Claude Agent Teams: Multi-Agent Skills Guide [2026]
How Claude agent teams use specialized skills to tackle complex tasks, the test-measure-refine loop for improving skills, and context engineering patterns for large-scale workflows.
Quick Answer
Claude agent teams are multi-agent workflows where an orchestrator agent delegates to specialist agents (research, code, test, review) — each with their own skills and context. Skills power each agent's specialization. The test-measure-refine loop (test 10 inputs → score → fix one thing → repeat) is how you systematically improve skills over time.
What Are Claude Agent Teams?
Claude agent teams are a multi-agent architecture where a primary orchestrator agent coordinates multiple specialist subagents working in parallel. Each subagent has its own context window, specialized skills, and assigned responsibilities.
This matters because complex software tasks (e.g., "refactor authentication across 200 files") exceed what any single context window can handle. Agent teams break the problem into parallel workstreams, each manageable within one agent's context.
Skills are the key differentiator — each agent uses skills matching its role, making it immediately effective without verbose per-task instructions. For more on how skills work, see our complete AI skills guide.
The 5 Agent Roles and Their Skills
Orchestrator Agent
Receives the high-level goal, breaks it into subtasks, assigns work to specialist agents, and synthesizes results. Uses context management skills to track team progress across long sessions.
/plan/coordinate/checkpoint/summarize-sessionResearch Agent
Explores the codebase and gathers relevant context. Produces compressed summaries that other agents can use without consuming their full context window.
/analyze-codebase/summarize-codebase/load-context/researchCode Agent
Writes, modifies, and refactors code. Receives a specific task from the orchestrator with pre-compressed context from the research agent — maximizing its effective context for coding.
/react-component/api-route/refactor/migrateTest Agent
Writes and evaluates tests for the code agent's output. Runs the test suite, reports failures, and suggests fixes. Operates in its own context to avoid contamination from implementation details.
/test-runner/test-frontend/validate/benchmarkReview Agent
Performs the final review of the completed work: code quality, security, performance, and documentation. Provides a go/no-go signal before the orchestrator delivers the result.
/review-pr/security-audit/performance-audit/documentSetting Up a Claude Agent Team
Here's a minimal Claude Code setup for a 3-agent team (orchestrator + code + review):
# Project structure for agent teams
.claude/
skills/
# Orchestrator skills
plan/SKILL.md
coordinate/SKILL.md
summarize-codebase/SKILL.md
# Code agent skills
refactor/SKILL.md
react-component/SKILL.md
# Review agent skills
review-pr/SKILL.md
security-audit/SKILL.md
CLAUDE.md # Orchestration instructions
# Example CLAUDE.md for agent team coordination:
# You are the Orchestrator Agent.
# For complex tasks requiring >200 lines of changes:
# 1. Use /summarize-codebase for context compression
# 2. Spawn a Code Agent with /refactor or specific skills
# 3. Spawn a Review Agent with /review-pr after code is written
# 4. Synthesize results and present to userAgent Skills for Context Engineering
Context engineering — managing what's in each agent's context — is the biggest lever for agent team performance. These skills make it practical:
/compress-contextReduces the current conversation history to ~10% of its original size while preserving all decisions, files modified, and next steps
/summarize-codebaseGenerates a compact (~500 token) map of the entire repository — structure, key exports, architectural patterns — for any agent starting work
/checkpointSaves full session state: open files, decisions made, work completed, and what comes next. Essential for long multi-day projects
/handoffPackages the current agent's work into a structured brief for the next agent — minimizing context transfer overhead
How to Improve, Test, Measure & Refine Agent Skills
The test-measure-refine loop is the systematic method for improving skills from "works sometimes" to "consistent professional quality":
Define the Skill Objective
Write a clear, measurable goal for what the skill should produce.
Example: '/commit should output a conventional commit message in < 10 words that accurately describes the change.'
Write the Initial SKILL.md
Create a first draft covering the trigger conditions, steps, and output format.
Start with the minimum viable skill — don't over-engineer on the first attempt.
Test with 10 Real Inputs
Run the skill against 10 actual use cases from your project. Note where it succeeds and where output quality is low.
Record: expected output vs actual output for each test case in a test-results.md file.
Measure Output Quality
Score each output 1-5 against your objective criteria. Identify the failure patterns.
Common failure modes: too vague, wrong format, triggers when it shouldn't, misses edge cases.
Refine Based on Patterns
Update the SKILL.md to address the specific failure patterns found. Add examples that resolve the most common issues.
One change per iteration — this lets you isolate which improvement caused the quality increase.
Re-test and Iterate
Re-run the same 10 test cases. Score again. If average score improved, keep the change. If not, revert.
Target: consistent 4+/5 score across all 10 test cases before declaring the skill stable.
Frequently Asked Questions
What are Claude agent teams?
Claude agent teams are a multi-agent architecture where multiple Claude instances work in parallel on subtasks of a complex goal. An orchestrator agent breaks down the work and delegates to specialist agents (research, code, test, review) — each with their own context window and specialized skills. This enables tackling tasks too large for a single context window.
How do agent skills work with Claude agent teams?
Each agent in a team has specialized skills that match its role. The orchestrator uses /plan and /coordinate skills. The code agent uses /react-component and /refactor. The test agent uses /test-runner. Skills make each agent immediately productive in its role without verbose instructions in every prompt.
What is context engineering for agents?
Context engineering is the practice of deliberately managing what information each agent has access to. Good context engineering ensures each agent gets the exact information it needs — no more, no less. Key skills include /compress-context (reduce context size), /summarize-codebase (compact repo map), and /checkpoint (save state for resumption).
How do I improve my agent skills?
Follow the test-measure-refine loop: (1) Define clear success criteria, (2) Test on 10 real inputs, (3) Score each output 1-5, (4) Identify failure patterns, (5) Make one targeted fix per iteration, (6) Re-test. Target consistent 4+ scores before shipping the skill to your team.
Can Claude Code run multiple agents simultaneously?
Claude Code supports orchestrated multi-agent workflows where an orchestrator spawns subagents using the Agent tool. Each subagent runs with its own context and assigned tools. This is how complex tasks like 'refactor the entire authentication system' can be broken into parallel workstreams managed by the orchestrator.
Build Your Agent Team Skills Library
Browse orchestration, context engineering, and specialist skills for every agent role in your team at mcpdirectory.app/skills.