Receiving a task and then staring at the screen for an hour not knowing where to start is something that happens to Antigravity users no less than regular workers. The problem isn't that you're incompetent or lazy, but that your brain doesn't fear difficult tasks; it fears unclear ones. And when you give AI a vague request, the results Antigravity produces will be equally vague.
Why does delegating tasks to Antigravity still yield poor results?
Antigravity is a true agent because it can plan, write code, execute commands, and self-verify results. But this is precisely why many people are disappointed on their first use: they immediately assign Antigravity a huge and vague task, and the agent runs for 30 minutes in the wrong direction, exhausting the quota with unusable results.
Cognitive scientists call the state of freezing before a large task "cognitive overload." The brain doesn't know where to start processing, so it chooses the safest option: doing nothing, and the familiar loop looks like this:
Brain fears making mistakes → freezes
Cannot start → deadline approaches
Becomes more fearful → freezes again
With Antigravity, user cognitive overload directly leads to poor prompts, and poor prompts cause the agent to run in the wrong direction. This loop, of course, consumes more tokens and time than any technical error.
There are three approaches to break that loop, depending on how well you understand the requirements and how much you've established the process.
Three Effective Approaches to Working with Antigravity
Method 1: Download Source Code from Experienced Users
This is the fastest way to get started without spending time setting up from scratch, especially suitable when you don't yet know what your process should look like. Antigravity works best when it has sufficient project context, meaning it can see the rules, workflows, skills, and memory directories that record old knowledge. Instead of building everything yourself, you copy the source code from someone who has fully set it up, download it, and let the agent read the entire existing configuration, provided, of course, that person has agreed or made it public.
Note: Many people have exploited this to spread malware, so only install source code officially from Anthropic, Google, xAI, OpenAI,... or reputable individuals.
When you copy the code repository from someone who has fully set it up, download it, and let the agent read the entire existing configuration, you gain two benefits simultaneously:
The agent immediately understands the writing style for skills, workflows, technical foundations, and project rules from day one without you needing to re-explain.
You learn how experienced individuals set up processes — from organizing memory directories to writing rules for the agent — without having to figure it out from scratch.
However, if you don't understand the author's intentions, you won't be able to fully utilize the functions of this source code, much like wearing an oversized shirt.
Method 2: Solve Small Steps Yourself Before Delegating Large Tasks
This is the most quota-saving method and also a lesson I learned after many instances of waste due to delegating overly large tasks from the start. The 4C framework — Clarify, Chunk, Consult, Commit — originally used for human task management, is extremely effective when applied to Antigravity for a simple reason: the clearer you are before delegating, the less the agent has to guess.
Clarify Step: Before typing anything into Antigravity, answer these 4 questions yourself:
What does the final result look like?
Who will use this?
What is the actual deadline?
What constitutes successful completion of this task?
Five minutes spent answering will completely change the quality of your command. Instead of "build me a login system," you'll be able to write "build a login system using Google OAuth for a Next.js application, save the session to Firestore, redirect to the main page after successful login, run it locally, and take a screenshot for me to review."
Chunk Step: Based on the Zeigarnik effect, once you start even a small step, your brain automatically wants to complete the subsequent steps. Ask the agent "break the task into the smallest steps to begin?" and go through each step. Allocate a specific amount of time to understand the structure and check if the agent correctly understands the requirements before letting it run a large task. But remember to only allocate a specific amount of time, because many problems only truly emerge during execution, and that's when we find solutions. In this step, we can immediately use Fast Mode for the agent to execute without needing to create a framework or deep thinking, or even if there's nothing special, Gemini Flash can perfectly handle this part, saving significant tokens for Gemini Pro and Claude Opus.
Consult Step: Don't make it hard on yourself when others have gone before you. Similar to Method 1 of downloading others' source code, this step involves actively finding and reading how they approach problems, how they break down tasks, how they write commands, and how they set up processes, then distilling suitable methods to apply to your own work. You don't need to copy verbatim; just learn from their thought structure. This is especially valuable for tasks you've never delegated to an agent before, as those who have done it often discover common pitfalls you might not be aware of.
Commit Step: Instead of trying to plan the entire task perfectly before starting, commit just the first 10 to 15 minutes to understanding it. Ask the agent a small question, see how it responds, and always add the prompt: “If the problem is unclear, you can always ask again; do not make arbitrary decisions.” There will certainly be shortcomings, but we will feel that we have come a long way with Antigravity and the task, instead of spending hours writing perfect prompts without accomplishing anything, which would surely be very boring.
Method 3: Delegate Large Tasks Immediately When a Process is Already Established
This method only works when you have gone through the previous two methods — having clear processes, contextual memory skills, and the agent being familiar with the rules and workflows. This can be considered the Commit step in the 4C framework: instead of worrying about the entire task, you need to guide the agent towards a specific outcome and let the agent handle the rest.
At this point, Plan Mode is a better choice than Fast Mode because the agent must create a detailed execution plan before performing the task, allowing you to review that plan and leave notes for adjustments before letting the agent run.
This method combines the agent's speed with your strategic vision because the process is already in place, so the clarification step should be integrated into the rules, workflows, and skills, eliminating the need for you to re-explain the context each time. This is especially a favorite method for Pros who use Claude for excellent planning and then feed it to GLM for task execution to save tokens.
Which Method Should We Choose for Our Work?
These three methods used with Antigravity are not mutually exclusive but are ordered from less to more context:
Vague tasks, don't know where to start: Copy others' source code or use the 4C framework to clarify first.
Understood but large and complex tasks: Go through small steps, use Flash for simple steps, and reserve Pro for steps requiring deep thought.
Tasks with clear processes: Delegate directly with Plan Mode, letting the agent handle it while you work on other things.
The common thread among all three methods is that you must do one thing before opening Antigravity: think. Not long thinking — just 5 to 10 minutes to clarify the requirements before delegating to the agent. That amount of time saves more quota than any other prompt optimization technique.
