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savior-li

Human Traits Learning

by AISavior · GitHub ↗ · v1.1.4 · MIT-0
cross-platform ✓ Security Clean
141
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Install in OpenClaw
/install htl
Description
Enables AI to learn and model professional human traits through structured corporate training with real-time user feedback and adaptive growth collaboration.
README (SKILL.md)

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
Usage Guidance
This skill appears to be a safe, documentation-only training framework. Before installing, note the repository/source is not listed (no homepage) and registry/_meta version numbers differ — these are administrative inconsistencies, not functional issues. If you require provenance, ask the publisher for the source or a link to a repository; otherwise it's reasonable to use as a read-only reference since it requests no secrets, network, or system access.
Capability Analysis
Type: OpenClaw Skill Name: htl Version: 1.1.4 The 'Human Traits Learning Skill' bundle consists entirely of Markdown documentation and metadata files (SKILL.md, README.md, etc.) providing behavioral frameworks for AI agents. There is no executable code, script, or external dependency included. The instructions focus on corporate-style training methodologies and emphasize user privacy and explicit consent for any behavioral analysis. No indicators of data exfiltration, malicious execution, or prompt injection were found.
Capability Assessment
Purpose & Capability
Name/description (corporate-style training for AI) align with the provided files (only documentation frameworks). No binaries, env vars, or install steps are requested. Minor metadata issues: registry lists v1.1.4/published date while _meta.json lists v1.1.3, and the skill's source/homepage are absent — these are administrative gaps but do not contradict purpose.
Instruction Scope
SKILL.md and all included docs contain only guidance for agents/humans (no commands, no system paths, no network endpoints). Instructions explicitly require explicit consent before any user-pattern analysis and state no automatic data collection.
Install Mechanism
No install spec and no code files are present; the skill is instruction-only so nothing is written to disk or executed during install.
Credentials
The skill declares no required environment variables, credentials, or config paths and the documentation repeatedly states it does not read environment variables or make external calls.
Persistence & Privilege
No 'always' flag, no elevated privileges requested, and it does not modify other skills or system settings. User-invocable and model invocation defaults are normal.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install htl
  3. After installation, invoke the skill by name or use /htl
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.4
- Removed language pack files (Arabic, English, Spanish, Chinese) and package.json, reducing multi-language support via bundled files. - Added RELEASE_NOTES.md for improved release transparency. - Updated SECURITY.md. - Core instructional frameworks and usage instructions remain unchanged.
v1.0.1
- Updated documentation across all supported languages for clarity and consistency. - Reworked SKILL.md to streamline descriptions and emphasize instructional-only purpose. - Enhanced security and privacy sections, clarifying that the skill is purely reference material. - Simplified implementation details and success metrics; removed technical execution references. - Added package.json and _meta.json for improved metadata and package management. - Removed legacy "pure-instruction-version" documentation files.
v1.1.3
v1.1.3 delivers a significant shift to a pure instructional/guidelines format with enhanced clarity and security. - All executable code and scripts removed; now provides only structured frameworks, templates, and documentation. - Security and privacy reinforced: no code execution, data collection, or external dependencies. - Clear separation of instruction sets for both AI agents and human users, now provided in multiple languages. - Usage, feedback, and adaptation guidelines require explicit user consent; pure local (OpenClaw sandbox) operation. - Updated documentation throughout for improved transparency and accessibility.
v1.1.2
**Security and codebase modernization update** - Strengthened security: No system tools, no environment variables, and pure JavaScript (no external dependencies); updated documentation to reflect this. - Added new red lines compliance scripts to enhance safe, guideline-driven interactions. - Removed legacy and test scripts; consolidated middleware and compliance logic. - Updated guides and user documentation across all supported languages.
v1.1.1
**Added security & privacy protections and internationalization, plus middleware for compliance.** - Introduced a SECURITY.md with clear privacy and data use policies - Added a security and privacy section to user documentation - Implemented a middleware layer for red-lines compliance checks - Localized quick start guide added (French) - New main entry point file (`index.js`) for better structure - Updated and expanded tests for enhanced and compliance features
v1.1.0
**Major update: Adds bidirectional growth, enabling AI agents to both learn from and provide feedback to users.** - Introduces bidirectional growth capability: AI now gives constructive feedback, fostering mutual achievement. - Expands human traits covered, including strategic thinking, adaptability, and leadership development. - Enhances real-time analysis of user preferences and context-aware response adaptation. - Provides advanced, multi-dimensional feedback and comprehensive user profile summaries. - Implements a structured, multi-phase corporate training framework for both learning and assessment. - Defines new success metrics reflecting user, AI, and mutual growth outcomes.
Metadata
Slug htl
Version 1.1.4
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 6
Frequently Asked Questions

What is Human Traits Learning?

Enables AI to learn and model professional human traits through structured corporate training with real-time user feedback and adaptive growth collaboration. It is an AI Agent Skill for Claude Code / OpenClaw, with 141 downloads so far.

How do I install Human Traits Learning?

Run "/install htl" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Human Traits Learning free?

Yes, Human Traits Learning is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Human Traits Learning support?

Human Traits Learning is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Human Traits Learning?

It is built and maintained by AISavior (@savior-li); the current version is v1.1.4.

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