embed

name	embed
description	Receives a text question and returns its embedding vector using gemini-embedding-001 via Vertex AI. Use this when you need a semantic embedding for a question — for downstream vector search, similarity comparison, or passing to another tool.

Embed Skill

Embeds a text question using gemini-embedding-001 (Vertex AI, project mozdata) and returns the resulting vector as a JSON array.

Usage

python "${CLAUDE_PLUGIN_ROOT:?set by the plugin system; if empty, invoke this via the Skill tool}"/skills/embed/scripts/embed.py --question "<your question>"

`` Output is a JSON array of 3072 floats printed to stdout:

[0.012, -0.034, 0.091, ...]

Steps

1. Run the script with the user's question as --question.
2. Capture stdout — that is the embedding vector.
3. Return or pass the vector as needed (e.g. to the vector-search skill).

Authentication

This skill connects to Vertex AI using a service account — the cloud-platform scope is enforced on the impersonated token in code. Just log in:

gcloud auth application-default login

Your authenticated Google Cloud credentials are only used to create a new and temporary access token on behalf of the service account defined in SERVICE_ACCOUNT, which requires the roles/iam.serviceAccountTokenCreator role on that service account. Your login no longer needs --scopes.

Troubleshooting

Symptom	Fix
GCP authentication required / API error 401	Re-run gcloud auth application-default login; confirm you have roles/iam.serviceAccountTokenCreator on the service account in SERVICE_ACCOUNT
Missing dependency	pip install google-auth requests
Unexpected response from Vertex AI	Confirm Vertex AI is enabled in project mozdata and the embedding model is available