SKILL.hm
A practical framework for translating AI skill processes into human worflows and habits.
This is not another article about how to write SKILL.md for AI, on the opposite is about the skills we need to write and keep developing our selfs for the current era.
What I call skills.HM, a human perspective to define, activate, and apply skills consistently across all our professional and personal live.
A difference with AI, we HUMANS have already skills, experience, and judgment, and a good context (even when some times we dont remember it, or as I said, we have it there on a folder stored in our mind which needs to be pulled out ).
What we miss is a reputable system to know which skill to use, when to use it and how to apply it.
A recommendation can be as follows:
This approach has help me to develop skills systematically, transfer across disciplines, and use our human capability in different contexts.
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When I refer to different contexts, for example, critical thinking foundation reamins as per our own capability, but its playbook modifieds based whether you are evaluating a product development hypothesis, diagnosis a business process defect, debugging software or resolving a project risk.
How to create SKILL.hm ?
Based on your professional development, create a playbook which can follow a consistent template as:
- Name: What is the skill for?
- Purpose: What problem does it solve?
- Trigger: When should be used?
- Context: What information I need? What should I need from others and my self?
- Procedure: What steps I should follow?
- Guadrails: What vulnerabilities, bad effects, should I avoid?
- Evaluation: Based on which metrics (sucess criteria) I can consider it worked?
- Learning: What should I modified and retain based on the outcome?
Build your personal skill library.
Organize your professional development as you are doing it on your agent skills.
You may create a series of areas to store them as:
- - Core Human Skills: For communication, critical thinking, problem-solving, adaptability, emotional intelligence.
- - Business Engineering: Systems thinking, process mapping, requirements analytics, lean principles application, kaizen.
- - Product Management: Discovery, product decisions, planning, coordination, conflic resolution, risk management.
- - Kaizen & Six Sigma: Observation, root cause analysis, experimentation, evidence-based improvement.
- - Software Engineering: Architecture determination, Testing, AI Specs Driven, Code Review, Debugging.
Reading and implementing the playbook does not mean you have developed the skill, practice and improvement is what on a near future will make you sucessfully and make it your own.
We should remember during this era of “automatization” we shall as well increase our skills, redefine them and keep using them as our differentiator with AI.
