Through the lens
A friend mentioned last week that his company is asking everyone to write a skill.md for their role.
My first reaction: that is the first draft of your replacement.
My second: write it anyway – before someone else does it for you.
What a skill.md actually is
A SKILL.md is a plain text file that teaches an AI agent a specific workflow. It describes what the skill does, when to trigger it, and the steps to follow. An agent reads it, loads it when a matching task arrives, and executes – no human required.
As of early 2026, the format is supported natively by over 30 major AI tools: Claude Code, OpenAI Codex CLI, GitHub Copilot, Google Gemini CLI, Cursor, Windsurf, and others. It is an open standard and the library is growing fast – tens of thousands of community skills exist across major platforms.
One skill in Vercel’s library – a framework that guides AI through hypothesis generation, structured analysis, and slide creation, replicating the workflow of a McKinsey consultant – averages 445 installs a week.
This is not prototype territory. It is live infrastructure.
The honest picture
I am not going to dress this up in alarm, but I am not going to pretend the direction is not clear either.
Research from BCG and the WEF points to the same conclusion: over the next two to three years, 50–55% of US jobs will be reshaped by AI – not wholesale replacement, but a shift in where human contribution sits inside a role. The WEF’s Future of Jobs Report projects AI will create far more roles globally than it eliminates by 2030, while acknowledging that the disruption in between is substantial. UK Government analysis found that the large majority of workers are in occupations where AI can already perform or enhance significant portions of the work.
The pattern that emerges is consistent: the most exposed tasks are the documented ones. The rule-based, step-by-step, reproducible work – requirements capture, standard analysis, configuring to known templates, building initial deliverables from established patterns.
For functional consultants, some of that list will feel uncomfortably familiar.
But this is the wrong frame to start from. Here is a more useful one.
Use it as a mirror
Try this exercise: write ten bullet points describing what you actually do in a typical working week. Then ask of each one – could this be captured in a skill.md? Could you write a step-by-step procedure that an agent could follow to produce this output?
Some of them will fit. That is honest and useful information. The right response is not to resist that – it is to automate those parts yourself, clearing space for the work that matters more.
Because the parts that do not fit are the important ones.
The call where you changed direction mid-conversation because you read something in the room. The instinct you had that a programme was drifting before anyone named it. The judgement call on who to brief informally before the formal review. The way you hold a client’s confidence steady through a difficult phase when nothing looks resolved yet.
None of that fits in a markdown file.
Those are the parts of your work that grow in value as the documented layer is automated.
Writing your own skill.md forces this distinction into clarity. It gives you a map – not of what will be replaced, but of what you should deliberately protect and build.
The human layer that compounds
Across research from the WEF, BCG, and LinkedIn, the same cluster of capabilities appears consistently as growing more valuable in an AI-augmented working environment:
- Judgement under ambiguity – making the right call when the playbook runs out
- Curiosity that interrogates AI outputs rather than accepts them
- Emotional intelligence and the ability to build trust in high-stakes relationships
- Organisational literacy – understanding how decisions actually get made, not just how they are supposed to
- Strategic storytelling – translating analysis into meaning that moves people to act
- The courage to lead when the path is not yet clear
LinkedIn’s Workplace Learning Report 2025 shows AI-related skills being added to profiles at nearly five times the rate of other skill categories tracked. The direction of travel from hiring data is clear: organisations want both AI capability and human judgement – not one without the other.
A practitioner who reviewed one of these AI-built consulting skills captured the gap precisely: it executes boilerplate analysis without the Socratic questioning that surfaces unstated assumptions. That, he noted, is the part AI is still learning.
That is where it is worth investing.
The barber story – and the impact nobody discusses
A few weeks ago I was getting my hair cut. The barber and I got talking about AI. He laughed and said he was clearly safe – he knows how to read a client, how to keep the conversation going, how to make someone comfortable in the chair. A robot cannot replicate that.
He was right. For now.
But on the drive home I thought about it differently. The risk to him is not that a machine cuts hair. It is that his clients – knowledge workers, analysts, consultants – face compressed incomes and tighter budgets. Discretionary spending, a good haircut among it, gets cut first. The route from AI to the barber chair is indirect – but it is real.
This is the second-order effect that gets lost in most AI coverage. AI does not need to automate a role to affect the people who do it. The economic chain connects knowledge work to everything else in ways that are structural, not visible in individual job descriptions.
The barber is safe from the robot. Whether his clients can still afford him is a different question.
Understanding this is not pessimism. It is context for what is actually shifting – and why building higher-value capabilities matters now, not later.
The evolutionary argument
Every major technological shift has followed the same pattern. The Industrial Revolution did not end human employment – it shifted the premium to design, supervision, quality control, and coordination of machines. The computing revolution did not end office work – it shifted value to knowledge architecture and judgement that computers could not replicate.
The environment changes. The humans who adapt most deliberately – who identify what the new environment rewards and build those capabilities intentionally – are the ones who create the next layer of value.
Write your skill.md. Use it as a mirror. Look at what is in it – and deliberately build the layer above.
Be the professional who designs the skill architecture, governs the agent stack, interrogates the outputs, holds the relationships, and makes the judgement calls that no markdown file can encode.
That professional’s value has not been reduced. It has grown.
My first reaction to skill.md — that is the first draft of your replacement — was a version of that same drive-home thought, pointing back at me. Working through my own since, I can see it was the wrong frame. You are not documenting your replacement. You are drawing the line where your contribution begins.
His chair stays full.
The challenge this week: Write ten bullet points describing what you actually do. See how many would fit in a skill.md. The ones that would not – that is where your future is.
Mike Richard is a 3x Microsoft FastTrack Recognised Solution Architect working across enterprise AI, programme delivery, and the Microsoft ecosystem. He writes regularly at mgrb.in and connects with practitioners on LinkedIn.