Authoring Agent Skills: A Software-Engineering Approach

2026-07-27

AI Summary

This work proposes a software engineering–inspired approach to enhance the controllability and engineering rigor of large language model (LLM) agents by treating agent skills as modular software components. For the first time, core software engineering principles—including single responsibility, separation of interface and implementation, low coupling, and token economy—are systematically applied to guide skill design. The authors establish a behavior-evaluation-driven skill development pipeline integrating UML modeling, phased loading mechanisms, and standardized skill descriptions, while formally defining skill structure and loading models. The study further identifies canonical implementation patterns and anti-patterns, and formulates decision rules for selecting among coordination mechanisms such as memory integration and sub-agent delegation. This framework provides developers with actionable guidelines for building reusable, maintainable skills and offers criteria for evaluating trustworthiness in third-party skills.

Abstract

Agent Skills are an emerging way to extend large language model agents with reusable procedural knowledge that the agent loads on demand. Anthropic introduced Agent Skills and published the format as an open specification supported across several agent tools. This note argues that a skill is a software artefact and that its construction should follow software-engineering principles, with qualifications: single responsibility, separation of interface from implementation, low coupling, and economy in a shared token budget, together with behavioural evaluation in place of deterministic testing. Using Claude Code as the reference implementation, it describes how a skill is structured, how its contents are loaded in stages, and how to write the description on which selection depends. It places skills against the other mechanisms a developer can use to shape agent behaviour, like project memory files, slash commands, subagents, external tool connections, and hooks, and gives a rule for choosing between them based on who decides that a mechanism runs and what guarantee it provides. It then sets out an evaluation-driven authoring process, a set of patterns and faults commonly encountered in authoring, and the trust question raised by using skills from third parties. We illustrate the comparison drawn in UML class style, the loading model, the anatomy of a skill, the relative position of each mechanism, and the points at which skills and hooks act during a session.

Problem

• Agent Skills
• software engineering
• large language models
• reusable procedural knowledge
• agent behavior customization

Innovation

• Agent Skills
• software engineering principles
• behavioral evaluation
• modular agent design
• token budget economy