Azure AI Projects Python SDK (Foundry SDK) workflow skill. Use this skill when the user needs Build AI applications on Microsoft Foundry using the azure-ai-projects SDK and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Azure AI Projects Python SDK (Foundry SDK) workflow skill. Use this skill when the user needs Build AI applications on Microsoft Foundry using the azure-ai-projects SDK and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/azure-ai-projects-py from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Azure AI Projects Python SDK (Foundry SDK) Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Authentication, Client Operations Overview, Two Client Approaches, Agent Operations, Tools Overview.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
This skill is applicable to execute the workflow or actions described in the overview.
Use when the request clearly matches the imported source intent: Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Use when copied upstream references, examples, or scripts materially improve the answer.
Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
bash pip install azure-ai-projects azure-identity
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Use @azure-ai-projects-py to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @azure-ai-projects-py against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @azure-ai-projects-py for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @azure-ai-projects-py using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Use context managers for async client: async with AIProjectClient(...) as client:
Clean up agents when done: client.agents.delete_agent(agent.id)
Use createandprocess for simple runs, streaming for real-time UX
Use versioned agents for production deployments
Prefer connections for external service integration (AI Search, Bing, etc.)
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Imported Operating Notes
Imported: Best Practices
Use context managers for async client: async with AIProjectClient(...) as client:
Clean up agents when done: client.agents.delete_agent(agent.id)
Use create_and_process for simple runs, streaming for real-time UX
Use versioned agents for production deployments
Prefer connections for external service integration (AI Search, Bing, etc.)
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/azure-ai-projects-py, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: Datasets and Indexes
# List datasets
datasets = client.datasets.list()
# List indexes
indexes = client.indexes.list()
See references/datasets-indexes.md for data operations.
Imported: Reference Files
references/agents.md: Agent operations with PromptAgentDefinition
references/tools.md: All agent tools with examples
references/custom-evaluators.md: Code and prompt-based evaluator patterns
references/connections.md: Connection operations
references/deployments.md: Deployment enumeration
references/datasets-indexes.md: Dataset and index operations
references/async-patterns.md: Async client usage
references/api-reference.md: Complete API reference for all 373 SDK exports (v2.0.0b4)
scripts/run_batch_evaluation.py: CLI tool for batch evaluations
Imported: Authentication
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
credential = DefaultAzureCredential()
client = AIProjectClient(
endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
credential=credential,
)
Imported: Client Operations Overview
Operation
Access
Purpose
client.agents
.agents.*
Agent CRUD, versions, threads, runs
client.connections
.connections.*
List/get project connections
client.deployments
.deployments.*
List model deployments
client.datasets
.datasets.*
Dataset management
client.indexes
.indexes.*
Index management
client.evaluations
.evaluations.*
Run evaluations
client.red_teams
.red_teams.*
Red team operations
Imported: Two Client Approaches
1. AIProjectClient (Native Foundry)
from azure.ai.projects import AIProjectClient
client = AIProjectClient(
endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
# Use Foundry-native operations
agent = client.agents.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
name="my-agent",
instructions="You are helpful.",
)
2. OpenAI-Compatible Client
# Get OpenAI-compatible client from project
openai_client = client.get_openai_client()
# Use standard OpenAI API
response = openai_client.chat.completions.create(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
messages=[{"role": "user", "content": "Hello!"}],
)
Imported: Agent Operations
Create Agent (Basic)
agent = client.agents.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
name="my-agent",
instructions="You are a helpful assistant.",
)
Create Agent with Tools
from azure.ai.agents import CodeInterpreterTool, FileSearchTool
agent = client.agents.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
name="tool-agent",
instructions="You can execute code and search files.",
tools=[CodeInterpreterTool(), FileSearchTool()],
)
Versioned Agents with PromptAgentDefinition
from azure.ai.projects.models import PromptAgentDefinition
# Create a versioned agent
agent_version = client.agents.create_version(
agent_name="customer-support-agent",
definition=PromptAgentDefinition(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
instructions="You are a customer support specialist.",
tools=[], # Add tools as needed
),
version_label="v1.0",
)
See references/agents.md for detailed agent patterns.
Imported: Tools Overview
Tool
Class
Use Case
Code Interpreter
CodeInterpreterTool
Execute Python, generate files
File Search
FileSearchTool
RAG over uploaded documents
Bing Grounding
BingGroundingTool
Web search (requires connection)
Azure AI Search
AzureAISearchTool
Search your indexes
Function Calling
FunctionTool
Call your Python functions
OpenAPI
OpenApiTool
Call REST APIs
MCP
McpTool
Model Context Protocol servers
Memory Search
MemorySearchTool
Search agent memory stores
SharePoint
SharepointGroundingTool
Search SharePoint content
See references/tools.md for all tool patterns.
Imported: Thread and Message Flow
# 1. Create thread
thread = client.agents.threads.create()
# 2. Add message
client.agents.messages.create(
thread_id=thread.id,
role="user",
content="What's the weather like?",
)
# 3. Create and process run
run = client.agents.runs.create_and_process(
thread_id=thread.id,
agent_id=agent.id,
)
# 4. Get responseif run.status == "completed":
messages = client.agents.messages.list(thread_id=thread.id)
for msg in messages:
if msg.role == "assistant":
print(msg.content[0].text.value)
Imported: Connections
# List all connections
connections = client.connections.list()
for conn in connections:
print(f"{conn.name}: {conn.connection_type}")
# Get specific connection
connection = client.connections.get(connection_name="my-search-connection")
See references/connections.md for connection patterns.
Imported: Deployments
# List available model deployments
deployments = client.deployments.list()
for deployment in deployments:
print(f"{deployment.name}: {deployment.model}")
See references/deployments.md for deployment patterns.
