Overview

This skill is the canonical procedure for adding a new LLM provider, model, or inference profile to a Vellum assistant. Follow the steps in order — each step's output feeds the next, and the final live-call verification is mandatory. Never skip ahead by writing raw config JSON.

Step 0 — Check what's already available (avoid collecting keys unnecessarily)

Managed (platform-credentialed) connections may already cover the user's need — no API key required:

assistant inference providers connections list
assistant inference providers default        # default provider + availability status
assistant inference profiles list           # effective profiles: managed + user, with availability

If a managed connection for the desired provider exists and is available, skip to Step 3 (create a profile against it). Only collect an API key when the user wants a provider/tier the managed connections don't cover, or explicitly wants to use their own key.

Step 1 — Reuse an existing key, or securely collect a new one

Before prompting the user for anything, check whether a suitable key is already stored:

assistant credentials list

If a credential for the target provider exists, reuse it — reference it by vault path in Step 2 and skip the prompt. Only collect a new key when none exists (or the user explicitly wants to replace it).

Never ask for secrets in chat. The key must not enter the conversation. Use the secure prompt:

assistant credentials prompt --service <provider> --field api_key \
  --label "<Provider> API Key" --placeholder "sk-..."

Exit code 0 = stored; exit code 130 = the user dismissed the prompt (a valid choice, not an error — ask whether they want to try again or stop). Any other non-zero exit is a real error.

Step 2 — Create the provider connection

Reference the stored credential by vault path — the assistant only ever handles the reference string:

assistant inference providers connections create <connection-name> \
  --provider <provider> \
  --auth api_key \
  --credential credential/<provider>/api_key

For self-hosted or OpenAI-compatible endpoints, use --provider openai-compatible and supply the endpoint's base URL plus at least one model id (both are required for this provider type — the endpoint advertises no fixed catalog). Pass --model once per model the endpoint serves:

assistant inference providers connections create <connection-name> \
  --provider openai-compatible \
  --auth api_key \
  --credential credential/<provider>/api_key \
  --base-url https://<host>/v1 \
  --model <model-id> \
  --model <another-model-id>

For a local, keyless endpoint (e.g. LM Studio, vLLM) use --auth none and drop --credential. The managed Vellum connection is not editable — create a new named connection instead of modifying it.

Step 3 — Discover a valid model id (do not guess)

Model ids are the most common failure point — never write one from memory:

assistant inference models list --provider <provider>

Pick from the catalog output. If the user wants a model not in the catalog (e.g. brand new or self-hosted), probe it with a live call before configuring anything:

assistant inference send --model <candidate-id> --max-tokens 32 "Reply with OK"

Step 4 — Create the profile

assistant inference profiles create <profile-name> \
  --provider <provider> \
  --model <model-id> \
  --connection <connection-name> \
  --label "<Display Name>"

Creation validates the provider, model id (against the catalog — pass --allow-unlisted only for a model you already probed in Step 3), and connection existence. Optional tuning flags: --effort, --max-tokens, --temperature, --thinking on|off.

Step 5 — Verify with a live call (mandatory)

Prove the whole chain — credential, connection, provider routing, model id — with one real call:

assistant inference send --profile <profile-name> --max-tokens 32 --json "Reply with OK"

If this fails, fix the profile before telling the user it is set up. Common failures: wrong model id (provider 4xx — go back to Step 3), missing/mistyped credential reference (auth error — check assistant credentials list), connection name typo (assistant inference providers connections get <name>).

Step 6 — Put it to use

• Make it the chat model: assistant inference profiles active <profile-name>
• Use it for one conversation: assistant inference session open <profile-name> --ttl 30m
• Pin a specific background task to it: see the llm-cost-optimizer skill for call-site pinning and cost trade-offs before pinning anything.

Reference: inspection commands

assistant inference profiles list [--json]      # effective profile catalog + availability
assistant inference profiles get <name>
assistant inference callsites list [--json]     # which profile each call site resolves to, default vs pinned
assistant inference callsites get <site>        # full resolution chain for one call site
assistant credentials list                       # stored credential names (never values)