Official agent skill

Google Cloud Solution Hybrid Search Alloydb

by google in google/skills

Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search.

OfficialApache-2.0Auto-check passedDatabases

Install Google Cloud Solution Hybrid Search Alloydb

skills CLI
$ npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google/skills google-cloud-solution-hybrid-search-alloydb --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/google-cloud-solution-hybrid-search-alloydb .claude/skills/google-cloud-solution-hybrid-search-alloydb && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
google-cloud-solution-hybrid-search-alloydb
GitHub stars
21k
Token cost
~4k tokens
SKILL.md length
1,874 words
Files
5 (incl. references, assets)
Skills in repo
147
Repo updated
First seen
Licence
Apache-2.0

At a glance

Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search.

  • Works in 4 steps: Requirements discovery → Solution architecture → Solution validation → …
  • Users need vector search combined with structured SQL filtering
  • SKILL.md covers Overview of the workflow, Product Renaming & Terminology, Phase 1: Requirements discovery and Phase 2: Solution architecture, plus 2 more sections
  • Calls gcloud and terraform

What it does

Google Cloud Solution Hybrid Search Alloydb is an agent skill from google/skills, published by the product's own GitHub organization. Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for…

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files and assets (for example `assets/output-template.md`, `references/design-recommendations.md` and `references/product-mapping.md`).

It sits in Databases, covering Retrieval-augmented generation and Vector databases. It works with Google Cloud and SQL. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.

When your agent uses it

  • Users need vector search combined with structured SQL filtering
  • Faceted attributes
  • Semantic reranking
  • In-database AI validation

Example prompts

  • “Use the google-cloud-solution-hybrid-search-alloydb skill to discover requirements and generates architectural, design, and deployment guidance for…”
  • “/google-cloud-solution-hybrid-search-alloydb”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Requirements discovery
  2. Solution architecture
  3. Solution validation
  4. Solution packaging and presentation

What it can do on your machine

Read from SKILL.md and the folder at commit 5120a76. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gcloud
    • terraform

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • developers.google.com
    • developerknowledge.googleapis.com
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Google Cloud Solution Hybrid Search Alloydb loads about 4k tokens when it runs, and up to ~8.9k if it reads all its reference files. Until then it costs about 164 tokens; SKILL.md has 1,874 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~164
When it runs · the whole SKILL.md, loaded when a task matches
~4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from google/skills at commit 5120a76, republished under its Apache-2.0 licence (© google). 1,874 words, ~3,984 tokens.

Download SKILL.mdSave it as .claude/skills/google-cloud-solution-hybrid-search-alloydb/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
google-cloud-solution-hybrid-search-alloydb
description
Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.
metadata.version
1.0.0
metadata.category
MultiProductSolutions

Dynamic Hybrid Search using AlloyDB

This skill provides a workflow to design and implement secure, low-latency, and high-accuracy hybrid search solutions combining structured dataset filtering, vector search indexing, faceted metadata filtering, semantic reranking, recall evaluation, in-database AI validation, database abstraction layers, and serverless application hosting.

Overview of the workflow

The workflow consists of the following phases:

  1. Requirements discovery. Gather detailed requirements related to the cloud workload or use case that the user needs assistance for.
  2. Solution architecture. Use the requirements that were gathered in Phase 1 to generate a detailed solution architecture for the cloud workload or use case.
  3. Solution validation. Create a plan to validate the generated solution, generate validation instructions and scripts, and run the validation.
  4. Solution packaging and presentation. Consolidate the generated content and present the solution.

Important notes about the workflow:

  • Strict phase separation: During Phase 1 (Requirements discovery), when you ask the user clarifying questions, DON'T recommend, propose, or outline any architectural designs, cloud services, or component mappings. This prevents premature architecture commitments or hallucinations before the full scope is understood.
  • Halting for approval: For any step where you are instructed to "obtain approval before proceeding", you MUST stop executing, present the completed tasks to the user, and wait for their explicit approval. You MUST NOT proceed to execute any subsequent tasks or generate any further guidance in that response.
  • Ground all generated content: For all tasks across all phases, you MUST first look in the following resources:

Product Renaming & Terminology

When generating solution designs, architecture diagrams, and documentation, check the latest Google Cloud documentation for the most up-to-date product names. The table below provides examples of name mappings to be aware of. Note that underlying APIs, Terraform resources, and IAM roles may retain their legacy identifiers.

