Teams using AI coding tools independently without shared context produce inconsistent output. This post presents a pattern using AGENTS.md files (repository-level context for architecture, conventions, and tech stack) combined with reusable Skills (step-by-step task instruction files under .github/skills/) consumed by GitHub Copilot CLI. Together they standardize AI-assisted development across a team. The post covers how to structure both artifacts, anti-patterns to avoid, skill lifecycle governance, and a verification layer using CI, testing pyramids, linting, and static analysis. The approach is language-agnostic and was validated on a polyglot Azure-deployed stack (Python/FastAPI, Next.js, Terraform).
Table of contents
Introduction Copy linkThe Problem: Ad-Hoc AI Usage Doesn’t Scale Copy linkThe Solution: AGENTS.md + Skills Copy linkApplying This Pattern Copy linkDesigning New Skills Copy linkSkill Lifecycle and Governance Copy linkAnti-Patterns When Creating Skills Copy linkLessons Learned Copy linkSummary Copy linkResources Copy linkAttribution Copy linkSort: