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Agent Skill · 研究检索

openclaw-agent-chinese-laoshi(OpenClaw Agent chinese laoshi 搜索)

openclaw-agent-chinese-laoshi 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

openclaw-agent-chinese-laoshi 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为操作浏览器;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 24,881。

总安装 3,897周安装 164GitHub Stars 1下载 1,364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
1.0.13

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:openclaw-agent-chinese-laoshi(OpenClaw Agent chinese laoshi 搜索)
来源仓库:https://github.com/zack-dev-cm/openclaw-agent-chinese-laoshi
安装命令:
openclaw skills install openclaw-agent-chinese-laoshi
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 openclaw-agent-chinese-laoshi

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

openclaw-agent-chinese-laoshi 处理中文课程成绩单与字幕输入标准化。包含公共课程数据、学习者文档与本地导出捆绑包。适用于教育数据研究或试点项目场景。安装前需确认权限范围与维护状态,可能涉及数据导入与处理。建议结合来源仓库和原始 README 核验数据格式与合规性要求。

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 openclaw-agent-chinese-laoshi;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

    它需要操作浏览器,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

openclaw-agent-chinese-laoshi 怎么安装?

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

openclaw-agent-chinese-laoshi 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。

openclaw-agent-chinese-laoshi 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

openclaw-agent-chinese-laoshi 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。

openclaw-agent-chinese-laoshi 用的人多吗?

公开数据是累计安装 3,897 次,近一周安装 164 次,下载 1,364 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。

openclaw-agent-chinese-laoshi 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。
  • 需要处理研究检索类任务、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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
openclaw-agent-chinese-laoshi

OpenClaw Agent chinese laoshi 搜索 · 本页

3,8971操作浏览器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
openclaw-agent-chinese-laoshi
description
Use when studying or normalizing Chinese lesson transcript/subtitle inputs with bundled public lesson data, learner docs, local export bundles, and pilot-first prepublish leak gates.
version
1.0.14
homepage
https://github.com/zack-dev-cm/openclaw-agent-chinese-laoshi
user-invocable
true
model-invocable
false
disable-model-invocation
true
metadata
{"openclaw":{"homepage":"https://github.com/zack-dev-cm/openclaw-agent-chinese-laoshi","skillKey":"openclaw-agent-chinese-laoshi"}}

OpenClaw Chinese Laoshi Ops

Use this skill when working with the bundled public Chinese lesson pack or with Chinese lesson transcript/subtitle inputs and a repository that documents its own lesson schema, local command surface, and publication gate.

Use This Skill When

  • the task is to normalize transcript or subtitle drops from Chinese lessons
  • the user wants to study from the bundled public lesson pack
  • the user wants lesson summaries, conspects, vocabulary, grammar, drills, or tests
  • the user wants roleplay scenarios, daily sprints, or HSK-style practice based on the bundled lesson data
  • the user asks for visible public tutor prompt artifacts or platform prompt packs
  • the user wants Markdown and JSON lesson assets prepared as local export bundles
  • the user wants to package or publish the workflow without leaking local paths,

known Drive IDs, or secret-shaped text

Runtime, Commands, And Credentials

  • This skill has no standalone runtime requirement and does not install code.
  • This published ClawHub skill can use bundled public course data or

transcript/subtitle inputs only.

  • It does not request API keys, cloud transcription credentials, browser

sessions, or Drive auth.

  • No Google Drive cloud upload or direct Drive API access is declared or assumed

by this published skill.

  • Optional mounted-Drive sync is allowed only when the checked-out source repo

documents a local sync command, and only with an explicit user-provided --drive-root pointing at a pre-authenticated local mount.

  • Before executing any repository command, present the exact command and wait for

explicit user confirmation in the current conversation.

  • Never search for credentials, infer credential locations, or read

system-wide browser/Drive auth stores.

Operating Procedure

  1. If the user wants study help, inspect the bundled public course pack in

references/course-data first and stay inside that data.

  1. If the user is operating through ChatGPT or a GitHub connector, apply

references/chatgpt-connector-guidance.md before searching across repos.

  1. If the user asks for visible tutor prompts, cite only public artifacts from

references/system-prompts or the public repo's prompts/system directory.

