hightower/hightower-first-assistant icon
public
Published on 6/5/2025
My First Assistant

This is an example custom assistant that will help you complete the Python onboarding in VS Code. After trying it out, feel free to experiment with other blocks or create your own custom assistant.

Rules
Prompts
Models
Context
relace Relace Instant Apply model icon

Relace Instant Apply

relace

40kinput·32koutput
anthropic Claude 3.7 Sonnet model icon

Claude 3.7 Sonnet

anthropic

200kinput·8.192koutput
anthropic Claude 3.5 Haiku model icon

Claude 3.5 Haiku

anthropic

200kinput·8.192koutput
mistral Codestral model icon

Codestral

mistral

voyage Voyage AI rerank-2 model icon

Voyage AI rerank-2

voyage

voyage voyage-code-3 model icon

voyage-code-3

voyage

Build & Development Commands
- Build Automation:
- Provide a unified build script (e.g., using Unreal Build Tool and dedicated batch or shell scripts) that encapsulates all build commands.
- Sample commands might include:
- build:debug – Perform a debugging build with full symbols and logging.
- build:release – Compile an optimized final version.
- build:ci – A script specifically targeted for the continuous integration environment.
- Environment Setup:
- Ensure that environment variables are configured properly for locating UE5.6 binaries and required SDKs.
- Include a pre-build step to verify the installation of all dependencies.
- Command Documentation:
- Maintain a clear README or developer guide outlining each build command, expected input parameters, and outputs to help onboard new developers.

Testing Guidelines
- Unit Testing:
- Write unit tests for individual AI logic components. Focus on core decision-making functions, edge-case inputs, and error handling.
- Integration Testing:
- Develop tests that simulate in-engine behavior. For instance, validate that the AI behaves correctly in various simulated game scenarios through Unreal’s automated testing framework.
- End-to-End (E2E) Testing:
- Include scenarios that mirror real-world use. For example, test AI modules interacting with game physics, rendering, and user input.
- Continuous Testing & CI:
- Automate running all tests in your CI pipeline. Ensure that every commit triggers the test suite so that potential issues are detected early.
- Logging & Metrics Collection:
- Integrate detailed logging and assertions in your test cases. This makes failures easier to diagnose, particularly in complex AI decision trees.
- Code Coverage:
- Aim for a coverage goal (e.g., over 80% in critical areas) and document the methodology used for measuring testing metrics.

Code Style & Guidelines
- General Formatting:
- Indentation: Use 4 spaces for indentation and maintain consistency across all files.
- Brace Style: Choose one brace style (such as Allman or K&R) and enforce it with code formatting tools.
- Naming Conventions:
- Classes: Use PascalCase (e.g., EnemyAIController).
- Functions/Methods: Utilize camelCase (e.g., calculatePath()).
- Variables: Use descriptive names with camelCase; prefix pointers/members with a consistent notation if needed.
- Modular Code:
- Ensure clear separation of concerns. AI logic should be decoupled from UI, input handling, and core game mechanics.
- Code Documentation:
- Utilize inline comments and header comments to describe the purpose of classes, functions, and nontrivial algorithms.
- Maintain a standards file or section in the README that outlines expectations for code quality.
- Static Analysis:
- Integrate static analysis tools (or Unreal’s built-in checkers) into your pipeline to catch style and potential error issues early.
- Commit Standards:
- Use consistent commit message conventions that reference issue numbers, brief descriptions of changes, and potential impacts.

Documentation Guidelines
- Centralized Documentation Repository:
- Create and maintain a dedicated documentation folder (e.g., /docs) within the repository for all project-related documents.
- README and Quick-Start Guides:
- Provide a comprehensive README that covers the overall architecture, setup instructions, and a quick-start guide for new developers.
- API Documentation:
- Include in-line documentation (preferably using Doxygen-style comments) so that API references can be automatically generated.
- Process & Architecture Diagrams:
- Use diagrams (such as UML or flowcharts) to visually describe the AI’s architecture. This is invaluable for onboarding and maintenance.
- Change Logs:
- Maintain version-specific change logs documenting updates to the AI system. This makes it easier to track progress and understand the evolution of the codebase.
- FAQ & Troubleshooting:
- Develop a troubleshooting guide that covers common pitfalls and how to resolve them—this is beneficial in a continuous integration context where bugs can arise from environment differences

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Prompts

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Write Cargo test
Write unit test with Cargo
Use Cargo to write a comprehensive suite of unit tests for this function

Context

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@code
Reference specific functions or classes from throughout your project
@docs
Reference the contents from any documentation site
@diff
Reference all of the changes you've made to your current branch
@terminal
Reference the last command you ran in your IDE's terminal and its output
@problems
Get Problems from the current file
@folder
Uses the same retrieval mechanism as @Codebase, but only on a single folder
@codebase
Reference the most relevant snippets from your codebase

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