Chapter 10: Spec-Driven Workflow

The latest EggHatch-AI repo does not only add features. It also adds a spec-driven development scaffold so future changes can be planned and reviewed more deliberately.

This shows up in the repository as:

• .agents/skills/
• .specify/
• AGENTS.md
• a project-specific constitution

Why Add This To A Small Repo?

Even a compact AI prototype can drift quickly:

• a demo starts pretending to be production
• scope expands without clear boundaries
• agent behavior gets more complex without a shared rationale
• UI improvements can hide weak data assumptions

The spec-driven scaffold helps slow that drift down.

The Core Idea

Instead of jumping straight into code, the project now has a default path for feature work:

flowchart LR
    Specify["$speckit-specify"] --> Clarify["$speckit-clarify"]
    Clarify --> Plan["$speckit-plan"]
    Plan --> Tasks["$speckit-tasks"]
    Tasks --> Analyze["$speckit-analyze"]
    Analyze --> Implement["$speckit-implement"]

This is especially useful for a repo like EggHatch-AI because the biggest risk is not syntax or tooling failure. The biggest risk is scope confusion.

The Constitution

The most important project-specific file in the new scaffold is:

.specify/memory/constitution.md

That file establishes rules such as:

• prototype honesty over product theater
• explainable agent workflows over black-box answers
• local-first defaults with graceful degradation
• explicit source integrity and freshness limits
• keep the system small, testable, and worth reading

Those principles fit EggHatch-AI well because they keep the project honest about what it is.

What The Skills Do

Each folder in .agents/skills/ contains one SKILL.md file.

The most important ones are:

• speckit-specify — define the feature
• speckit-plan — decide how to implement it
• speckit-tasks — break work into steps
• speckit-implement — execute
• speckit-analyze — check for drift or inconsistency

A Real Example In This Repo

The explainable comparison feature is a good example of why this workflow helps.

The feature can be framed in a structured way:

1. Define the user-facing need: compare 2-3 laptops clearly
2. Plan the implementation boundary: comparison helper, trend analysis, UI rendering, tests
3. Break it into tasks
4. Implement without losing the repository’s prototype constraints

That is exactly the kind of discipline a growing open-source AI repo benefits from.