<table>
  <thead>
    <tr>
      <th>Legacy Name</th>
      <th>Updated Name</th>
      <th>Notes</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Vertex AI</td>
      <td>Gemini Enterprise Agent Platform</td>
      <td>Gemini Enterprise Agent Platform can be shortened to Agent Platform after first instance</td>
    </tr>
    <tr>
      <td>Vertex AI Embedding</td>
      <td>Text embedding on Gemini Enterprise Agent Platform</td>
      <td>This refers to the text embedding models available on Gemini Enterprise Agent Platform</td>
    </tr>
    <tr>
      <td>Vertex AI Matching Engine</td>
      <td>Vector Search</td>
      <td></td>
    </tr>
  </tbody>
</table>

Phase 1: Requirements discovery

In this phase, you must gather detailed requirements related to the hybrid search workload that the user wants to design and deploy in Google Cloud.

Acknowledge provided requirements: If the user's prompt already contains some requirements (functional or non-functional, such as catalog size, search modalities, faceted attributes, or latency targets), you MUST explicitly acknowledge and restate all of these requirements in your response. Do NOT ask the user to describe or re-describe any requirements that they have already provided in the prompt.

Complete the following steps strictly in the specified order:

  • Step 1: Ask the user to describe the functional requirements of the workload, including catalog dataset details (e.g., e-commerce apparel, retail products, patent database), search modalities (natural language text, visual search, attribute filters), metadata attributes for faceted filtering (e.g., category, sub_category, color, gender, price), and quality checks (reranking, LLM validation).

  • Step 2: You MUST explicitly ask the user to describe ALL of the following six categories of non-functional requirements. You need this information because each category represents a critical architectural pillar, and neglecting any of them can result in a solution that is insecure, unreliable, or inefficient (do NOT omit any of them):

    • Security, privacy, and compliance: E.g., private VPC endpoints, Private Service Connect, Direct VPC Egress, and access control.
    • Reliability: E.g., high availability, failover, disaster recovery goals (RTO/RPO), regional vs multi-region AlloyDB topology.
    • Cost: E.g., budget constraints for compute, database instances, and Gemini Enterprise Agent Platform API calls.
    • Operational excellence: E.g., monitoring, logging, dashboards, and automated deployment.
    • Performance: E.g., target P95 query latency (e.g., < 100ms), vector search recall target (e.g., > 95%), catalog item scale, and QPS expectations.
    • Sustainability: E.g., carbon footprint, low-carbon region selection.
  • Step 3: Ask the user whether the workload currently runs on other cloud providers or on-premises.

    • If the user's answer is "yes", then ask the user to describe the architecture of the current deployment.
    • If the user's answer is "no", then proceed to the next step.
  • Step 4: Ask the user to describe dependencies, if any, on other workloads, products, or tools (e.g., existing inventory databases, ERP systems, application runtime languages like Java or Python).

  • Step 5: Review the input that the user has provided so far, and check whether there are any ambiguities, conflicts, or contradictions in the functional requirements, non-functional requirements, and dependencies. You MUST compare all requirements against each other to identify any conflicts.

    If you identify any ambiguities, conflicts, or contradictions in the requirements that the user has provided, you MUST do the following for each ambiguity, conflict, or contradiction:

    • Identify exactly where each contradiction lies and explain to the user why the requirements are incompatible and cannot be simultaneously satisfied. Do NOT treat fundamental contradictions as design choice questions (e.g., asking how to implement or configure a conflicting requirement).
    • Ask the user to clarify their trade-off preferences to resolve the contradiction.
    • If the user delegates the choice to you (e.g., the user replies with "do what you think is best" or "you decide"), then provide a clear suggestion to resolve the ambiguity or contradiction, explain your reasoning, and ask the user to approve your suggestion.

    Critical: Until all the ambiguities and contradictions that you identify are resolved according to the preceding guidance, you must NOT recommend or generate any architecture design or Google Cloud product recommendations.

  • Step 6: Summarize the functional and non-functional requirements provided by the user into a consolidated requirements summary.

  • Step 7: Present the generated requirements summary to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Phase 2.

Important: STOP, DON'T proceed to generate architecture diagram, architecture description or product recommendations until you have confirmed the generated requirements summary and resolved all ambiguities and contradictions in this phase.

