Large Language Models (LLMs) have revolutionized text interaction, but their utility in complex operational environments is often limited by their inability to directly interact with the external world. Imagine an LLM that, instead of just responding with textual information learned during training, could query a database, call an API for real-time data, or execute a script on a server. This capability transforms a simple chatbot into an autonomous, proactive agent. I’ve seen firsthand how integrating these capabilities can boost efficiency: in an environment with 2,000 endpoints, an LLM-powered agent with CMDB query skills significantly reduced initial alert triage time by providing immediate context. Anthropic‘s ‘Skills‘ framework is a powerful tool to realize this vision, allowing LLMs to extend their capabilities far beyond text.
Tested on: Python 3.10 · Anthropic Claude API · August 2026
Prerequisites / Test Environment
To implement and test ‘Skills’ with an Anthropic LLM, you will need:
- A Python environment (version 3.8 or higher).
- A valid API key for Anthropic’s Claude models (e.g.,
claude-3-opus-20240229). - The
anthropiclibrary installed (pip install anthropic). - Familiarity with defining Python functions and basic REST API interaction to simulate ‘external tools’.
1. Defining Skills: The LLM’s Toolset
The first step is to define the ‘skills’ your LLM can use. Each skill is essentially a Python function that performs a specific action or retrieves data. It’s crucial to clearly describe the function’s purpose, its parameters, and the expected output. This description (usually in JSON Schema format) is what the LLM will use to decide if and how to invoke the skill.
Let’s say we want to equip our LLM with the ability to query an internal ticketing system for a ticket’s status.
import anthropic
# Simulate a ticketing system
def get_ticket_status(ticket_id: str) -> dict:
"""
Retrieves the current status of a ticket from the management system.
:param ticket_id: The unique ID of the ticket.
:return: A dictionary with the ticket status and assignee.
"""
if ticket_id == "INC-2026-001":
return {"id": ticket_id, "status": "In Progress", "assigned_to": "Rosario Giordano"}
elif ticket_id == "INC-2026-002":
return {"id": ticket_id, "status": "Resolved", "assigned_to": "Support Team"}
else:
return {"id": ticket_id, "status": "Not Found", "assigned_to": None}
# Skill definition for the LLM (JSON Schema)
SKILL_DEFINITIONS = [
{
"name": "get_ticket_status",
"description": "Retrieves the status and assignee of a support ticket.",
"input_schema": {
"type": "object",
"properties": {
"ticket_id": {
"type": "string",
"description": "The unique ID of the ticket (e.g., INC-2026-001)"
}
},
"required": ["ticket_id"]
}
}
]2. Invoking the LLM with Skills
Once skills are defined, it’s time to pass these definitions to the LLM. When making a call to the Anthropic API, you include the SKILL_DEFINITIONS in the tools parameter. The LLM, based on the received prompt, will autonomously decide whether and which skill to invoke, providing the necessary parameters.
client = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_API_KEY")
def chat_with_llm_with_skills(user_message: str):
response = client.messages.create(
model="claude-3-opus-20240229", # Choose a model capable of tool use
max_tokens=1024,
tools=SKILL_DEFINITIONS,
messages=[
{"role": "user", "content": user_message}
]
)
# Handle the LLM's response
if response.stop_reason == "tool_use":
tool_call = response.content[0]
tool_name = tool_call.name
tool_input = tool_call.input
print(f"LLM requested to use skill: {tool_name} with input: {tool_input}")
# Execute the requested skill
if tool_name == "get_ticket_status":
result = get_ticket_status(tool_input["ticket_id"])
print(f"Skill result: {result}")
# Send the skill result back to the LLM to generate the final response
final_response = client.messages.create(
model="claude-3-opus-20240229",
max_tokens=1024,
messages=[
{"role": "user", "content": user_message},
{"role": "tool_use", "tool_call_id": tool_call.id, "tool_name": tool_name, "content": str(result)}
]
)
print(f"Final LLM response: {final_response.content[0].text}")
else:
print(f"Direct LLM response: {response.content[0].text}")
# Example usage
# chat_with_llm_with_skills("What is the status of ticket INC-2026-001?")
# chat_with_llm_with_skills("Tell me a joke.")Read also: PrimeAgent: AI Agent for IT Automation
3. Orchestration and Result Management
The interaction process with skills is often a cycle: the user makes a request, the LLM decides to use a skill, the system executes the skill, the result is transmitted back to the LLM, which then generates the final response or decides to invoke another skill. This orchestration is crucial for complex workflows. It’s important to correctly handle errors that may arise from skill execution (e.g., API unavailable, incorrect parameters) and communicate these errors to the LLM for robust handling.
Consider encapsulating this cycle in a helper function that manages the call and response logic. In an enterprise environment, you might want to add detailed logging of each skill invocation and its results for auditing and debugging. Read also: Agency Agents: Orchestrating AI for Complex Tasks
Common Errors and Troubleshooting
- LLM does not invoke the skill: Ensure the skill’s
descriptionis clear and theinput_schemais well-defined. The LLM relies on these descriptions to understand when and how to use the skill. Make the user prompt explicit in its intent to use the skill. - Error in skill parameters: If the LLM sends incorrect parameters, verify that the
input_schemais correct and that examples provided in the description are relevant. Fine-tuning descriptions might be necessary to better guide the LLM. - Timeout or errors in skill execution: Functions implementing skills should be robust and handle their own errors. If an external API fails, the result returned to the LLM should indicate the error so the LLM can inform the user or attempt an alternative action. Read also: ACN Cloud: Reading the Service Qualification Catalog
FAQ
What types of tools can I integrate as ‘skills’?
Virtually any tool that can be called from Python code: REST APIs, databases (SQL/NoSQL), local scripts, cloud services, monitoring systems, automation tools like Ansible, and so on. The key is that the skill is a well-defined function with clear input and output.
Is it safe to give LLMs access to external systems?
Security is a fundamental consideration. Each skill should be designed with the principle of least privilege. The LLM should not have direct, uncontrolled access; rather, skills should be secure wrappers that perform pre-authorized actions. It’s advisable to implement granular access controls and logging for every action performed via a skill.
Can I combine multiple ‘skills’ in a single workflow?
Yes, this is one of the great potentials. An LLM can sequentially decide to invoke multiple skills to complete a complex task. For example, it might first use a skill to retrieve information, then another skill to process it, and finally a third skill to send a notification with the result. The orchestrator (your Python code) will manage the feedback loop between the LLM and skills.
Which Anthropic models support ‘skills’ (tool use)?
Generally, Anthropic’s more advanced models, such as the Claude 3 family (Opus, Sonnet, Haiku), are optimized for ‘tool use’ and ‘skills’. It’s always advisable to consult Anthropic’s official documentation for an updated list of models and their capabilities.
Conclusions with Operational Takeaways
Anthropic’s ‘Skills’ framework represents a qualitative leap in LLM utilization, transforming them from mere text generators into true operational agents. The ability to interact with external tools paves the way for smarter, more contextual automation. The operational takeaways are clear: start by defining your external interaction needs, design modular and well-described skills, and implement robust orchestration and error management mechanisms. Remember that security is paramount: each skill must be a controlled and monitored gateway to your systems.
Sources
Updated: August 2026
