Team Topologies

Implements the framework from Skelton and Pais's book for structuring engineering orgs around Conway's law. Claude will score your team design 0-10, explain the four team types (stream-aligned, platform, enabling, complicated-subsystem), push you to declare interaction modes between teams (collaboration, X-as-a-Service, facilitating), and apply the inverse Conway maneuver when you're splitting services or planning a reorg. Useful when cognitive load questions come up, when you're deciding who owns what after breaking up a monolith, or when cross-team dependencies are slowing you down. The scoring rubric is opinionated: it wants most teams stream-aligned and will call out component teams and ticket-driven shared services as anti-patterns.

skills.sh installs: 3.3K
GitHub stars: 2.7K
last updated: Aug 21, 2026

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Install

$ npx -y skills add wondelai/skills --skill team-topologies --agent claude-code

Installs into .claude/skills of the current project.

Files

Team Topologies

A team-first approach to organization design from Matthew Skelton and Manuel Pais's Team Topologies: four fundamental team types, three interaction modes, and deliberate attention to Conway's law and team cognitive load. Use it to structure engineering organizations for fast flow of change — and to keep evolving them as the system, technology, and market shift.

Core Principle

The team is the unit of delivery, and organizations ship their communication structure. Conway's law guarantees that system architecture mirrors how teams actually communicate, so team boundaries and interactions must be designed as deliberately as the software itself. Size each team's responsibilities to its cognitive load, align most teams to streams of business change, declare how teams interact, and treat the resulting topology as a living architecture decision that optimizes for fast flow.

Scoring

Goal: 10/10. Rate org and team designs 0-10 against the principles below. Report the current score and the specific changes needed to reach 10/10.

SKILL.md on GitHub