VibeCodingBreadcrumbDemo

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VibeCodingBreadcrumbDemo

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This repository serves as an example showcasing a structured approach for collaborating with AI agents (like GitHub Copilot Chat or similar LLM-based assistants) during software development.

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# Structured workflows for coding with AI agents using the Breadcrumb Protocol

The Breadcrumb Protocol is a structured workflow for collaborating with AI agents during software development. It centres around creating and maintaining shared documentation files ("breadcrumbs") that track each task's progress and serve as a single source of truth.

This protocol creates a traceable history of decisions and implementations, making code reviews easier and ensuring both parties maintain shared context throughout development tasks.

The Breadcrumb Protocol implements these themes through a simple yet powerful concept: a shared scratch pad that allows both the developer and AI to align their vision at all times. Each development task gets its own “breadcrumb” file - a single source of truth that tracks progress from requirements through implementation.

You can read more about it here <https://dasith.me/2025/04/02/vibe-coding-breadcrumbs/>.

## Overview

This repository serves as an example showcasing a structured approach for collaborating with AI agents (like GitHub Copilot Chat or similar LLM-based assistants) during software development. It provides a specific set of instructions, defined within this project, that guide the AI agent's behaviour to ensure clarity, maintain context, and produce well-documented changes.

The core idea is to establish a predictable and transparent workflow where the AI agent acts as a diligent collaborator, leveraging project-specific knowledge and adhering to a defined process for planning, implementation, and documentation.

1. **Structured Planning & Task Management:** Breaking complex goals into well-defined phases and actionable tasks with clear success criteria. This approach provides AI with clear, manageable units of work, reducing ambiguity and allowing it to focus its generation capabilities effectively.

2. **Centralized & Accessible Knowledge Context:** Establishing designated locations with consistent naming conventions for project-related information, including domain knowledge and specifications. This makes it easier for the AI to access and utilize the “ground truth” of your project.

3. **Living Documentation & Shared Understanding:** Maintaining a dynamic, collaborative record of the development process that acts as an external, persistent memory for both the developer and the AI assistant.

## Development Workflow

![Development workflow](https://raw.githubusercontent.com/dasiths/VibeCodingBreadcrumbDemo/main/image.png)

[*Assumes spec is already created...*]
1. Prompt the agent: *[Instructions file has details on domain knowledge and specs]*
    ```text
    Help me create a aspnet api project according to the spec. I don't need the database context just yet so we can return a hardcoded response from the request processor.

Location: src/backend/
    Solution name: CarRental
    Project name: CarRental.Api

Use dotnet 9. Use this document on instructions of how to add swagger/openapi endpoint. https://devblogs.microsoft.com/dotnet/dotnet9-openapi/
    ```
2. Follow conversation with agent.
    - Provide input, confirm plans.
    - If the agent in veering off path, pause, give it instructions and ask to update the breadcrumb with the new context before continuing.
    - You also have the opportunity to update breadcrumb yourself and start a new conversation with the agent and continue from where it left off.
3. Results in breadcrumb: [2025-04-13-1723-car-rental-api-setup.md](.github/.copilot/breadcrumbs/2025-04-13-1723-car-rental-api-setup.md)

### Demo 
[![Demo](http://img.youtube.com/vi/etYG-6-9Mlk/0.jpg)](http://www.youtube.com/watch?v=etYG-6-9Mlk "Example")

## How It Works: Agent Instructions

The AI agent operating within this repository is expected to follow a specific set of rules and protocols defined in its prompt (often configured within the AI tool's settings or provided at the start of an interaction).

See the [full prompt](.github/copilot-instructions.md) for more details.

Key aspects of these instructions include:

1.  **Domain Knowledge Integration:**
    * The agent uses files within `.github/.copilot/domain_knowledge` as the authoritative source for understanding the project's context, entities, workflows, and language.
    * It is expected to update these files as the understanding of the domain evolves during development.

2.  **Specification Adherence:**
    * The agent refers to specification files located in `.github/.copilot/specifications` to guide implementation.
    * It uses a defined template (`.github/.copilot/specifications/.template.md`) for structure and clarifies which specifications are relevant if needed.

3.  **The Breadcrumb Protocol:** This is the central pillar of the collaborative process:
    * **Purpose:** To create a shared "scratchpad" or log (a "breadcrumb" file) for each task, ensuring alignment between the user and the agent, and documenting the development journey.
    * **Creation:** For each new task, a breadcrumb file is created in `.github/.copilot/breadcrumbs` with the format `yyyy-mm-dd-HHMM-{title}.md`.
    * **Structure:** Each breadcrumb file includes mandatory sections: Requirements, User Comments, Plan, Decisions, Implementation Details, Changes Made, Before/After Comparison, and References.
    * **Workflow:**
        * Updates happen *before* coding changes.
        * The implementation **Plan** requires explicit user approval before starting work.
        * The breadcrumb is updated *after* each significant change.
        * It serves as the single source of truth for the task's context and progress.
    * **Planning:** Plans are structured into phases and tasks, include checklists, reference domain knowledge/specs, prioritize tests, and have clear success criteria.
    * **Execution:** The agent follows the approved plan, marks tasks as complete *after* successful implementation (including passing tests), and updates the breadcrumb accordingly.

## Repository Structure
`.github/copilot-instructions.md`: contains instruction for the agent.

`.github/.copilot/` directory is crucial for this workflow:
* `domain_knowledge/`: Contains files defining the project's context and rules.
* `specifications/`: Holds requirement specifications, organized by feature or area.
    * `.template.md`: Template for new specification files.
* `breadcrumbs/`: Stores the collaborative log files created for each task according to the Breadcrumb Protocol.

Example
```text
.github/.copilot/
├── breadcrumbs/
│   ├── 2025-04-13-0130-car-rental-entity-model.md
│   ├── 2025-04-13-0135-aspnet-core-api-specification.md
│   └── 2025-04-13-1723-car-rental-api-setup.md
│
├── domain_knowledge/
│   └── entities/
│       └── car-rental-entities.md
│
└── specifications/
    ├── application_architecture/
    │   └── aspnet-core-minimal-api.spec.md
    └── .template.md
```

## Goal

By enforcing these instructions, this repository demonstrates a method to:

* Improve the reliability and predictability of AI agent contributions.
* Maintain a clear, documented trail of requirements, decisions, and implementation steps.
* Facilitate easier review of changes by linking code modifications back to the documented plan and reasoning within the breadcrumb files.
* Ensure the agent consistently leverages project-specific context (domain knowledge and specifications).

This setup provides a template and example for teams looking to integrate AI agents more deeply and effectively into their development workflows.

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