agent-creator

name	agent-creator
description	Create a new Microsoft Agent Framework agent from a natural-language description or manual instruction edits. Use when the user wants to build a new AI agent, modify an existing agent's instructions, or generate an agent configuration. Outputs a validated agent definition (YAML + Python code) that can be deployed to Azure AI Foundry.
license	MIT
compatibility	Requires python3, agent-framework SDK
metadata	{"author":"poc-team","version":"1.0"}
allowed-tools	load_skill read_skill_resource run_skill_script

Agent Creator Skill

Purpose

This skill enables an orchestrator agent to create and validate Microsoft Agent Framework agent definitions from either:

1. Natural-language prompts – The user describes the agent they want in plain language, analogous to creating a GPT in ChatGPT.
2. Manual instruction editing – The user provides explicit instruction text, tool lists, and model parameters.

Workflow

Step 1 – Gather Requirements

Collect the following from the user or from the orchestrator context:

Field	Required	Default	Description
name	yes	—	Unique snake_case agent name (max 64 chars)
description	yes	—	One-line description of the agent
instructions	yes	—	System prompt / instruction text for the agent
model	no	gpt-4o	Azure OpenAI deployment name
tools	no	[]	List of tools the agent may call
context_providers	no	[]	Context providers (e.g., SkillsProvider)
temperature	no	0.7	Sampling temperature

Step 2 – Generate Agent Definition

Produce two artefacts:

1. agent.yaml – declarative agent configuration:

name: <name>
description: <description>
model: <model>
instructions: |
  <instructions>
tools: <tools>
temperature: <temperature>

2. agent.py – executable Python code using agent_framework:

from agent_framework import AzureOpenAIChatClient
from azure.identity.aio import DefaultAzureCredential

agent = AzureOpenAIChatClient(
    credential=DefaultAzureCredential(),
    endpoint="<AZURE_OPENAI_ENDPOINT>",
    deployment="<model>",
).as_agent(
    name="<name>",
    instructions="""<instructions>""",
)

Step 3 – Validate

Run the validate_agent script to check:

• YAML is well-formed
• Required fields present
• agent.py has no syntax errors
• Instructions length < 32,000 chars

If validation fails, report the errors and suggest fixes.

Step 4 – Persist

Save both files to generated/agents/<name>/.

Natural Language → Agent Generation

When the user provides a natural-language description like "I want an agent that helps with expense reports", generate appropriate:

1. Name – derive from the description (e.g., expense_report_assistant)
2. Instructions – craft detailed system prompt capturing expertise, tone, constraints, and response format
3. Tools – suggest relevant tools based on the domain
4. Description – concise summary

Quality Guidelines

• Instructions should be 200–2000 characters
• Be specific about the agent's domain and boundaries
• Include output format guidance where applicable
• Add safety guardrails (out-of-scope handling, refusal policies)