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Agent Skill · 效率

weclone-twin-reply(我们克隆双胞胎回复)

weclone-twin-reply 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。

weclone-twin-reply 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 8,635。

总安装 6,888周安装 287GitHub Stars 1下载 2,296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
0.1.0

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:weclone-twin-reply(我们克隆双胞胎回复)
来源仓库:https://github.com/xming521/weclone-twin-reply
安装命令:
openclaw skills install weclone-twin-reply
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 weclone-twin-reply

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。

根据角色设定与对话上下文生成审查门控的AI回复。支持模仿特定用户风格并保持一致性表达。适用于客服、教育或个性化交互场景。weclone-twin-reply 属于效率类 Skill,可作为该场景下的辅助能力补充。需设置内容过滤规则以避免不当输出。使用前请确认角色示例数据的准确性与版权合规性。

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 weclone-twin-reply;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

weclone-twin-reply 怎么安装?

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

weclone-twin-reply 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。

weclone-twin-reply 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。

weclone-twin-reply 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。

weclone-twin-reply 用的人多吗?

公开数据是累计安装 6,888 次,近一周安装 287 次,下载 2,296 次,GitHub 1 星。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。

weclone-twin-reply 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
weclone-twin-reply

我们克隆双胞胎回复 · 本页

6,8881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
weclone-twin-reply
description
Build a review-gated digital twin reply from persona markdown, persona examples, and live conversation context. Use when you need to imitate a specific user's chat style, draft a reply on the user's behalf, or generate a persona-consistent message candidate from an existing persona pack.
version
0.1.0
emoji
✍️
homepage
https://github.com/xming521/WeClone-Skill
metadata
openclaw
requires
bins

WeClone Twin Reply

Assemble an isolated prompt package that lets a separate model imitate one user's messaging style, personality, values, and worldview from markdown persona files and persona examples. Draft first, review second, send last.

Expected Inputs

  • A prepared persona directory. Default to ai_twin/ at the repo root, containing profile.md, persona_examples.md, guardrails.md, and optional state.md.
  • Runtime context: one short scene summary and one dialogue window.
  • Explicit approval from the user before any outbound send.

If the persona directory does not exist yet, use $weclone-init-twin first to scaffold the default ai_twin/ directory, then ask the user to fill the generated templates before drafting.

Core Workflow

  1. Confirm that the persona pack already exists.

If profile.md, persona_examples.md, or guardrails.md are missing, stop and hand off to $weclone-init-twin. Confirm that the persona files are filled with real content rather than placeholders.

  1. Gather the minimum high-signal context.

Include who the other person is, what the current situation is, and the recent messages that the reply must answer. Before drafting, determine whether the available context is sufficient to answer faithfully and safely. If key facts are missing or the likely reply would change materially depending on missing context, stop and ask the user whether to collect more information before proceeding. Write that context into two runtime files: - scene.md: a short summary of background facts that are necessary for the reply but may not be obvious from the raw chat. Include who the other person is, the relationship, the current situation, the platform or app where the reply will be sent, the user's likely goal or constraint, and any reply-specific caution such as "do not commit yet". - dialogue.md: the active message window, usually the recent turns that the candidate reply is directly answering. Keep the original wording and speaker attribution when possible. Use scene.md for distilled context and dialogue.md for raw conversation. Do not dump the entire chat history into scene.md.

  1. Render the prompt package.

Run python3 skills/weclone-twin-reply/scripts/render_clone_prompt.py --scene <scene.md> --dialogue <dialogue.md> [--extra-context <file>]. By default it reads persona files from ai_twin/; pass --persona-dir <dir> only when overriding that location. The script injects scene.md into the Runtime Scene section of the final prompt and dialogue.md into Active Dialogue. Treat the rendered prompt as the entire allowed context for that generation.

  1. Return a reviewable draft.

Show the user the candidate reply plus risk flags. Do not send on the user's behalf yet.

  1. Send only after explicit approval.

If the user edits or rejects the draft, revise it and repeat the review step.

Execution Rules

  • Keep the clone run isolated.

Use a separate model call or isolated agent run that receives only the rendered clone prompt. Do not mix in unrelated notes, hidden scratchpad, or other task memory from the current thread.

  • Load persona files intentionally.

Use profile.md for stable identity, personality, values, worldview, and decision logic; state.md for recent status and goals; persona_examples.md for style imitation plus behavioral evidence; and guardrails.md for hard limits. Load extra *.md files in the persona directory only when they materially improve the reply.

Guardrails

  • Treat guardrails.md as the persona pack's source of truth for hard limits.
  • The renderer template adds runtime guardrails for promises, privacy, reputation, ambiguity, and reviewer handoff.
  • If the request is close to the boundary, bias toward a shorter, safer draft and stop at draft stage unless a human review step is guaranteed.
  • If the available context is not enough for a defensible draft, do not fill the gaps by guesswork. Ask whether to gather more information first.

Files And Resources

  • assets/clone_prompt_template.md: single source of truth for the isolated clone prompt seen by the downstream model.
  • scripts/render_clone_prompt.py: compile persona files and runtime context into the template, reading ai_twin/ by default.

Runtime Context Format

Use a short scene.md like:

# Scene

Other person: former coworker, familiar but not close lately.
Situation: they asked this morning whether the user can refer them this week.
Platform: WeChat private chat.
User goal: stay polite and leave room without making a commitment.
Reply caution: do not promise a referral or a timeline.

Use a short dialogue.md like:

# Dialogue

Them: Hey, are you free to refer me for the role we talked about?
User: I saw your message just now.
Them: No rush, but they are moving quickly this week.

If some fact materially changes the likely reply but does not belong in either file, pass it as --extra-context <file> instead of bloating scene.md.

Output Contract

Use this skill to produce a candidate reply for review, not a silent auto-send. The exact handoff structure is defined in assets/clone_prompt_template.md.

If the user asks to automate sending, keep the approval gate in place and make the send step conditional on explicit confirmation.

平台分布

OpenClaw

79.5%
按下载量换算1,825

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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