Token导航 LogoToken导航

Agent Skill · 效率

universal-code-converter(通用代码转换器)

universal-code-converter 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。

universal-code-converter 是效率类 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,按安装量在效率类的 21,003 个 Skill 里排第 8,606。

总安装 6,908周安装 285GitHub Stars 公开资料未说明下载 2,257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
1.0.0

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:universal-code-converter(通用代码转换器)
来源仓库:https://github.com/syuaibsyuaib/universal-code-converter
安装命令:
openclaw skills install universal-code-converter
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 universal-code-converter

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

实现源到源代码转换管道,支持多语言间代码迁移。适用于构建或评估跨编程语言的项目时调用。可通过 clawhub 安装,具体用法参考其 GitHub 仓库。使用时应注意目标语言的语法规范与依赖关系。universal-code-converter 属于效率类 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 universal-code-converter;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

universal-code-converter 怎么安装?

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

universal-code-converter 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。

universal-code-converter 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。

universal-code-converter 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。

universal-code-converter 用的人多吗?

公开数据是累计安装 6,908 次,近一周安装 285 次,下载 2,257 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。

universal-code-converter 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。
  • 需要处理效率类任务、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。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
universal-code-converter

通用代码转换器 · 本页

6,908—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
universal-code-converter
description
Design, review, or implement source-to-source code translation pipelines that convert or port code between programming languages. Use when building or evaluating a transpiler, code-porting or migration scaffold, tree-sitter-based parser pipeline, intermediate representation (IR), lowering rules, semantic-gap handling, or validation strategy for multi-language code conversion.

Universal Code Converter

Build or refine the converter as a staged translation pipeline. Optimize for semantic fidelity, diagnostics, and incremental delivery instead of "convert everything" claims.

Run This Workflow

  1. Clarify the scope.

- Capture the source language, target language, runtime constraints, supported constructs, and quality bar. - Ask for or extract at least 3 representative snippets before designing the pipeline. - Define what "done" means: compilable output, behavior-preserving output, migration scaffold, or partial assisted conversion.

  1. Verify the parser frontend.

- Confirm the required Tree-sitter grammars exist and are actively usable. - Prefer official prebuilt bindings or grammar packages for the first frontend probe. Compile grammars manually only when official artifacts are missing or the task needs grammar changes. - Inspect actual node types and field names before hardcoding visitors. - Capture one reusable parser-probe artifact from real fixtures: root node type, critical field names, and one query/capture example. Keep it in tests or fixtures so later work does not repeat manual spelunking. - If Tree-sitter is part of the plan, run one incremental reparse check with old_tree to prove the grammar is usable as an editing frontend. Treat this as a capability check, not a brittle microbenchmark. - Treat Tree-sitter as a concrete syntax tree frontend with incremental parsing, not as a full semantic compiler.

  1. Separate the pipeline into explicit passes.

- Use this default flow:

parse -> normalize -> semantic enrichment -> IR -> lowering -> emit -> validate

- Keep normalization separate from semantic analysis. - Keep shared IR logic separate from pair-specific lowering rules. - Keep code formatting separate from translation logic.

  1. Design the IR around semantics, not surface syntax.

- Model declarations, scopes, bindings, literals, calls, control flow, imports, types, and diagnostics. - Represent lossy or unsupported constructs explicitly with diagnostic nodes or status flags. - Remove syntax sugar early when it simplifies downstream lowering.

  1. Implement a narrow vertical slice first.

- Start with one source-target pair. - Start with modules, functions, parameters, literals, identifiers, returns, calls, and simple conditionals. - Add tests for each slice before adding more syntax.

  1. Handle semantic gaps intentionally.

- Classify every feature as one of: direct mapping, desugaring, runtime helper, manual rewrite required, or unsupported. - Emit warnings for behavior changes or lossy rewrites. - Never silently drop exceptions, mutability rules, async semantics, or type expectations.

  1. Generate target code from a structured model.

- Prefer a typed target-language model or emitter API over raw string replacement. - Centralize naming, escaping, and indentation in the emitter layer. - Preserve comments or source maps only when the task explicitly requires them.

  1. Validate the output at multiple levels.

- Validate frontend assumptions separately from lowering assumptions. - Reparse representative source fixtures and assert the parser-probe contract stays stable. - Reparse the generated code. - Compile or type-check the generated code when tooling is available. - Run fixture-based execution tests for behavior-preserving conversions. - Include at least one explicit failure-path fixture that must emit diagnostics. - Snapshot IR and emitted output for regression coverage.

  1. Deliver a bounded result.

- Return the supported feature matrix, known gaps, assumptions, warnings, and next steps. - Say clearly when the result is a scaffold, partial converter, or production-ready slice.

Produce These Deliverables

  • For architecture requests, return: scope, pipeline, IR outline, feature mapping taxonomy, validation plan, and risk list.
  • For implementation requests, code the smallest working slice first, add executable tests with it, and record the exact frontend dependency/version used for the parser probe.
  • For review requests, prioritize semantic drift, unsupported constructs, parser-shape assumptions, and missing validation.

Read Additional References Only When Needed

  • Read references/architecture-blueprint.md for module layout, feature mapping taxonomy, and IR design prompts.
  • Read references/validation-checklist.md for test strategy, regression gates, and release criteria.

Apply These Guardrails

  • Do not call the converter "universal" unless a supported-language and supported-feature matrix exists.
  • Do not equate CST shape with program meaning.
  • Do not put all translation rules in one transformer file.
  • Do not promise idiomatic target code before correctness and diagnostics exist.
  • Do not add a new language pair without representative fixtures and validation.

Start With This Repo Shape

src/
  frontends/
  normalization/
  semantic/
  ir/
  lowering/
  emitters/
  diagnostics/
tests/
  fixtures/
  golden/

平台分布

OpenClaw

72.3%
按下载量换算1,632

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。

继续浏览同类 Skills