Subagent Orchestration in Claude Skills: Implementation Guide and Examples

Subagent orchestration in Claude Skills enables complex workflow coordination by allowing a parent skill to delegate tasks to specialized sub-agents through the SubAgentOrchestrator class, defined in skill.json manifests and implemented in the ComposioHQ/awesome-claude-skills repository.

Claude Skills are composable units that extend Claude's capabilities beyond single-prompt interactions. When workflows become too complex for monolithic implementation, subagent orchestration allows developers to break tasks into discrete, manageable components that share state and execute sequentially. This pattern is implemented in the ComposioHQ/awesome-claude-skills repository through a dedicated orchestration layer that manages invocation, data flow, and error handling.

How Subagent Orchestration Works

The orchestration layer coordinates multiple specialized agents through four distinct stages: definition, invocation, data transformation, and error management. Each stage is implemented through specific classes and configuration files in the skill architecture.

Declaring Subagents in the Skill Manifest

Each skill defines its sub-agents in a skill.json manifest file located at the skill root. The manifest includes a subagents array that specifies each sub-skill name and its required parameters.

{
  "name": "contract-assistant",
  "description": "Creates and summarizes contracts",
  "subagents": [
    {
      "name": "contract-draft-generator",
      "params": { "template": "nda" }
    },
    {
      "name": "clause-extractor",
      "params": { "sections": ["confidentiality", "termination"] }
    },
    {
      "name": "summary-writer",
      "params": {}
    }
  ]
}

Initializing the SubAgentOrchestrator

The SubAgentOrchestrator class, implemented in skill_core/orchestrator.py, serves as the central coordinator. It loads skill definitions and manages the execution lifecycle of all declared sub-agents.

from skill_core.orchestrator import SubAgentOrchestrator

# Load the top-level skill definition

orchestrator = SubAgentOrchestrator.from_file(
    "skills/contract-assistant/skill.json"
)

# Run the whole workflow with a single higher-level call

result = orchestrator.run(
    input_data={"client": "Acme Corp", "partner": "Beta Ltd"}
)

print(result["summary"])

Context Passing and Data Flow

The orchestrator maintains a shared context dictionary that propagates data between sub-agents. When contract-draft-generator completes execution, its return value automatically becomes available to subsequent agents like clause-extractor.

def contract_draft_generator(params, context):
    template = params.get("template", "generic")
    # Generate a draft using Claude's text-generation capability

    draft = claude.complete(
        prompt=f"Write a {template.upper()} contract between {context['client']} and {context['partner']}."
    )
    return {"draft": draft}

Results from each sub-agent are captured, optionally transformed, and passed back to the orchestrator, which assembles the final response.

Error Handling and Retry Logic

The orchestrator tracks the status of every sub-agent call through the execution pipeline. If a sub-agent fails, the system can retry the operation, fallback to a default implementation, or abort the entire workflow with a structured error message. This resilience pattern ensures that complex multi-step workflows fail gracefully or self-heal when possible.

Key Source Files in the Architecture

Understanding the file structure helps developers navigate the implementation of subagent orchestration within the repository.

• skill_core/orchestrator.py – Contains the SubAgentOrchestrator class that implements core logic for loading sub-agents, invoking Claude, handling results, and managing retries.

• skill-creator/scripts/package_skill.py – Packages a skill including its sub-agent definitions, creating the distributable bundle that the orchestrator reads at runtime.

• skills/contract-assistant/skill.json – Sample manifest demonstrating how a top-level skill lists its sub-agents and passes initial parameters.

• skills/contract-assistant/subagents/contract-draft-generator.py – Example sub-agent implementation that generates contract drafts; references the shared context for dynamic content generation.

• skills/contract-assistant/subagents/clause-extractor.py – Example sub-agent that receives the draft from the previous step and extracts specific legal clauses, demonstrating sequential data dependency.

Practical Benefits of Subagent Orchestration

This architectural pattern provides specific advantages for complex Claude implementations. Workflow decomposition allows developers to isolate concerns such as drafting, extraction, and summarization into testable units. State management happens automatically through the orchestrator's context passing, eliminating manual data marshaling between steps. Modular deployment enables teams to update individual sub-agents without redeploying entire skill suites, as defined in the packaging logic of package_skill.py.

Summary

• Subagent orchestration breaks complex Claude workflows into manageable, discrete skills coordinated by the SubAgentOrchestrator class.
• Configuration occurs in skill.json manifests located in each skill directory, specifying the subagents array and their parameters.
• The orchestrator in skill_core/orchestrator.py handles execution order, context sharing between agents, and error recovery strategies.
• Data flows automatically from one sub-agent to the next through a shared context dictionary, enabling sequential processing pipelines.
• The package_skill.py script bundles sub-agent definitions for distribution and runtime loading.

Frequently Asked Questions

What is the difference between a Claude Skill and a sub-agent?

A Claude Skill is a complete, deployable unit that appears in the skill registry and handles high-level user requests. A sub-agent is a specialized function or module that a skill calls to perform a specific task, such as data extraction or text generation. Sub-agents are not exposed directly to users; instead, the parent skill orchestrates them through the SubAgentOrchestrator to complete complex workflows.

How does data pass between sub-agents in a workflow?

The SubAgentOrchestrator maintains a shared context dictionary that persists throughout the execution lifecycle. When a sub-agent function returns a result, the orchestrator merges that data into the context object. Subsequent sub-agents receive this updated context as a parameter, allowing them to access outputs from previous steps, as demonstrated in the contract-draft-generator.py to clause-extractor.py handoff.

Can sub-agent orchestration handle failures in individual steps?

Yes, the orchestration layer includes robust error handling and retry mechanisms. As implemented in skill_core/orchestrator.py, the system tracks the status of each sub-agent invocation. If a step fails, the orchestrator can retry the operation based on configuration, execute a fallback implementation, or terminate the workflow and return a structured error to the parent skill.

Where are sub-agent definitions stored in the repository?

Sub-agent definitions are declared in the parent skill's skill.json manifest file, typically located at skills/{skill-name}/skill.json. The actual sub-agent implementations reside in a subagents/ subdirectory within the skill folder, such as skills/contract-assistant/subagents/. The package_skill.py script processes these locations when creating distributable skill bundles.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}