Retail Product Search
Creates product search agents with semantic search and RAG on Google Cloud (Vector...
Install
npx skills add google/adk-recipes --skill retail-product-search -g -a claude-code -y- Repository
- google/adk-recipes10.6k
- Category
- AI and agents
- Updated
- 4d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 2,794 tokens
- Runs scripts
- Yes, review before use
What it does
Creates product search agents with semantic search and RAG on Google Cloud (Vector Search on Gemini Enterprise Agent Platform, BigQuery, embeddings). Use when the user wants to "build a product search agent", "create an e-commerce search", "make a shopping assistant", "set up semantic catalog discovery", "ingest products into Vector Search", or "deploy a retail RAG agent". Handles the full pipeline: catalog data ingestion to BigQuery, Gemini Enterprise Agent Platform Vector Search collection setup, ADK agent scaffolding, evaluation, and Cloud Run deployment.
Product Search Agent
Creates product search agents with semantic search and RAG on Google Cloud.
STOP, Q-MODE FIRST
If a catalog is already loaded (system context says "DEPLOYED search agent"
or provides a <catalog> block), skip Q-MODE and answer product queries
directly using the catalog.
Otherwise, your first message MUST be exactly this:
[skill: retail-product-search] active.
Q-MODE: Pick a setup mode? [default: 1]
1. Quick start -- 2 questions, smart defaults, ~60s. Best for demos and first-timers.
2. Full setup -- 4 questions, ~2 min. Best for real builds.
Then stop and wait. Accept 1, quick, empty/Enter (Quick), or 2, full (Full).
Execution Rules
- Q-MODE first. No preamble, no plan-proposing.
- One question at a time. Show
[default: ...]. Empty input = default. - Save answers to
./design-spec.mdin the workspace as you collect them. - After interview, run
scripts/setup.py(see Workspace Setup below). - User can say "configure more" mid-Quick-Start to switch to Full.
Workspace Setup
The skill has two locations:
- Install dir, where SKILL.md and scripts live (varies by host)
- Workspace, the agent's cwd; design-spec.md, .venv, and per-run state live here
By the end of this section the workspace must have .venv/ (with the skill
installed editable), design-spec.md, and SKILL_DIR exported in the shell.
Run this as ONE shell command, splitting it across tool calls loses state:
SKILL_DIR=$(for d in ~/.claude/skills ~/.agents/skills ~/.gemini/skills ~/.cursor/skills; do
[ -f "$d/retail-product-search/SKILL.md" ] && echo "$d/retail-product-search" && break
done)
bash "$SKILL_DIR/scripts/bootstrap.sh"
bootstrap.sh finds a Python 3.11+ interpreter (with absolute-path fallback
for sandboxed shells), creates .venv, installs the skill editable, and
copies design-spec.md into the workspace.
