Add .github/copilot-instructions.md to onboard repository for Copilot coding agent

[Copilot]

Overview

This PR adds a comprehensive .github/copilot-instructions.md file to onboard the TankControllerPython repository for use with GitHub Copilot coding agents. This one-time setup will significantly improve the quality and efficiency of AI-assisted code changes by providing essential context about the repository structure, build processes, and best practices.

What's Added

The instructions file provides agents with:

Project Context

• Purpose: Python port of TankController for Raspberry Pi, controlling an alkalinity titrator for ocean acidification research
• Scale: ~8,790 lines of Python across 102 files with 32 test files
• Tech Stack: Python 3.9-3.11, pipenv for dependency management, pytest for testing, with hardware mocking for development

Validated Build & Test Instructions

All commands have been tested and validated:

# Install dependencies (always run first)
pipenv sync -d

# Run tests
./test.sh

# Run application
./run_gui.sh      # GUI mode for development
./run.sh          # Hardware mode for Raspberry Pi

Includes documented workarounds for known issues (e.g., pipenv timeout on slow networks, hardware import errors when mocking is disabled).

Repository Structure Guide

• Detailed layout of titration/ (application code), test/ (test files), and arduino/ (stepper motor firmware)
• Key architectural patterns: state machine design, hardware abstraction layer, threading model
• Location of all configuration files (.flake8, .pylintrc, .isort.cfg, .codespellrc)
• GitHub Actions workflows (pytest, linter, spell-check) with expected durations

Development Workflow

• Linting commands (flake8, pylint, isort, codespell)
• Code style guidelines (120 char lines, snake_case, PascalCase, docstring requirements)
• Full validation sequence to run before submitting PRs
• Common pitfalls and solutions in an easy-to-reference table

Benefits

This will help coding agents:

• ✅ Reduce exploration time by providing repository context upfront
• ✅ Minimize command failures by documenting validated workflows
• ✅ Avoid common pitfalls (hardware imports, timeout issues, etc.)
• ✅ Generate code that passes CI checks on the first try
• ✅ Follow established code style and testing patterns

File Details

• Location: .github/copilot-instructions.md
• Size: 922 words (~1.8 pages) - optimized to stay under the 2-page limit
• Format: Markdown with clear sections and code examples
• Maintenance: Only needs updates when build processes or project structure changes significantly

Validation

• ✅ All commands tested and verified working
• ✅ Spell-checked with codespell
• ✅ Meets length requirement (under 2 pages)
• ✅ Not task-specific (provides general repository guidance)
• ✅ Includes trust statement encouraging agents to use instructions before searching

This is a one-time setup that will improve the quality of all future AI-assisted contributions to this repository.

Original prompt

Your task is to "onboard" this repository to Copilot coding agent by adding a .github/copilot-instructions.md file in the repository that contains information describing how a coding agent seeing it for the first time can work most efficiently.

You will do this task only one time per repository and doing a good job can SIGNIFICANTLY improve the quality of the agent's work, so take your time, think carefully, and search thoroughly before writing the instructions.

- Reduce the likelihood of a coding agent pull request getting rejected by the user due to generating code that fails the continuous integration build, fails a validation pipeline, or having misbehavior. - Minimize bash command and build failures. - Allow the agent to complete its task more quickly by minimizing the need for exploration using grep, find, str_replace_editor, and code search tools. - Instructions must be no longer than 2 pages. - Instructions must not be task specific.

Add the following high level details about the codebase to reduce the amount of searching the agent has to do to understand the codebase each time:

• A summary of what the repository does.
• High level repository information, such as the size of the repo, the type of the project, the languages, frameworks, or target runtimes in use.

Add information about how to build and validate changes so the agent does not need to search and find it each time.

• For each of bootstrap, build, test, run, lint, and any other scripted step, document the sequence of steps to take to run it successfully as well as the versions of any runtime or build tools used.
• Each command should be validated by running it to ensure that it works correctly as well as any preconditions and postconditions.
• Try cleaning the repo and environment and running commands in different orders and document errors and and misbehavior observed as well as any steps used to mitigate the problem.
• Run the tests and document the order of steps required to run the tests.
• Make a change to the codebase. Document any unexpected build issues as well as the workarounds.
• Document environment setup steps that seem optional but that you have validated are actually required.
• Document the time required for commands that failed due to timing out.
• When you find a sequence of commands that work for a particular purpose, document them in detail.
• Use language to indicate when something should always be done. For example: "always run npm install before building".
• Record any validation steps from documentation.

List key facts about the layout and architecture of the codebase to help the agent find where to make changes with minimal searching.

• A description of the major architectural elements of the project, including the relative paths to the main project files, the location
  of configuration files for linting, compilation, testing, and preferences.
• A description of the checks run prior to check in, including any GitHub workflows, continuous integration builds, or other validation pipelines.
• Document the steps so that the agent can replicate these itself.
• Any explicit validation steps that the agent can consider to have further confidence in its changes.
• Dependencies that aren't obvious from the layout or file structure.
• Finally, fill in any remaining space with detailed lists of the following, in order of priority: the list of files in the repo root, the
  contents of the README, the contents of any key source files, the list of files in the next level down of directories, giving priority to the more structurally important and snippets of code from key source files, such as the one containing the main method.

- Perform a comprehensive inventory of the codebase. Search for and view: - README.md, CONTRIBUTING.md, and all other documentation files. - Search the codebase for build steps and indications of workarounds like 'HACK', 'TODO', etc. - All scripts, particularly those pertaining to build and repo or environment setup. - All build and actions pipelines. - All project files. - All configuration and linting files. - For each file: - think: are the contents or the existence of the file information that the coding agent will need to implement, build, test, validate, or demo a code change? - If yes: - Document the command or information in detail. - Explicitly indicate which commands work and which do not and the order in which commands should be run. - Document any errors encountered as well as the steps taken to workaround them. - Document any other steps or information that the agent can use to reduce time spent exploring or trying and failing to run bash commands. - Finally, explicitly instruct the agent to trust the instructions and only perform a search if the information in the instruction...

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✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.