Introduction
In the previous Part 3, we explained the basics of defining, registering, and invoking a single Skill. In Part 4, we cover "multi-skill configurations" — combining multiple Skills to autonomously handle more complex tasks — as well as techniques for designing agent flows that include loops and conditional branching.
What Is a Multi-Skill Configuration?
Skills in the Claude Agent SDK are designed not only as standalone "tools" but also as components that can be combined to form pipelines. Where a single Skill handles an individual capability such as "search," "calculation," or "summarization," a multi-skill configuration chains these together, allowing the agent to dynamically decide which Skill to invoke depending on the situation.
This design philosophy resonates with the approach NVIDIA adopted when achieving first place on the DABStep benchmark. In their NeMo Agent Toolkit, the agent autonomously generated and combined multiple reusable tools — such as data exploration, hypothesis generation, and code execution — to handle complex data analysis tasks [Source: https://huggingface.co/blog/nvidia/nemo-agent-toolkit-data-explorer-dabstep-1st-place]. The same concept of "dynamic combination of reusable modules" is central to Claude Skills as well.
Basic Structure for Registering Skills and Making Multiple Calls
In the Claude Agent SDK, when assigning multiple Skills to an agent, they are passed as a list to the skills parameter.
from anthropic import Anthropic client = Anthropic() response = client.beta.messages.create( model="claude-opus-4-5", max_tokens=4096, skills=[ search_skill, summarize_skill, store_skill ], messages=[{"role": "user", "content": "Please look up the latest LLM papers, summarize them, and save them to storage."}] ) Claude autonomously selects from these three Skills whichever are necessary to solve the task, determines the order, and invokes them. The key point is that the invocation order is not explicitly specified by a human — it is determined by the model within its own reasoning process.
Implementing the Agent Loop
To truly leverage a multi-skill configuration, you need to implement an agent loop rather than a one-shot API call. A loop refers to the pattern of continuously feeding back results as tool_result and connecting them to the next reasoning step, for as long as Claude returns tool_use blocks.
import json def run_agent(client, skills, initial_message): messages = [{"role": "user", "content": initial_message}] while True: response = client.beta.messages.create( model="claude-opus-4-5", max_tokens=4096, skills=skills, messages=messages ) # If stop_reason is end_turn, exit the loop if response.stop_reason == "end_turn": return response.content # Extract and execute tool_use blocks tool_results = [] for block in response.content: if block.type == "tool_use": result = dispatch_skill(block.name, block.input) tool_results.append({ "type": "tool_result", "tool_use_id": block.id, "content": json.dumps(result) }) # Append the assistant's response and tool results to messages messages.append({"role": "assistant", "content": response.content}) messages.append({"role": "user", "content": tool_results}) This loop structure is conceptually similar to the "experience collection -> update -> re-collection" cycle in asynchronous reinforcement learning training, and a common underlying design principle exists whereby an agent updates its actions based on feedback received from the environment [Source: https://huggingface.co/blog/async-rl-training-landscape].
Conditional Branching Design Patterns
Conditional branching in agent flows can be implemented primarily at two levels.
1. Branching via Claude's Internal Reasoning A method of describing conditional logic in the system prompt and allowing Claude to autonomously decide which Skill to invoke. For example, an instruction such as "if the search results are empty, invoke the alternative data source Skill" can be included in the system prompt.
2. Branching via Host-Side Code A method of evaluating tool_use results inside the dispatch_skill function and dynamically changing the next message content or Skill list using Python-side if statements. In situations where more deterministic control is required, this approach is superior in both safety and debuggability.
In practical systems, an architecture that combines both of these is common. It is a hybrid design that leverages Claude's high reasoning capability while keeping the strict parts of the business logic under host-side control.
Best Practices for Multi-Skill Design
- Keep each Skill's responsibility singular: If a single Skill has multiple side effects, the agent's reasoning becomes unstable
- Define input/output schemas clearly: Type definitions via JSON Schema encourage Claude to generate parameters accurately
- Explicitly state loop termination conditions: Including a "definition of task completion" in the system prompt prevents infinite loops
- Handle errors in tool results: Include error status in
tool_resultso that Claude can recognize anomalies and attempt recovery
Preview of the Next Part
In Part 5, we will take a deeper dive into error handling touched on in this article, covering robust fallback strategies for Skill execution failures, timeouts, and invalid inputs, as well as how to implement debugging and observability in production environments.
Category: LLM | Tags: Claude, AgentSkill, LLM, エージェント設計, マルチスキル
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