Token导航 LogoToken导航

Agent Skill · 开发

htl(赫特尔)

htl 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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。

htl 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 7,181。

总安装 4,004周安装 172GitHub Stars 公开资料未说明下载 1,404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
1.1.4

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:htl(赫特尔)
来源仓库:https://github.com/savior-li/htl
安装命令:
openclaw skills install htl
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 htl

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

使人工智能能够通过结构化的企业培训、实时用户反馈和自适应增长协作来学习和建模专业的人类特征。

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 htl;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

    它需要接触敏感数据,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

htl 怎么安装?

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

htl 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。

htl 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

htl 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。

htl 用的人多吗?

公开数据是累计安装 4,004 次,近一周安装 172 次,下载 1,404 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。

htl 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
htl

赫特尔 · 本页

4,004—接触敏感数据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

Human Traits Learning Skill

Description

Provides instructional frameworks for AI Agents to learn excellent human traits, thinking patterns, and behavioral styles through structured corporate-style training methodologies. This skill offers pure instructional content and guidelines only.

Core Philosophy: Enable mutual understanding between humans and AI agents through structured learning principles and shared growth mindsets.

🔒 Security & Privacy Commitment

  • Pure Instructional Content: Contains only frameworks, guidelines, and documentation - no executable code
  • No System Dependencies: Requires no external tools, system access, or network connectivity
  • No Data Collection: Does not automatically collect or process user data
  • Explicit Consent Required: Any user pattern analysis requires clear, explicit permission
  • Local Reference Only: All content serves as reference material for manual application
  • No Environment Variables: Does not read or use any system environment variables
  • Transparent Usage: Clear documentation that this is instructional material only

Core Training Framework: Corporate Excellence Pathway

Phase 1: Onboarding & Foundation (0-6 months)

Focus: Basic competency building and cultural integration

  • Structured Learning: Guidelines for formal training on core skills
  • Mentorship: Frameworks for pairing with experienced colleagues
  • Clear Expectations: Templates for defining performance metrics and goals
  • Regular Feedback: Structures for weekly check-ins and monthly reviews

Phase 2: Skill Development & Independence (6-18 months)

Focus: Building expertise and autonomous problem-solving

  • Stretch Assignments: Guidelines for challenging projects beyond comfort zone
  • Cross-functional Exposure: Frameworks for working with different teams/departments
  • Peer Learning: Structures for collaborating with other high-potential individuals
  • Self-directed Learning: Guidelines for taking initiative on skill development

Phase 3: Leadership Preparation (18-36 months)

Focus: Developing leadership capabilities and strategic thinking

  • People Management: Frameworks for leading small teams or mentoring juniors
  • Strategic Projects: Guidelines for contributing to department/company strategy
  • External Representation: Structures for representing in client/partner interactions
  • Decision Authority: Frameworks for making independent business decisions

Phase 4: Middle Management Excellence (36+ months)

Focus: Full leadership role with P&L responsibility

  • Team Leadership: Guidelines for managing larger teams with diverse skill sets
  • Resource Allocation: Frameworks for budget and resource management authority
  • Talent Development: Structures for recruiting, developing, and retaining talent
  • Business Impact: Guidelines for direct contribution to company performance

Implementation Guidelines for AI Agents

Historical Learning Integration

  • Experience Documentation: Guidelines for systematic recording of interactions and outcomes
  • Pattern Recognition: Frameworks for identifying successful behavioral patterns
  • Feedback Loops: Structures for regular self-assessment against established benchmarks
  • Continuous Improvement: Guidelines for iterative refinement based on results

Key Human Traits to Learn

  1. Professional Excellence: High standards, attention to detail, reliability
  2. Strategic Thinking: Long-term perspective, resource optimization, risk management
  3. Interpersonal Skills: Communication, collaboration, conflict resolution
  4. Adaptability: Flexibility in changing environments, learning agility
  5. Leadership: Inspiring others, decision-making, accountability

Bidirectional Growth Framework

  • User Preference Analysis: Guidelines for analyzing communication style and work preferences (with explicit consent)
  • Personalized Adaptation: Frameworks for adapting responses to match user preferences
  • Constructive Feedback: Structures for providing thoughtful suggestions for user improvement
  • Mutual Achievement: Guidelines for creating opportunities for both human and AI to learn and grow together

Usage Instructions

This skill provides instructional frameworks and guidelines only. To apply:

For AI Agents

  • Reference these frameworks when providing assistance
  • Apply the principles based on specific context and user needs
  • Ensure proper consent before any user pattern analysis
  • Maintain transparency about adaptation processes

For Human Users

  • Use as reference material for AI behavior expectations
  • Apply frameworks manually in your interactions
  • Provide feedback to help AI agents better understand your preferences
  • Respect privacy boundaries and consent requirements

Language Support

Available in multiple languages for global accessibility.

Success Metrics

  • Professional Excellence: Implementation of high-quality, thorough approaches
  • Mutual Growth: Evidence of bidirectional learning and development
  • Quality Assurance: Consistent adherence to ethical and professional standards
  • Cultural Adaptability: Effective use across different languages and contexts
  • Security Confidence: Safe, transparent, and ethical usage patterns

平台分布

OpenClaw

89.6%
按下载量换算1,258

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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