Multi-Agent AI Skills: Building for Team Workflows

Most skills today are designed for a single agent working on a single task. You invoke a skill, it does its thing, you move on. That model works fine for isolated problems. But the infrastructure beneath it is moving toward something fundamentally different: agents that coordinate in teams, delegate tasks, and operate in parallel.

For skill builders, this is not a theoretical concern. It changes what you build, how you structure it, and what makes a skill worth installing.

Disclaimer: Signal Reading, Not Confirmed Roadmap

The multi-agent patterns discussed here draw on CCLeaks research -- community-generated analysis of Claude Code's architecture based on code artifacts and feature flags. This content is AI-generated, may contain errors or misinterpretations, and is not affiliated with or endorsed by Anthropic. Treat the specifics as directional signals about where agent infrastructure is heading, not as confirmed features or release plans.

Key Takeaways

• Multi-agent coordination moves skills from self-contained generalists to narrow specialists that excel at one task within a team pipeline.
• Four key handoff patterns define team-aware skills: pipeline (sequential), fan-out/fan-in (parallel), escalation (scope boundaries), and supervision (background monitoring).
• Composability -- clean input/output contracts, scoped permissions, and structured output -- becomes the primary quality signal for skills in a multi-agent world.
• Team configurations (.claude/teams/<name>.json) create a new product category: curated team recipes that combine specialist skills into complete workflows.
• The agentName@teamName addressing model gives skills explicit identity, requiring role clarity, capability advertising, and confidence-level reporting.

What the Multi-Agent Signal Looks Like

The CCLeaks research paints a detailed picture of multi-agent coordination in Claude Code's architecture. The core concept is a Coordinator Mode where "Claude becomes a manager. It breaks your task into pieces, assigns each to a separate worker agent running in parallel."

The implementation details are instructive. Multiple backend options are described: InProcessBackend (agents running in the same Node.js process), TmuxBackend (agents in separate terminal panes), and ITermBackend. Team configuration lives in .claude/teams/<name>.json files with lead and member definitions. Agents get addressed through an identity model -- agentName@teamName -- that feels remarkably like email addressing for AI workers.

There are restrictions in this architecture. Agents can be blocked from certain tools, while the Coordinator gets exclusive tools that worker agents cannot access. Cross-session message passing uses a UDS (Unix Domain Socket) Inbox system. A Daemon Mode enables background session supervision. And for Max and Enterprise tiers, Plan Mode V2 is described as deploying three parallel exploration agents to investigate a problem space before committing to an approach.

The pattern is clear: the single-agent model is being extended into a multi-agent operating model. And that changes the game for skills.

The Shift from Solo Skills to Specialist Skills

In a single-agent world, a skill needs to be self-contained. Your code review skill handles everything: reading the diff, understanding the context, checking for issues, formatting feedback, and presenting results. It is a generalist by necessity because there is nobody to delegate to.

In a multi-agent world, skills can specialize. Instead of one monolithic code review skill, you might have:

A Researcher Skill that examines the codebase context -- what was this code doing before the change, what are the related files, what are the testing patterns for this module. It hands off a context package.

An Evaluator Skill that takes the context package and the diff, applying specific quality criteria. It does not need to understand the codebase layout because the researcher already handled that.

A Standards Skill that checks against team-specific coding standards, naming conventions, and architectural patterns. Narrow scope, deep expertise.

A Writer Skill that takes the evaluator's findings and produces human-readable review comments in the team's preferred format and tone.

Each skill does one thing well. The coordinator assembles them into a pipeline. This is the Unix philosophy applied to AI capabilities: small, composable tools connected through well-defined interfaces (for more on how Claude Code's 43-tool architecture shapes skill design, see our detailed analysis).

Solo Skills vs Team-Aware Skills

Composability Is the New Competitive Advantage

If multi-agent coordination becomes the standard execution model, the skills that win will not be the most feature-rich. They will be the most composable.

Composability means several things for skill design:

Clean Input/Output Contracts

A team-aware skill needs to clearly define what it expects as input and what it produces as output. Today, most skills receive natural language context and produce natural language output. That works for solo use. For team coordination, skills need more structured contracts.

What format does your skill expect its input in? What does it guarantee about its output structure? Can another agent reliably parse what your skill produces? These questions do not matter when a human is the only consumer of skill output. They matter enormously when another agent is the consumer.

Scoped Capabilities

The CCLeaks research mentions tool restrictions -- agents blocked from certain tools. This implies that skills operating in a team context will have defined capability boundaries. A researcher skill might have read access to the filesystem but no write access. An evaluator skill might have no filesystem access at all, working purely with the context it receives.

Skill builders should think about minimum viable permissions. What is the smallest set of tools your skill actually needs? Skills that request broad access will be less useful in team compositions because they create security and predictability concerns for the coordinator.

Stateless Between Handoffs

In a team workflow, skills need to pass state explicitly rather than relying on implicit context. If a researcher skill discovers something important, it cannot assume the evaluator skill shares the same session context. The finding needs to be in the output, not in the ambient state.

This is a discipline that improves skill quality even in solo use. Skills that externalize their reasoning rather than relying on conversation context are more robust, more testable, and more composable.

Handoff Patterns That Skill Builders Should Understand

Multi-agent coordination introduces handoff patterns that do not exist in solo-agent work. Understanding these patterns helps skill builders design for the emerging execution model.

Pipeline Handoff

The simplest pattern: Skill A produces output, Skill B consumes it. A documentation skill generates API docs, then a review skill checks them for completeness. The key design question: what is the handoff format? If your skill's output is a blob of unstructured text, it is hard for the next skill in the pipeline to work with reliably.

