Book a demo
Codifying technical writing
AI

How Technical Writers Can Build a skills.md for AI Assistants

Updated on Apr 17, 2026

5 Mins Read
Build Your AI Knowledge Base
View all

Skills are reusable resources that can be provided to AI assistants such as Claude, ChatGPT and so on. Skills contain workflows, context, and best practices in your domain that can augment your AI assistant’s capabilities. Rather than repeating a set of prompts, skills can be reused for tasks that are tailored to your domain. Moreover, complex workflows can be built using skills. AI assistants such as Claude load skills metadata during startup, and instructions inside skills are loaded when triggered. Other resources and code as part of skills are loaded as needed.

For technical writers, it is important to create a skill.md file to leverage AI to its fullest capabilities and thus become a GenAI-native technical writer. Skills are becoming industry standards, and thus can be ported to any AI assistant. However, writing skills.md and its components require attention to detail in the process, required artifacts, tool descriptions, and instructions on what to use, when to use, and how to use.

What is Skills.md?

Skills.md is a Markdown file that contains instructions, rules, and guidelines for how an AI model, such as Claude, ChatGPT, and so on, should carry out a task. Skills.md can be considered as reusable prompts in general. The AI model reads skills.md and dynamically loads relevant content. Skills.md helps your AI model to produce consistent outputs.

Hierarchy of technical writing skills example

Example of Technical Writing Skills

Skills Hierarchy & Structure Explained

Skills can contain three types of content; each type is loaded at a different time.

  • Stage 1: This content contains high-level metadata, which the AI assistant loads at startup.
  • Stage 2: This content contains instructions to run once loaded into an AI assistant’s context window.
  • Stage 3: This content contains resources, code, and other tools that can be loaded as needed.

This is the generic architecture of how skills are loaded.

Generic architecture for loading skills

Technical writers need to create a good hierarchy and structure their skills to be more modular.

Skill.md file showcasing modular skill hierarchy

Skill.md file is the root file that ties everything together. Content in this file should reference the style guide, tools, and other resources so that the AI assistant can load them on an “as required” basis to perform specific documentation-related activities.

Turn your Skills.md workflows into scalable documentation with Document360.

Book A Demo
Document360

How to Structure Your Styleguide in Skills.md

In this resource, you can add all your style guides. The style guide should be written in markdown format (.md extension). If your organization uses different style guides for different product lines or services, additional style guides can be added under this folder structure. The purpose of each style guide can be stated at the start of each section so that AI assistants can understand the intent and scope of a particular guide.

Active voice example from style guide

Using a Terminology File for Consistent AI Output

Having a list of business glossary containing approved business terms as an artifact helps with an AI assistant using consistent terms for better clarity and communication. This file defines the canonical terms used across all documentation. When writing any article, these definitions and spellings are used exactly. This prevents the AI assistant from using synonyms for defined terms or paraphrasing terms to avoid misinterpretation.

Importance of templates in technical products

Why Templates Are Essential in Your Skills.md

As technical writers, we follow templates to help our readers understand the structure of different types of content. For example, templates can be made for step-by-step procedures, release notes, troubleshooting guides, use cases, and so on. Each of those templates can be stored as an .md file in the templates hierarchy.

Markdown writing workflows for technical documentation

Writing Workflows in Markdown

In technical writing practice, each process in the DDLC (Documentation Development Life Cycle) has a workflow. Each workflow is usually documented in classical swimlanes using business modeling methodology. However, workflows can be converted into a sequence of steps and written in Markdown format.

Swimlane diagram illustrating workflow steps

Example of workflow represented in swimlanes

All workflows can be given in h1 tags, while procedures can be written in h2/h3 tags. All workflows can be added in a single markdown file.

Workflow example for API documentation creation

Defining Tools in tools.md

These are a set of tools that a technical writer will have access to to undertake certain tasks. For example, if a technical writer wishes to research a particular product feature before writing, they might use a search engine, collate information, and then compile notes before writing a first draft. In some scenarios, a technical writer uses tools such as MCP servers, APIs, AI search engines, and so on. All tools can be clearly stated in the tools.md file along with the purpose of operations. These tools can be tied to each workflow, and your AI assistant will use those tools to execute workflows effectively.

Testing Your Skills.md: Ensuring Proper Triggering

Writing a proper skill.md file for an AI assistant is indispensable to leverage all capabilities of cutting-edge GenAI technology. More importantly, technical writers need to test all skill.md components to ensure that they are properly triggered on the right instances and able to execute workflows using the right tools. It is high time that technical writers codify their traditional skills and put them in md files to help AI produce documentation.

Centralize all your documentation and make it easily searchable for everyone.

cta

Selvaraaju Murugesan

Selvaraaju (Selva) Murugesan received the B.Eng. degree in Mechatronics Engineering (Gold medalist) from Anna University in 2004 and the M.Eng. degree from LaTrobe University, Australia, in 2008. He has received his Ph.D. degree in Computational mathematics, LaTrobe University. He is currently working as a Senior Director of Data Science at SaaS startup Kovai.co. His interests are in the areas of business strategy, data analytics, Artificial Intelligence and technical documentation.

Read more
Request Documentation Preview