SuperAPI AI Directory

genai-hub-foundation-models

Agents & models

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Updated 10-16 01:27

About this skill

Rules

  1. Fetch the live model list when the model name is unknown or unconfirmed — skip if the user already named a specific model.
  2. executable-id and model-name values from this skill are the inputs needed by aicore-lifecycle-management to create a deployment.
  3. If credentials are not configured, invoke aicore-admin-resources first.
  4. Before executing any curl example, get a Bearer token with:
    export TOKEN=$(uv run skills/aicore-admin-resources/scripts/get_token.py)
    
    Never add 2>&1 or pipe the command — that prints the raw JWT into the conversation.

List Available Models

# Show all options and examples:
uv run scripts/list_foundation_models.py --help
One provider only (e.g. Azure OpenAI):

uv run scripts/list_foundation_models.py --executable-id azure-openai

Detailed view (versions, capabilities, context length, streaming support):

uv run scripts/list_foundation_models.py --response-format detailed

Machine-readable JSON for scripting:

uv run scripts/list_foundation_models.py --json | jq '.[].model' uv run scripts/list_foundation_models.py --response-format detailed --json | jq '.[] | select(.model == "gpt-4o") | .versions'

Models supported by the Orchestration service only:

uv run scripts/list_foundation_models.py --scenario-id orchestration


---

## Provider → Executable ID Mapping

| Provider       | `--executable-id`   |
| -------------- | ------------------- |
| Azure OpenAI   | `azure-openai`      |
| Anthropic      | `aws-bedrock`       |
| Google         | `gcp-vertexai`      |
| Amazon Bedrock | `aws-bedrock`       |
| Mistral AI     | `aicore-mistralai`  |
| Cohere         | `aicore-cohere`     |
| Perplexity     | `perplexity-ai`     |
| NVIDIA         | `aicore-nvidia`     |
| Open Source    | `aicore-opensource` |
| SAP            | `aicore-sap`        |

Supported models in your instance may differ from this table — use the script to get the live list.

---

## Create a Deployment

Once you have the `executable-id` and `model-name`, check whether a RUNNING deployment for that model already exists:

```bash
uv run skills/aicore-lifecycle-management/scripts/get_deployments.py \
  --scenario foundation-models --status RUNNING --json

If one is found, use AskUserQuestion to ask whether to reuse it or create a new one. Then invoke aicore-lifecycle-management with both values.

The modelName value must exactly match the catalog output — a mismatch causes an "Invalid Configuration" error at deployment time, not at configuration creation.


When to Load Reference Files

Trigger Load
Curl inference examples, provider-specific endpoint paths, or about to call a deployed model references/MODELS.md

Finding the Deployment URL for a Curl Call

  1. Get the executable-id — look it up in the Provider → Executable ID table above.

  2. Find the running deployment:

    uv run skills/aicore-lifecycle-management/scripts/get_deployments.py \
      --scenario foundation-models --executable-id <executable-id> --status RUNNING --json
    # use .deployment_url and .resource_group from the matching entry
    
  3. See references/MODELS.md for export setup and curl examples per provider.


Handoffs

  • Credential/auth error? → invoke aicore-admin-resources
  • Ready to create a deployment with the model you found? → invoke aicore-lifecycle-management