Phase 2: Solution architecture

Task 2.1: Identify Google Cloud products and features required for the workload.
  • Step 1: Recommend products and features that are appropriate for each component of the user's workload, prioritizing Google Cloud products.

    Important: The Google Cloud products and features that you recommend MUST be consistent with the guidance in Product Mapping.

  • Step 4: Present the generated product recommendations to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Task 2.2.

    Important: STOP, DON'T proceed to generate architecture diagram until you have confirmed the generated product recommendations with the user.

Show full SKILL.md (733 more words)Show less
Task 2.2: Generate an architecture diagram and description
  • Step 1: Generate an architecture diagram in the Mermaid format: https://github.com/mermaid-js/mermaid.

    The diagram must show the data flows and request flows across the components of the architecture, based on the gathered requirements and product recommendations. The diagram MUST explicitly show both the ingestion pipeline and serving pipeline.

    The following is an example of the data flows and request flows that the architecture diagram should show:

    • Ingestion pipeline: Catalog Data -> AlloyDB Table (apparels) -> B-Tree Indexes on Facets -> Text embedding (text-embedding-005) -> ScaNN Vector Index.
    • Serving pipeline: User Browser -> Cloud Run Web App -> MCP Toolbox for Databases -> AlloyDB Single-Query Hybrid Search (ScaNN Vector Search + SQL WHERE Filters) -> ai.rank Reranker -> Gemini Pro ai.generate Quality Validation -> Validated Results -> User Browser.
  • Step 2: Generate a description that explains the purpose of each component, the relationships between the components, and the task flow or data flow.

  • Step 3: Present the generated architecture diagram and description to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Task 2.3.

    Important: STOP, DON'T proceed to generate design recommendations until you have confirmed the generated architecture description with the user.

Task 2.3: Generate design recommendations.
  • Step 1: Generate design recommendations and best practices to optimally configure each component in the architecture based on the workload requirements.

    Important:

    • When you generate design recommendations, consider the following:
      • Functional requirements that were gathered in Phase 1.
      • Non-functional requirements that were gathered in Phase 1.
    • Align the generated design recommendations with the recommendations in Design Recommendations.
    • To generate guidance for the non-functional requirements, use the following skills:
      • google-cloud-waf-security
      • google-cloud-waf-reliability
      • google-cloud-waf-cost-optimization
      • google-cloud-waf-operational-excellence
      • google-cloud-waf-performance-optimization
      • google-cloud-waf-sustainability
  • Step 2: Present the generated recommendations to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Task 2.4.

    Important: STOP, DON'T proceed to generate deployment guidance until you have confirmed the design recommendations with the user.

Task 2.4: Generate deployment guidance.
  • Step 1: Generate guidance to deploy the solution, including the following:

    • AlloyDB DDL & SQL setup scripts for extensions (google_ml_integration, alloydb_scan), tables, B-Tree indexes, ScaNN vector indexes, hybrid search SQL, and Gemini validation CTEs.
    • MCP Toolbox deployment configuration on Cloud Run.
    • Python Cloud Run Function shim deployment command.
    • Application deployment command (gcloud run deploy {app_name}).
    • Terraform code or gcloud CLI commands to create required infrastructure.

    Important: The deployment guidance that you generate MUST be consistent with the guidance in the following resources:

  • Step 2: Present the generated deployment guidance to the user and obtain approval (the user MUST explicitly say "yes" or "I approve") before proceeding to Phase 3.

    Important: STOP, DON'T proceed to generate solution validation until you have confirmed the deployment guidance with the user.

Phase 3: Solution validation

Task 3.1: Pre-deployment validation
  • Step 1: Create a pre-deployment plan to statically validate the generated solution and verify that it meets the workload requirements without provisioning live resources:
    • Deployment dry-run: Validate infrastructure syntax and preview the resources that will be provisioned using dry-run commands (e.g., terraform plan or (where supported) gcloud ... --dry-run).
    • Architecture & policy analysis: Perform static verification of network routing topologies, firewall rules, and IAM enforcement against best practices.
  • Step 2: Present the static validation plan to the user, obtain approval (the user MUST explicitly say "yes" or "I approve"), and execute the dry-run commands.
  • Step 3: Troubleshoot and fix any errors or policy discrepancies identified during dry-run checks until validation succeeds.
  • Step 4: Proceed to Task 3.2
Task 3.2: Runtime validation (Post-deployment)
  • Step 1: Ask the user whether they choose to deploy the infrastructure now to perform live runtime verification, or skip directly to Phase 4.
  • Step 2: If the user chooses to deploy the infrastructure:
    • After the user deploys the infrastructure, generate runtime verification commands (using tools like curl, ping, or gcloud) and provide them to the user to execute, to test live endpoint reachability, networking paths, and load balancer routing.
    • Troubleshoot any deployment or runtime routing issues until checks pass.
  • Step 3: Proceed to Phase 4.