  1. If the user wants content creation, confirm the input is transcript or subtitle text. If the source is

video-only, stop and ask for transcript/subtitle input or for the user to switch to a private source-repo workflow.

  1. Inspect the checked-out repository docs, schemas, and command references

before proposing edits or commands.

  1. Move only one lesson at a time beyond scaffold state. Lesson 01 remains the

pilot gate before scaling.

  1. Build learner-facing artifacts only after grounded extraction exists.
  2. Run the repository's documented public release gate before GitHub or ClawHub

publication.

If a matching audited command is absent, stop and ask for source-repo instructions or explicit commands. Do not recreate the pipeline, call external services, inspect local credential stores, or continue with ad hoc extraction.

Core Rules

  • Raw lesson media stays in Drive or another operator-controlled store.
  • Lesson 01 is the pilot gate. Do not scale real content to lessons 02-16 until

lesson 01 is approved.

  • Keep uncertainty visible. Missing Hanzi, pinyin, or translation should be

marked, not guessed.

  • The tutor is Petrov-inspired, not Petrov impersonation.
  • Treat all public publication surfaces as hostile to private details. ClawHub

and GitHub publication should assume anyone can read SKILL.md.

Workflow

1. Extract

  • For study mode, use references/course-data/lessons-bundle.json,

references/course-data/roleplays, and references/course-data/hsk before asking for external files.

  • Prefer a transcript or subtitle drop when available.
  • If the source is video-only, stop until a transcript/subtitle input exists or

the user explicitly switches to a source-repo-specific private workflow.

  • Keep timestamps, speaker-role placeholders, and uncertainty notes.

2. Ground

  • Convert raw transcript segments into the lesson schema.
  • Add summaries, conspects, vocabulary, grammar, pronunciation, drills, and

tests only when the source supports them.

  • Keep source traceability visible.

3. Review

  • Check lesson quality against the pilot-first and editorial gates.
  • Reject unsupported content, weak answer keys, and synthetic filler.
  • Treat speaker labeling, Hanzi, pinyin, and translation drift as correctness

problems, not style nits.

4. Render And Export

  • Rebuild learner-facing Markdown after lesson JSON changes.
  • Build JSON and Markdown export bundles locally after the repo copy passes

checks.

  • Sync to a mounted Drive folder only when the user supplies an explicit

--drive-root and the repository documents a managed export marker.

  • Keep exports small; raw media should not enter the repo or the public skill.
  • Public skill course data must stay sanitized and small: lesson bundle,

roleplays, HSK payloads, lesson plans, and course index only.

5. Publish

  • The public bundle must pass the release gate before GitHub or ClawHub.
  • The gate should fail closed on placeholders, local absolute paths, localhost

URLs, websocket/debug endpoints, secret-like strings, and known lesson file IDs.

  • The gate must also fail if bundled references/course-data is missing,

incomplete, or different between the standalone public skill and plugin skill.

  • The gate must also fail if public tutor prompt artifacts are missing from

references/system-prompts, missing from the public repo prompts/system surface, or drift between standalone and plugin copies.

Do Not

  • Do not guess missing Chinese text or smooth weak source material into fake fluency.
  • Do not move lessons 02-16 past scaffold state before the lesson 01 pilot clears.
  • Do not publish local paths, private emails, mounted Drive paths, or browser

session details.

  • Do not let the public skill and the bundled plugin copy drift apart.
  • Do not request API keys, browser sessions, or Drive auth from the published

ClawHub skill.

  • Do not execute repository commands until the user confirms the exact command.
  • Do not run Drive sync or media extraction unless the required local command is

documented in the checked-out repo and the user has supplied the needed input explicitly.

References

  • references/pipeline.md

- current lesson pipeline, state transitions, and repo command surfaces

  • references/release-gates.md

- public publication checklist and leak/slop/bleed blockers

  • references/chatgpt-connector-guidance.md

- ChatGPT/GitHub connector routing rules for prompt lookup and roleplay start

  • references/system-prompts

- public tutor prompt packs and shared runtime correction contract

  • references/course-data

- sanitized lesson bundle, lesson plans, roleplays, and HSK-style practice

平台分布

OpenClaw

98.7%
按下载量换算1,346

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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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