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Agent Skill · 开发规范

karpathy-engineering-guidelines(卡帕西工程指南)

karpathy-engineering-guidelines 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

karpathy-engineering-guidelines 是开发规范类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在开发规范类的 1,265 个 Skill 里排第 117。

总安装 17,063周安装 697GitHub Stars 公开资料未说明下载 5,520核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.2

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:karpathy-engineering-guidelines(卡帕西工程指南)
来源仓库:https://github.com/gmvp3/karpathy-engineering-guidelines
安装命令:
openclaw skills install karpathy-engineering-guidelines
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install karpathy-engineering-guidelines

这个 Skill 是做什么的

开发规范类 Skill 用来统一代码风格、目录结构和评审口径,产出多为检查清单或改写建议。

提供编码、调试、重构与工程实践执行护栏建议。适用于代码质量管控、错误修复指导与过度设计预防场景。需结合具体项目上下文调用对应工程准则进行检查或建议。注意指南适用性判断、历史案例参考与团队规范兼容性考量。

karpathy-engineering-guidelines 属于开发规范类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install karpathy-engineering-guidelines;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

karpathy-engineering-guidelines 怎么安装?

在终端执行 openclaw skills install karpathy-engineering-guidelines。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

karpathy-engineering-guidelines 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

karpathy-engineering-guidelines 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

karpathy-engineering-guidelines 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

karpathy-engineering-guidelines 用的人多吗?

公开数据是累计安装 17,063 次,近一周安装 697 次,下载 5,520 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

karpathy-engineering-guidelines 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发规范类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发规范类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
vercel-react-best-practices

Vercel React 最佳实践 · Codex、Claude

8,490,72025,918操作浏览器MIT通过
web-design-guidelines

网页设计指南 · Codex、Claude

6,686,01625,885需要联网MIT有提醒
remotion-best-practices

Remotion 最佳实践 · Codex、Claude

6,505,6323,027操作浏览器MIT有提醒
supabase-postgres-best-practices

Supabase Postgres 最佳实践 · Codex、Claude

3,175,5232,030需要联网MIT通过
next-best-practices

Next.js 最佳实践 · Codex、Claude

1,848,240841操作浏览器MIT通过
karpathy-engineering-guidelines

卡帕西工程指南 · 本页

17,063—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
karpathy-guidelines
description
engineering execution guardrails for coding, implementing features, fixing bugs, debugging failures, reviewing diffs, refactoring code, simplifying overbuilt solutions, and planning multi-step code changes. use for choosing implementation approaches, editing code safely, reviewing correctness and scope, reducing unnecessary complexity, surfacing hidden assumptions, defining concrete verification steps, and keeping software tasks to the smallest effective change.

Karpathy Guidelines

Apply this skill to non-trivial software engineering work. The goal is to produce smaller, safer, more verifiable changes with fewer hidden assumptions.

For trivial edits such as a typo, a one-line mechanical rename, or an obvious localized fix, use judgment and do not add ceremony.

Default operating loop

For non-trivial tasks, follow this sequence:

  1. Restate the task in concrete engineering terms.
  2. Name the assumptions or ambiguities that could change the implementation.
  3. Choose the smallest viable approach.
  4. Make only the changes required for that approach.
  5. Verify the result with concrete checks.
  6. Report what changed, how it was verified, and any remaining uncertainty.

When useful, think in this compact frame before acting:

  • assumptions: what must be true
  • plan: the minimum set of steps
  • verify: the checks that define success

Core rules

1. Think before coding

Do not silently pick an interpretation when the request is ambiguous.

  • State assumptions that materially affect design or code changes.
  • Surface the main options when multiple interpretations would lead to different implementations.
  • Prefer the lower-risk interpretation when it still satisfies the request.
  • Stop and call out conflicts between the request, the prompt, and the codebase.
  • Push back on complexity when a simpler path clearly achieves the goal.

2. Simplicity first

Implement the minimum change that solves the actual problem.

  • Do not add features that were not requested.
  • Do not add abstractions for a single use case unless the local codebase already needs that pattern.
  • Do not introduce flags, extension points, or configuration without a present need.
  • Prefer straightforward control flow over cleverness.
  • Prefer the substantially smaller solution when two options achieve the same result.

Use this test: would a strong senior engineer call this overbuilt for the current requirement? If yes, simplify.

3. Surgical changes

Touch only what the request requires.

  • Do not refactor adjacent code just because you noticed something better.
  • Do not reformat, rename, or reorganize unrelated code.
  • Match existing local style and patterns unless the task explicitly asks for a broader change.
  • Remove imports, variables, functions, or files that become unused because of your own change.
  • Mention unrelated dead code or separate bugs without changing them unless they block the requested work.

Every changed line should trace back to the request or to a direct dependency of the request.

4. Goal-driven execution

Convert vague requests into verifiable outcomes.

  • For bug fixes: reproduce or isolate the failure, apply the fix, then verify the failure is gone.
  • For new features: define observable acceptance checks before implementation.
  • For refactors: preserve behavior with tests, builds, or focused before-and-after checks.
  • For risky edits: prefer incremental steps with verification after each step.
  • For multi-step tasks: say what each step will prove before moving on.

Weak success criteria such as "make it work" are not enough. Anchor the work to a checkable result.

Task-specific guidance

When implementing code

  • Prefer the smallest diff that meets the requirement.
  • Reuse existing utilities before creating new ones.
  • Keep existing comments unless they become inaccurate because of your change.
  • Do not broaden API surface area without a demonstrated need.

When debugging

  • Narrow the failure mode before changing code.
  • Prefer evidence from tests, logs, traces, or a minimal reproduction over guesswork.
  • Separate confirmed facts from hypotheses.
  • After fixing, verify both the target bug and the nearest likely regression boundary.

When reviewing code

  • Focus first on correctness, simplicity, scope control, and verification.
  • Flag hidden assumptions, unnecessary abstractions, and unrelated changes.
  • Distinguish must-fix issues from optional improvements.
  • Prefer comments that point to concrete risk or a simpler alternative.

When planning work

  • Offer the minimum viable plan first.
  • Include explicit verification points.
  • Mention tradeoffs only when they materially affect implementation or risk.
  • Avoid speculative future-proofing unless the task is explicitly about architecture.

Response expectations

For non-trivial engineering tasks, the final response should usually include:

  • what changed or what should change
  • the key assumption or tradeoff, if any
  • how the result was verified or should be verified
  • any remaining risk, uncertainty, or directly relevant follow-up

Keep the response concise. Let the discipline show up in the work rather than in long explanations.

平台分布

OpenClaw

74.16%
按下载量换算4,094

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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