Phase 4: Solution packaging and presentation

  • Step 1: Consolidate the final text artifacts that were generated in Phase 2 into a single Markdown file named solution-architecture-guide.md, based on the template in Output Template.
  • Step 2: Request the user's permission to write the code files in the user's workspace.
  • Step 3: After the user gives permission, write the final code files in the user's workspace.

© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references, assets) in skills/cloud/google-cloud-solution-hybrid-search-alloydb of google/skills.

  • SKILL.md
  • assets/output-template.md
  • references/design-recommendations.md
  • references/product-mapping.md
  • references/related-guidance.md

Open the folder on GitHubat commit 5120a76

Compare with similar skills

Google Cloud Solution Hybrid Search Alloydb next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Google Cloud Solution Hybrid Search Alloydb compared with similar skills
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Retail Product Search Agentgoogle/adk-recipes10k—~3kAutomated safety check: PassApache-2.0
Neon Postgresneondatabase/agent-skills100—~4.1kAutomated safety check: NotesApache-2.0
Cortexdbliliang-cn/cortexdb274—~18kAutomated safety check: WarnMIT

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Works with

Questions about Google Cloud Solution Hybrid Search Alloydb

What does Google Cloud Solution Hybrid Search Alloydb do?

Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Google Cloud Solution Hybrid Search Alloydb is an agent skill from google/skills, published by the product's own GitHub organization. Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search.

When should I use Google Cloud Solution Hybrid Search Alloydb?

Google Cloud Solution Hybrid Search Alloydb fits situations like: users need vector search combined with structured SQL filtering; faceted attributes; semantic reranking; in-database AI validation.

How do I install Google Cloud Solution Hybrid Search Alloydb in Claude Code?

Run `npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a claude-code`. Or copy the skill folder (skills/cloud/google-cloud-solution-hybrid-search-alloydb in google/skills) into .claude/skills/google-cloud-solution-hybrid-search-alloydb in your project. Claude Code loads it when a task matches its description.

How do I install Google Cloud Solution Hybrid Search Alloydb in Codex?

Run `npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a codex`. Or copy the skill folder (skills/cloud/google-cloud-solution-hybrid-search-alloydb in google/skills) into .agents/skills/google-cloud-solution-hybrid-search-alloydb in your project. Codex loads it when a task matches its description.

Can I use Google Cloud Solution Hybrid Search Alloydb in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/google-cloud-solution-hybrid-search-alloydb, .gemini/skills/google-cloud-solution-hybrid-search-alloydb, .github/skills/google-cloud-solution-hybrid-search-alloydb and .opencode/skills/google-cloud-solution-hybrid-search-alloydb in your project.

What does Google Cloud Solution Hybrid Search Alloydb need to run?

Going by SKILL.md and its folder, Google Cloud Solution Hybrid Search Alloydb needs the command-line tools its instructions call (gcloud and terraform).

Does Google Cloud Solution Hybrid Search Alloydb access the network?

SKILL.md names 3 domains. As links in the text: developers.google.com, developerknowledge.googleapis.com and github.com. This is read from the text; nothing was executed.

Is Google Cloud Solution Hybrid Search Alloydb safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Google Cloud Solution Hybrid Search Alloydb use?

Google Cloud Solution Hybrid Search Alloydb is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Google Cloud Solution Hybrid Search Alloydb use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.9k tokens, read only when the agent opens those files.

What are the alternatives to Google Cloud Solution Hybrid Search Alloydb?

Skills that share tags, products or a category with Google Cloud Solution Hybrid Search Alloydb: DB (oracle/skills, 876 stars), Aliyun Opensearch Search (cinience/alicloud-skills, 397 stars), Retail Product Search Agent (google/adk-recipes, 10k stars) and Neon Postgres (neondatabase/agent-skills, 100 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Google Cloud Solution Hybrid Search Alloydb?

google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,069 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 9, 2026.

Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.