Keep implementation small by checking whether work is needed, then considering existing code, standard libraries, platform features, and installed dependencies. Helps avoid unnecessary packages, helper classes, and boilerplate before writing new code.
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Use when the user asks to write code efficiently, avoid over-engineering, reduce dependencies, or prevent unnecessary abstractions. Enforces a strict efficiency ladder: YAGNI, reuse, stdlib, native platform, existing deps — before writing any new code.
安装前可以查看交给 AI 代理的指令内容。
You are highly efficient. The best code is the code never written.
Use this skill whenever the goal is to solve a problem with the least code possible. It prevents common AI failure modes: inventing helper classes for single-use logic, installing packages for one-line operations, and producing boilerplate that the user will never need.
Before writing any new code, stop at the first rung that holds:
When asked to implement something:
pathlib instead of a custom file helper."| Anti-Pattern | What to do instead |
|---|---|
| Installing a package for a one-liner | Use the standard library |
| Writing a class for a single function | Write the function |
| Adding a config file for a single hardcoded value | Hardcode it until there are 2+ uses |
| Creating a utility module before it's reused anywhere | Write inline, extract later |
| Adding docstrings/comments the user didn't ask for | Skip them |
| Building error handling for errors that can't happen | Skip it |
| Adding logging before the code works | Ship the code first |
engineering/strict-api — prevents hallucinated APIs when writing minimal code; use together.engineering/zero-hallucination-coder — enforces verified-only API usage.engineering/karpathy-coder — Karpathy-inspired behavioral guidelines for LLM-assisted coding.还没有评价。欢迎分享你的使用感受。
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Audit web applications for accessibility issues against WCAG 2.2 Level A and AA, the web accessibility standards. Get framework-specific code fixes, contrast checks, verification steps, and reports, with options for automated checks in development pipelines.
中文简介正在准备中。当前显示英语简介。
Plan A/B tests that compare page variations, from headlines to pricing layouts. Define a hypothesis, choose success metrics, calculate sample size and duration, and prepare launch checks and results reports to support decisions.
中文简介正在准备中。当前显示英语简介。
Create paid ad headlines and body copy tailored to each platform's character limits. Use existing performance data to develop variations, explore new messaging angles, and check copy for length, quality, and potential rejection risks.
中文简介正在准备中。当前显示英语简介。
Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
中文简介正在准备中。当前显示英语简介。
Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.
中文简介正在准备中。当前显示英语简介。
Create signed records for consequential agent actions such as deployments, deletions, and payments. Decide which actions need evidence, record the agent, action, target, and policy, then verify the resulting certificate offline to detect later changes.
中文简介正在准备中。当前显示英语简介。