mlsanityData to 7 Oct 2026

Authoring Agent Skills: A Software-Engineering Approach

arXiv 2607.25032 ↗First posted 27 Jul 2026cs.SE cs.AIcited by 1

On the map: LLM agents and tool use → Agent skill learning and evolution

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use. Builds on 0 of its 4 references that are in the map. First posted on arXiv 27 Jul 2026.

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  1. 2026Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality
  2. 2026From Anatomy to Smells: An Empirical Study of SKILL.md in Agent Skills
  3. 2026SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
  4. 2026SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?
  5. 2026From Registry to Repository: How AI Agent Skills Are Written, Adapted, and Maintained
  6. 2026A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications
  7. 2026Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries
  8. 2026A Framework for Evaluating Agentic Skills at Scale