Fan-Out / Fan-In

The coordinator dispatches the same task to multiple skills in parallel, then synthesizes results. Three different analysis skills examine a codebase from different angles -- security, performance, maintainability -- and the coordinator combines their findings. Skills designed for fan-out need to produce output that is easy to merge without conflicts or redundancy.

Escalation

A worker skill encounters something outside its scope and signals the coordinator for help or reassignment. This requires skills to have clear boundaries and the ability to articulate what they cannot handle. A security-focused skill that finds a performance issue should flag it and move on, not attempt an analysis it was not designed for.

Supervision

The Daemon Mode concept from CCLeaks suggests background supervision -- an agent monitoring other agents' work. Skills that operate in supervised contexts need to produce observable outputs: progress indicators, intermediate results, and clear completion signals. A skill that runs silently for ten minutes and then dumps a result is hard to supervise.

The Team Configuration Implications

The .claude/teams/<name>.json configuration format described in the CCLeaks research suggests that teams will be explicitly defined, not emergent. This means skill builders can anticipate specific team compositions and design for them.

Consider a "Full Stack Review Team" configuration:

• Lead: A coordinator skill that understands code review workflow
• Members: Frontend specialist, backend specialist, database specialist, security specialist

Each member skill is designed to work in this team context. The frontend specialist knows it only needs to examine client-side code. The database specialist focuses on query patterns and schema changes. They do not step on each other's territory because the team configuration defines their scope.

This creates an opportunity for skill builders to publish not just individual skills, but team recipes -- curated combinations of skills designed to work together. The marketplace value shifts from "best individual skill" to "best team composition."

Addressing and Identity in Skill Design

The agentName@teamName addressing model is worth thinking about carefully. It implies that skills have identity within a team context -- they are not anonymous workers but named participants with roles.

For skill builders, this means thinking about:

Role clarity. When your skill introduces itself to other agents in a team, what does it say? "I am the security reviewer" is more useful than "I am a general-purpose code analysis tool." Clear role identity helps the coordinator make better delegation decisions.

Capability advertising. In a team context, your skill may need to declare what it can and cannot do so the coordinator can route tasks appropriately. This is different from a skill description aimed at humans -- it needs to be precise enough for another agent to reason about.

Conflict resolution. If two skills in a team produce contradictory recommendations, how does the coordinator resolve it? Skills that express confidence levels and explain their reasoning make coordination easier. "This is a critical security issue, high confidence" is more useful than "you should fix this."

What This Means for the Skill Market

Multi-agent coordination creates several market dynamics that skill builders should anticipate.

Specialist skills become more valuable than generalist skills. In a team model, you want the best security reviewer, not the best all-around code reviewer. Depth beats breadth.

Composability becomes a quality signal. Skills that work well with others -- clean contracts, scoped permissions, structured outputs -- will be preferred over skills that only work in isolation.

Team recipes become a product category. Beyond individual skills, curated team compositions that solve specific workflows (code review team, deployment team, documentation team) will have marketplace value.

Testing becomes multi-agent. You cannot test a team-aware skill in isolation and claim it works. Testing needs to verify behavior in team contexts -- with handoffs, parallel execution, and coordinator oversight.

Frequently Asked Questions

What is multi-agent coordination in AI?

Multi-agent coordination is an execution model where multiple AI agents work together on a task, with a coordinator agent breaking work into pieces and delegating to specialist worker agents running in parallel. Each agent has a defined role, scoped permissions, and structured handoff patterns.

What is Coordinator Mode in Claude Code?

Coordinator Mode is described in CCLeaks research as a feature where Claude acts as a manager, breaking tasks into pieces and assigning each to a separate worker agent. The coordinator gets exclusive tools that workers cannot access, and manages task delegation, result synthesis, and escalation handling.

What are team-aware AI skills?

Team-aware skills are AI capabilities designed to operate within multi-agent teams rather than in isolation. They feature clean input/output contracts, scoped permissions, structured output for agent-to-agent handoff, and clear role identity -- making them composable building blocks for coordinated workflows.

What are AI skill team recipes?

Team recipes are curated combinations of specialist skills designed to work together for a specific workflow. For example, a "Full Stack Review Team" might combine a frontend specialist, backend specialist, database specialist, and security specialist under a coordinator skill that manages the code review workflow.

How do handoff patterns work in multi-agent systems?

There are four primary handoff patterns: pipeline (sequential, Skill A feeds Skill B), fan-out/fan-in (parallel execution with result synthesis), escalation (a worker flags tasks outside its scope for reassignment), and supervision (background monitoring of other agents' work with progress tracking).

Building for the Team Era

You do not need multi-agent infrastructure to start building team-aware skills. The design principles apply now:

Define your skill's interface explicitly. What does it expect? What does it produce? Document the contract.

Scope narrowly. Resist the temptation to build a skill that does everything. A skill that does one thing reliably is more valuable in a team composition than a skill that does ten things adequately.

Structure your output. If another agent needed to parse your skill's output programmatically, could it? If not, add structure.

Declare your boundaries. What does your skill not do? What should be handled by a different skill? Being explicit about scope makes your skill a better team player.

Think about the coordinator's perspective. If you were managing a team of agents and one of them was your skill, what would you need to know to use it effectively? That is the information your skill should surface.

The single-agent era built the foundations. The multi-agent era will build the economy. The skills designed for team coordination today will be the building blocks of tomorrow's most valuable AI workflows.

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