Azure AI Projects SDK for TypeScript
Overview
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/azure-ai-projects-ts 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 SDK for TypeScript High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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, Operation Groups, Getting OpenAI Client, Agents, Connections.
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: High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
- 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 npm install @azure/ai-projects @azure/identity For tracing: bash npm install @azure/monitor-opentelemetry @opentelemetry/api
- 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.
Imported Workflow Notes
Imported: Installation
npm install @azure/ai-projects @azure/identity
For tracing:
npm install @azure/monitor-opentelemetry @opentelemetry/api
Imported: Environment Variables
AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o
Examples
Example 1: Ask for the upstream workflow directly
Use @azure-ai-projects-ts 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-ts 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-ts 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-ts 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 getOpenAIClient() - For responses, conversations, files, and vector stores
- Version your agents - Use createVersion for reproducible agent definitions
- Clean up resources - Delete agents, conversations when done
- Use connections - Get credentials from project connections, don't hardcode
- Filter deployments - Use modelPublisher filter to find specific models
- 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 getOpenAIClient() - For responses, conversations, files, and vector stores
- Version your agents - Use
createVersion for reproducible agent definitions
- Clean up resources - Delete agents, conversations when done
- Use connections - Get credentials from project connections, don't hardcode
- Filter deployments - Use
modelPublisher filter to find specific models
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-ts, 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: Indexes
import { AzureAISearchIndex } from "@azure/ai-projects";
const indexConfig: AzureAISearchIndex = {
name: "my-index",
type: "AzureSearch",
version: "1",
indexName: "my-index",
connectionName: "search-connection"
};
const index = await client.indexes.createOrUpdate("my-index", "1", indexConfig);
for await (const idx of client.indexes.list()) {
console.log(idx.name);
}
await client.indexes.delete("my-index", "1");
Imported: Authentication
import { AIProjectClient } from "@azure/ai-projects";
import { DefaultAzureCredential } from "@azure/identity";
const client = new AIProjectClient(
process.env.AZURE_AI_PROJECT_ENDPOINT!,
new DefaultAzureCredential()
);
Imported: Operation Groups
| Group | Purpose |
|---|
client.agents | Create and manage AI agents |
client.connections | List connected Azure resources |
client.deployments | List model deployments |
client.datasets | Upload and manage datasets |
client.indexes | Create and manage search indexes |
client.evaluators | Manage evaluation metrics |
client.memoryStores | Manage agent memory |
Imported: Getting OpenAI Client
const openAIClient = await client.getOpenAIClient();
const response = await openAIClient.responses.create({
model: "gpt-4o",
input: "What is the capital of France?"
});
const conversation = await openAIClient.conversations.create({
items: [{ type: "message", role: "user", content: "Hello!" }]
});
Imported: Agents
Create Agent
const agent = await client.agents.createVersion("my-agent", {
kind: "prompt",
model: "gpt-4o",
instructions: "You are a helpful assistant."
});
Agent with Tools
const agent = await client.agents.createVersion("code-agent", {
kind: "prompt",
model: "gpt-4o",
instructions: "You can execute code.",
tools: [{ type: "code_interpreter", container: { type: "auto" } }]
});
const agent = await client.agents.createVersion("search-agent", {
kind: "prompt",
model: "gpt-4o",
tools: [{ type: "file_search", vector_store_ids: [vectorStoreId] }]
});
const agent = await client.agents.createVersion("web-agent", {
kind: "prompt",
model: "gpt-4o",
tools: [{
type: "web_search_preview",
user_location: { type: "approximate", country: "US", city: "Seattle" }
}]
});
agent = client..(, {
: ,
: ,
: [{
: ,
: {
: [{
: connectionId,
: ,
:
}]
}
}]
});
agent = client..(, {
: ,
: ,
: [{
: ,
: {
: ,
: ,
: ,
: {
: ,
: { : { : } },
: []
}
}
}]
});
agent = client..(, {
: ,
: ,
: [{
: ,
: ,
: ,
:
}]
});
Run Agent
const openAIClient = await client.getOpenAIClient();
const conversation = await openAIClient.conversations.create({
items: [{ type: "message", role: "user", content: "Hello!" }]
});
const response = await openAIClient.responses.create(
{ conversation: conversation.id },
{ body: { agent: { name: agent.name, type: "agent_reference" } } }
);
await openAIClient.conversations.delete(conversation.id);
await client.agents.deleteVersion(agent.name, agent.version);
Imported: Connections
for await (const conn of client.connections.list()) {
console.log(conn.name, conn.type);
}
const conn = await client.connections.get("my-connection");
const connWithCreds = await client.connections.getWithCredentials("my-connection");
const defaultAzureOpenAI = await client.connections.getDefault("AzureOpenAI", true);
Imported: Deployments
for await (const deployment of client.deployments.list()) {
if (deployment.type === "ModelDeployment") {
console.log(deployment.name, deployment.modelName);
}
}
for await (const d of client.deployments.list({ modelPublisher: "OpenAI" })) {
console.log(d.name);
}
const deployment = await client.deployments.get("gpt-4o");
Imported: Datasets
const dataset = await client.datasets.uploadFile(
"my-dataset",
"1.0",
"./data/training.jsonl"
);
const dataset = await client.datasets.uploadFolder(
"my-dataset",
"2.0",
"./data/documents/"
);
const ds = await client.datasets.get("my-dataset", "1.0");
for await (const version of client.datasets.listVersions("my-dataset")) {
console.log(version);
}
await client.datasets.delete("my-dataset", "1.0");
Imported: Key Types
import {
AIProjectClient,
AIProjectClientOptionalParams,
Connection,
ModelDeployment,
DatasetVersionUnion,
AzureAISearchIndex
} from "@azure/ai-projects";
Imported: Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
