AgentLimb · Video Antigravity Zero To One Beginner Guide

Lesson approach

This companion note follows the same order as the video: understand the work problem first, prepare the files and rules, run the prompts in sequence, then verify the output before treating it as usable.

The Chinese source lesson covers 一、新手初识：为什么在 2026 年首选谷歌 Antigravity？, 二、核心利器：Antigravity 高频内建指令（Commands）详解, 三、独家省钱秘笈：「徒弟问师父」双模型协同架构, 四、自动化关键桥梁：自研开源浏览器插件 AgentLimb, 五、实战演示：30分钟全自动手搓网页版「三国杀」卡牌游戏 MVP, 六、开箱即用：【徒弟端 Prompt】本地小模型感知与提问模板（填入 Antigravity）, 七、开箱即用：【师父端 Prompt】网页版 Gemini 4 专家角色设定（填入网页端）, 八、认知复盘：做真实项目，不要为了学 AI 而学 AI. It is designed as a practical workflow note, not a detached summary: the reader should be able to pause the video, complete one step, and return to the next step with evidence of what changed.

How to study with the video

On the first pass, watch for the inputs, constraints, and expected outputs. On the second pass, reproduce the workflow in a separate test folder. Keep source files unchanged and save generated reports, cleaned data, drafts, or exports in the designated output location.

Do not treat an AI response as a final decision. Check whether the task was fully specified, whether uncertain values were labelled, whether the result can be traced back to source material, and whether a human approval is needed before any submission, purchase, application, or external action.

Copy-ready source prompts

The original prompts are kept below in Chinese so that the operational meaning used in the video is not weakened by translation. Replace every sample file name, person, company, amount, rule, and destination with verified information from your own task.

你现在的身份是「开发执行徒弟」。
你的任务是负责本地代码的扫描、环境感知与落地执行，但你遇到以下情况时绝对不要自己凭空猜测编写复杂架构：
1. 核心业务架构设计；
2. 复杂的算法推导或多状态机流转；
3. 游戏规则判定与数值平衡。

当你遇到上述复杂问题时，请严格执行以下动作：
1. 整理当前本地文件的关键结构与痛点；
2. 提炼成一份条理清晰、没有歧义的「求教清单」；
3. 通过 AgentLimb 浏览器工具发送给网页端的「架构师父（Gemini 4）」；
4. 获取师父的明确方案与代码片段后，严格按其指导进行本地代码写入与测试，绝不擅自魔改。

你现在的身份是「首席架构师与资深导师（师父）」。
你拥有顶尖的系统架构能力、严密的逻辑推导力与丰富的全栈开发经验。
你的徒弟（本地小模型）会向你请教实际项目中的疑难架构与核心代码逻辑。

请按如下标准向徒弟传授方案：
1. 逻辑必须极其严密，边界条件考虑周全，避免留坑；
2. 给出明确的数据结构定义、函数签名与关键业务代码实现；
3. 将任务拆解为可被按部就班执行的步骤（Step-by-step），语言简练精准，方便小模型无缝解析执行。

Knowledge points and checks

The transferable skill is not a single prompt. It is the ability to turn scattered materials into a repeatable system: inputs have fixed locations, rules are written down, intermediate work is separated from final deliverables, and exceptions are visible instead of silently invented.

Before closing the task, verify four things: the input set is complete; unknown information is marked rather than guessed; the output is saved where the workflow expects it; and a small sample can be checked against the original files. High-impact decisions always remain subject to human review.

Wrap-up

Reuse the structure rather than copying the example blindly: define the objective and boundary, organize files and rules, run the steps in order, verify the result, and keep the workflow for the next similar task.

That is the central lesson behind this WorkBuddy series: AI becomes more reliable when work is expressed as a clear, inspectable process instead of a vague request for a finished answer.