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retail-product-search

Creates product search agents with semantic search and RAG on Google Cloud (Vertex AI Vector Search, 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, Vertex AI Vector Search collection setup, ADK agent scaffolding, evaluation, and Cloud Run deployment.

77

Quality

96%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable and well-sequenced with validation checkpoints and feedback loops, and it practices progressive disclosure with verified one-level-deep references. The only weakness is minor repetition of the venv/adk 'not bare' warnings and some troubleshooting duplication that lightly pads the token budget.

Suggestions

State each interpreter warning once near its first use (e.g. in Workspace Setup) and link to references/install-paths.md instead of repeating '.venv/bin/python, not bare python' and '.venv/bin/adk, not bare adk' across Workspace Setup and Test.

Trim the inline Troubleshooting table to the top 2-3 most common errors and defer the rest to references/troubleshooting.md to remove duplication.

Move the forward-looking MCP Migration table into a reference doc, since it does not guide immediate action and adds length to the overview.

DimensionReasoningScore

Conciseness

The body is dense and action-oriented with little concept over-explanation, but repeats the '.venv/bin/python, not bare python' and '.venv/bin/adk, not bare adk' warnings across Workspace Setup and Test, and the inline Troubleshooting table duplicates content already in references — minor padding that could be trimmed, so it sits above the midpoint rather than at the top anchor.

4 / 5

Actionability

Copy-paste-ready commands cover the common cases — the SKILL_DIR lookup + bootstrap.sh one-liner, setup.py/cleanup.py invocations, the single-line VECTOR_SEARCH_COLLECTION export, adk web, the direct smoke test, and the eval command — all with explicit defaults.

5 / 5

Workflow Clarity

Quick Start is an explicitly sequenced 1-6 list with a 'do not parallelize' directive and race-condition rationale, a non-zero-exit feedback loop into troubleshooting, an explicit deploy approval gate ('Never deploy without explicit human approval'), and a Completion Checklist — clear sequence with validation and feedback loops.

5 / 5

Progressive Disclosure

SKILL.md is an overview pointing to six real one-level-deep reference files (all verified present), each with a one-line description in the References section, with scripts and assets split out separately — clear overview with well-signaled navigation.

5 / 5

Total

19

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is third-person, concise, and explicitly covers both what the skill does and when to use it, with multiple concrete pipeline actions and natural trigger phrases including synonyms. It matches the top anchor on every dimension.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions covering the full pipeline — 'catalog data ingestion to BigQuery, Vertex AI Vector Search collection setup, ADK agent scaffolding, evaluation, and Cloud Run deployment' — which matches the comprehensive-coverage anchor.

5 / 5

Completeness

Explicitly answers both 'what' (creates product search agents with semantic search/RAG, full pipeline) and 'when' ('Use when the user wants to…' with concrete trigger phrases), matching the top anchor.

5 / 5

Trigger Term Quality

Six quoted natural triggers ('build a product search agent', 'create an e-commerce search', 'make a shopping assistant', 'set up semantic catalog discovery', 'ingest products into Vector Search', 'deploy a retail RAG agent') give comprehensive coverage including synonyms a user would actually say.

5 / 5

Distinctiveness Conflict Risk

Clear retail-specific niche on Vertex AI Vector Search / BigQuery with distinct triggers, giving minimal overlap risk with other skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
google/adk-samples
Reviewed

Table of Contents

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