How Domain Experts Can Build AI Agent Skills

Using a medical monitor review workflow as an example

By Alex P. Wang | May 8, 2026 | Tags: AI Agents, Agent Skills, Domain Expertise, Medical Monitor, Workflow Automation

AI tools are already very good at language, summarization, reasoning, and pattern recognition. But many experts still use them like generic chatbots: copy data into an AI tool, ask a question, adjust the prompt, and repeat.

That works for experimentation, but it does not scale well for recurring professional work. The real opportunity is not just asking AI questions. The real opportunity is turning expert workflows into reusable AI agent skills.

What Is an AI Agent Skill?

An AI agent skill is a reusable instruction framework that tells an AI agent how to perform a specific type of work.

A skill is not just a prompt. It can include review logic, evaluation criteria, expected inputs, output structure, escalation rules, domain-specific reasoning patterns, validation steps, and retrieval of supporting knowledge.

In simple terms, a skill captures how an expert approaches recurring work.

Why Generic Prompting Breaks Down

Many professionals start with a simple prompt such as:

Sometimes the result is useful. Sometimes it is vague, inconsistent, or misses important findings. The user then adds more instructions: focus on operational risks, check compliance issues, summarize key findings, provide recommendations, use bullet points, and so on.

Over time, prompts become longer, harder to manage, inconsistent, difficult to reuse, and difficult to share across teams.

The problem is not the AI model itself. The problem is that the workflow exists only inside scattered prompts and human memory.

The Shift: From Prompting to Skills

Instead of writing the same instructions repeatedly, experts can define reusable skills.

The workflow changes from:

For example, instead of explaining the entire medical review process each time, a user can simply say:

The user provides the data. The skill provides the expertise structure. The AI combines both.

Medical Monitor Review as an Example

A medical monitor often reviews adverse event listings, laboratory listings, vital signs, coding consistency, eligibility concerns, and protocol deviations.

The review process usually follows recurring patterns:

• Identify serious adverse events
• Identify Grade 3 or Grade 4 events
• Review liver toxicity
• Assess dose-response relationships
• Identify stopping-criteria events
• Evaluate clinically significant trends
• Determine whether escalation is needed

This is an ideal candidate for an AI agent skill because the workflow is structured, the review criteria are repeatable, the outputs are relatively standardized, and human oversight remains essential.

Step 1 — Identify the Repeatable Workflow

The first step is not AI. The first step is understanding your own workflow.

Ask yourself:

• What recurring tasks do I perform repeatedly?
• What patterns do I consistently look for?
• What outputs do I repeatedly generate?
• What judgment criteria do I apply?
• What findings usually require escalation?

For medical monitor review, the workflow may be:

Step 2 — Define the Required Input Data

Next, define what data the skill expects. A skill cannot reliably review data that is inconsistent, incomplete, or poorly structured.

AE Listing

• Subject ID
• Treatment arm or dose
• AE term
• CTCAE grade
• Seriousness flag
• Relationship to study drug
• Action taken
• Outcome

Lab Listing

• Subject ID
• Visit
• Analyte
• Result
• Units
• Reference range
• CTCAE grade

Vital Signs Listing

• Subject ID
• Visit
• Blood pressure
• Heart rate
• Temperature
• Oxygen saturation
• Weight
• Baseline values

Step 3 — Define the Review Logic

This is where expertise becomes reusable. The goal is to teach the AI what matters, what patterns to look for, and how to prioritize findings.

For medical monitor review, focus areas may include:

• Serious adverse events
• Grade 3 or Grade 4 abnormalities
• Liver toxicity
• Dose-response trends
• Stopping criteria
• Skin reactions
• Protocol deviations
• Coding inconsistencies

Step 4 — Define the Output Structure

Experts often underestimate how important output structure is. A reusable skill should produce consistent outputs.

This makes reviews easier to read, compare, validate, and eventually automate.

Step 5 — Write the Skill File

The skill can be written as a simple Markdown file. It does not need to be complicated at first.

This file becomes reusable. You can improve it over time instead of rewriting prompts repeatedly.

Step 6 — Test with Sample Data

Next, test the skill using synthetic data, sample listings, or de-identified datasets.

The process is simple:

Example:

Then review what the AI missed, what it overstated, whether the output was clinically useful, and whether the structure helped the review.

Where Can You Test and Run a Skill?

A skill does not need a complex platform on day one. You can start testing it anywhere you can provide three things: the skill instructions, the input data, and a clear request to apply the skill.

This is also where i80agent is heading: a place where domain knowledge, retrieval, and reusable expert skills can be tested together and eventually used in real workflows.

Step 7 — Refine Through Real Usage

The first version will not be perfect. That is expected.

Over time, new rules get added, edge cases get captured, escalation logic improves, outputs become more useful, and the workflow becomes more standardized.

This is how expertise gradually becomes operationalized.

Skills Do Not Replace Experts

This point is important: the goal is not to replace experts.

The goal is to reduce repetitive work, improve consistency, accelerate review, identify patterns earlier, structure workflows, and support decision-making.

The expert still reviews findings, validates conclusions, handles ambiguity, and makes final decisions.

This Applies Far Beyond Medical Review

The same process works in many domains.

Retrieval + Skills

One important lesson from building domain-specific AI systems is that skills alone are not enough. A skill often still depends on trusted knowledge.

For example, a medical monitor review skill may need protocol rules, study context, stopping criteria, coding standards, eligibility criteria, and historical decisions.

This is where retrieval becomes important.

Final Thoughts

Many professionals already have highly structured expertise. They just do not always think of it as something that can be formalized.

But once recurring workflows become explicit, structured, testable, and reusable, they can evolve into AI agent skills.

This is one of the most practical paths toward domain-specific AI: not replacing experts, but helping experts scale their expertise more consistently and efficiently.

I believe people in many different domains can do this. Medical reviewers, legal teams, HR leaders, finance teams, operations managers, educators, consultants, and creative professionals all have recurring workflows that depend on judgment and experience. If those workflows can be described clearly, they can probably become skills that improve consistency, reduce repetitive work, and make expert work more efficient.