes
    Tech stack
      Python
      OpenStreetMap
      Agent skills
    Use cases
      Restaurant discovery
      Bakery screening
      Local food research
    Audience
      Frequent diners
      Coding agent users

Code map

Detail Auto

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filefunction / class

What do people build with it?

USE CASE 1

Find restaurants in a given location that make their food genuinely from scratch.

USE CASE 2

Screen bakeries for par-bake shortcuts versus true on-site baking.

USE CASE 3

Generate a ranked, evidence-backed list of standout local food spots.

USE CASE 4

Run a repeatable, self-contained research process for a specific city or area.

What is it built with?

PythonOpenStreetMap

How does it compare?

kyleamathews/scratch-food-rubrics0marildo/imago0xdfi/glm-5.2-1m-4x-dgx-spark
Stars333
LanguagePythonPythonPython
Setup difficultymoderateeasyhard
Complexity3/52/55/5
Audiencegeneralgeneralresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires an AI coding agent like Claude Code and installing the full repository, not a single file.

You can use, modify, and share this freely, including commercially, as long as you keep the copyright notice.

So what is it?

scratch-food-rubrics is a set of skills meant to be used by AI coding agents like Claude Code or Codex, designed to help find restaurants and bakeries that actually make their food from raw ingredients on site, rather than reheating or finishing off pre-made components. It ships as two separate rubrics, one for restaurants and one for bakeries, each triggered by typing a slash command followed by a location. Each rubric scores candidate places on three measures: how much of the food is genuinely made from scratch, how interesting the place is to someone who eats or shops there often, based on menu variety and how rare that kind of food is locally, and a basic quality rating used only to filter out poor places rather than to rank them. The bakery version is tuned to catch specific tells of shortcuts, such as frozen dough that gets partially baked elsewhere and finished in store, which the README calls par-bake. The process the agent follows is fairly involved. It starts by creating a dedicated folder for that specific search so nothing overlaps between runs, then searches a general map data source for an unbiased list of nearby places, followed by more targeted, language aware searches to catch well known, new, renamed, or oddly labeled venues that map data might miss. Candidates are merged and deduplicated, then separate worker agents go out and gather quotes and evidence from menus, websites, reviews, and press coverage without being allowed to score anything themselves. The main agent then checks that evidence, asks for more detail if something is missing, applies the scoring rubric, and produces a final result organized by categories like standout finds and rare discoveries. A notable idea in the README is watching for what it calls involuntary tells, meaning physical details a business reveals without meaning to, such as a bakery selling out unusually early, which can contradict marketing language claiming everything is baked fresh. To use this project, you install or link the entire folder structure, not just a single file, since the skills share reference files. It is released under the MIT license.

Copy-paste prompts

Prompt 1
Run the restaurant rubric on my city to find scratch-made dining spots.
Prompt 2
Use the bakery rubric to find bakeries that avoid par-bake shortcuts near me.
Prompt 3
Explain how the involuntary tells concept helps this project detect fake scratch cooking claims.
Prompt 4
Walk me through installing scratch-food-rubrics so both skills share their reference files correctly.

Frequently asked questions

What is scratch-food-rubrics?

AI agent skills that score restaurants and bakeries on how much of their food is genuinely made from scratch.

What language is scratch-food-rubrics written in?

Mainly Python. The stack also includes Python, OpenStreetMap.

What license does scratch-food-rubrics use?

You can use, modify, and share this freely, including commercially, as long as you keep the copyright notice.

How hard is scratch-food-rubrics to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is scratch-food-rubrics for?

Mainly general.

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