gf-agent-contributor

name	gf-agent-contributor
description	Guide for creating or contributing modular knowledge to the gf_agent skill. Use this when a user wants to teach gf_agent a new trick or document an Apps Script parity oddity.

Skill: gf-agent-contributor

Overview

This skill provides instructions on how to correctly contribute knowledge, best practices, or API parity warnings to the gf_agent so that it learns how to write better Google Apps Script automation code.

The Architecture

To prevent Git merge conflicts between multiple developers, the monolithic gf_agent/SKILL.md file is auto-generated. You MUST NOT edit gf_agent/SKILL.md directly.

Instead, gf_agent uses a modular knowledge base. The builder.js script aggregates all markdown files in the gf_agent/knowledge/ directory and appends them to a base template (gf_agent/scripts/SKILL.template.md).

How to Contribute Knowledge

When you need to teach gf_agent something new, you must first ensure you are working from a local Git clone, NOT the cached Gemini skills folder.

1. Clone the gf_agent Environment (Sparse Checkout): Because gf_agent is a subfolder of the larger gas-fakes repository, you should use Git's Sparse Checkout feature to download only the gf_agent directory.

   🤖 Agent Instruction: If the user is not yet in a configured workspace, offer to run the sparse checkout setup for them! If they agree, use run_shell_command to execute this exact sequence (adjust the parent directory if they specify one):

   mkdir gf_agent_standalone && cd gf_agent_standalone
   git init
   git remote add origin https://github.com/brucemcpherson/gas-fakes.git
   git config core.sparseCheckout true
   echo "gf_agent/*" >> .git/info/sparse-checkout
   git pull origin main
   gemini skills link ./gf_agent
   

   (If the user prefers to do it manually, provide them the script above).

2. Create a Modular File: Navigate to the gf_agent/knowledge/ directory inside your new standalone environment. Create a new markdown file for your topic.

   • Use a numeric prefix to ensure it sorts correctly during compilation if order matters (e.g., 06-new-feature.md). If it is a standalone topic, any descriptive name is fine.

3. Draft the Knowledge: Write your instructions clearly. Use Markdown headers (###). Keep it concise and actionable for an AI agent. Good Example: "When creating a Foo, you MUST always set the Bar property, otherwise the API will crash with error X."

4. Testing Your Contributions (Optional but Recommended): If you want to verify that your new knowledge correctly guides the agent before you submit your PR:

   • Run the builder script from the root of your standalone directory (or the gas-fakes root) to compile your change into the monolithic SKILL.md:

     node gf_agent/scripts/builder.js
     

   • Since you have already "linked" the skill in step 1, the Gemini CLI will immediately start using your updated SKILL.md. You can now test the behavior by asking the agent a question related to your new knowledge.

5. Submit Your Knowledge: Once satisfied, you do not need to include the updated monolithic SKILL.md in your commit (the repo maintainers will handle the final build). Simply stage your new gf_agent/knowledge/XX-my-topic.md file, commit it, and push it to your fork.

6. The Merge Process: Submit a Pull Request with your new file. Because you only added a distinct file to a directory, your pull request will not cause merge conflicts. Once your PR is merged, the repository maintainers will run the root-level documentation pipeline (npm run docs) which will automatically compile your new knowledge into the master gf_agent/SKILL.md for all users!