If you've ever felt like your AI coding assistant is just guessing what you want to build, it's time to flip the script. Code has been king for decades, with specifications often discarded once the "real work" began. But what if your specifications became executable?
Enter the ultimate developer combo: GitHub Spec Kit combined with Google Antigravity, the next-generation agentic development platform.
By using the lightweight Antigravity CLI to run autonomous coding agents right from your terminal, you can guide your Python applications through a highly structured 6-step Spec-Driven Development (SDD) process.
Here is how you can build a Python application from scratch using this powerful stack:
1️⃣ Constitution: Setting the Rules The setup: You use the /speckit.constitution command. What happens: You establish your Python project's core principles and development guidelines. Google Antigravity's agent reads this to ensure all future code follows your specific standards.
2️⃣ Specify: The "What" and "Why" The setup: You run /speckit.specify. What happens: You describe exactly what your Python app needs to do (your requirements and user stories) without worrying about the tech stack just yet.
3️⃣ Clarify: Filling the Gaps The setup: You use /speckit.clarify. What happens: Instead of guessing, the Antigravity agent asks you sequential questions to clarify vague areas before any code is planned.
4️⃣ Plan: The Blueprint The setup: You hit /speckit.plan. What happens: You define your tech stack (e.g., FastAPI, PostgreSQL). The agent creates a comprehensive technical implementation plan and architecture choices based on your specifications.
5️⃣ Task: The Roadmap The setup: You run /speckit.tasks. What happens: The agent breaks down the plan into a highly detailed, actionable task list. It even groups tasks that the Antigravity 2.0 command center can execute in parallel!
6️⃣ Implement: Bringing it to Life The setup: You execute /speckit.implement. What happens: The Antigravity CLI goes to work, autonomously executing all the tasks in the correct order to build your Python feature exactly according to the spec
Spec-Driven Development isn't just about writing code faster; it's about building high-quality software with predictable outcomes.
Here is a detailed example of how you can build a Python-based task management application, let's call it "Taskify", using the 6-step Spec-Driven Development process with GitHub Spec Kit and Google Antigravity:
Example Application: Taskify (A Python FastAPI Task Manager)
The Setup in VS Code: Open your Taskify project folder in VS Code. Open the integrated terminal and run specify init to pull in the Spec-Driven Development (SDD) templates and initialize the Google Antigravity integration. You will use the lightweight Google Antigravity CLI directly within your VS Code terminal to execute the autonomous coding agents.
1️⃣ Constitution:
Command: You run /speckit.constitution in the Antigravity CLI inside VS Code.
Action: You establish the foundational guidelines for the project. For example, you instruct the agent: "We are building a Python backend. All code must use Python 3.11+, follow PEP 8 standards, and prioritize security.".
Result: The Antigravity agent creates a .specify/memory/constitution.md file in your VS Code workspace to ensure these core rules are followed throughout the entire development lifecycle.
2️⃣ Specify:
Command: You run /speckit.specify.
Action: You describe the what and why of the application without mentioning the tech stack just yet. You prompt: "Build a task management system called Taskify where users can create, read, update, and delete tasks featuring a title, description, due date, and status."
Result: The agent drafts a comprehensive functional specification containing user stories and automatically creates a new working branch (e.g., 001-create-taskify).
3️⃣ Clarify:
Command: You run /speckit.clarify.
Action: To reduce downstream rework, the agent uses sequential, coverage-based questioning to clarify underspecified areas. It might ask in the VS Code terminal: "Should users be able to filter tasks by status?"
Result: Your answers are recorded in a Clarifications section to ensure requirements are solid before technical planning begins.
4️⃣ Plan:
Command: You run /speckit.plan.
Action: You introduce your technology choices by prompting: "We will build Taskify using Python FastAPI and PostgreSQL.".
Result: The agent generates technical implementation detail documents, architectural blueprints, and a research.md file to validate that the chosen tech stack adheres to the project constitution.
5️⃣ Task:
Command: You run /speckit.tasks.
Action: The agent breaks down the technical plan into an actionable list.
Result: It generates a tasks.md file in your workspace, organizing tasks by user story and managing dependencies. It also adds parallel execution markers ([P]) to specific tasks. This perfectly leverages Google Antigravity 2.0, which acts as a command center capable of managing multiple local agents in parallel.
6️⃣ Implement:
Command: You run /speckit.implement.
Action: The Antigravity CLI parses the tasks.md file and executes the steps in the exact correct order, respecting dependencies and the parallel markers.
Result: The agent autonomously executes shell commands, writes the Python models, builds the FastAPI routes, and creates tests directly in your VS Code workspace. By the end of this step, you have a fully functional application built exactly to your specifications.
Have you tried shifting your focus from how to code, to what to build? Let me know in the comments! 👇
