New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Official agent skill

Google Cloud Solution RAG Enterprise Search Gke Sqldb

by google in google/skills

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Google Cloud Solution RAG Enterprise Search Gke Sqldb

skills CLI
$ npx skills add google/skills --skill google-cloud-solution-rag-enterprise-search-gke-sqldb -a claude-code

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

GitHub CLI
$ gh skill install google/skills google-cloud-solution-rag-enterprise-search-gke-sqldb --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-rag-enterprise-search-gke-sqldb .claude/skills/google-cloud-solution-rag-enterprise-search-gke-sqldb && 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-rag-enterprise-search-gke-sqldb
GitHub stars
21k
Token cost
~4k tokens
SKILL.md length
1,920 words
Files
5 (incl. references, assets)
Skills in repo
150
Repo updated
First seen
Licence
Apache-2.0

At a glance

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud.

  • Works in 4 steps: Requirements discovery → Solution architecture → Solution validation → …
  • Users need a vector-enabled SQL database as the store and index for the embedding vectors
  • SKILL.md covers Overview of the workflow, Phase 1: Requirements discovery, Phase 2: Solution architecture and Phase 3: Solution validation, plus 2 more sections
  • Calls terraform

What it does

Google Cloud Solution RAG Enterprise Search Gke Sqldb 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 a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.

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-selection-recommendations.md`).

It sits in AI & LLM Engineering, covering Retrieval-augmented generation, Vector databases and Embeddings. It works with Google Cloud, Google Kubernetes Engine, SQL and Kubernetes. 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 a vector-enabled SQL database as the store and index for the embedding vectors
  • An open model and open-source inferencing framework
  • Kubernetes containers to host all the application components
  • Fully-managed RAG

Example prompts

  • “Use the google-cloud-solution-rag-enterprise-search-gke-sqldb skill to discover requirements, and generates architectural, design, and deployment…”
  • “/google-cloud-solution-rag-enterprise-search-gke-sqldb”

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 4b940dd. 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:

    • 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
    • docs.cloud.google.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 RAG Enterprise Search Gke Sqldb loads about 4k tokens when it runs, and up to ~7.1k if it reads all its reference files. Until then it costs about 142 tokens; SKILL.md has 1,920 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~142
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
~7.1k

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 4b940dd, republished under its Apache-2.0 licence (© google). 1,920 words, ~3,981 tokens.

Download SKILL.mdSave it as .claude/skills/google-cloud-solution-rag-enterprise-search-gke-sqldb/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-rag-enterprise-search-gke-sqldb
description
Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.
metadata.version
1.0.0
metadata.category
MultiProductSolutions

RAG for enterprise search using GKE and AlloyDB

This skill provides a workflow to design and implement a secure, low-latency, and high-accuracy RAG-enabled conversational search solution for private enterprise content by using an AlloyDB database, Cloud Storage, and a Google Kubernetes Engine (GKE) cluster to host all the application components, including an open model and an open-source inference framework.

Overview of the workflow

The workflow consists of the following phases:

  • Phase 1: Requirements discovery. Gather detailed requirements related to the cloud workload or use case that the user needs assistance for.
  • Phase 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.
  • Phase 3: Solution validation. Create a plan to validate the generated solution, generate validation instructions and scripts, and run the validation.
  • Phase 4: Solution packing 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, technical decompositions, cloud services, or component mappings.

  • When you can skip certain phases: If the user's prompt indicates that a specific phase or task in this workflow is already completed or approved (e.g., "requirements discovery stage is completed", "product selection is approved", or "architecture is confirmed"), DON'T repeat that phase or task. Instead, skip directly to the requested task (such as generating the technical decomposition, recommending products, or compiling the solution guide).

Phase 1: Requirements discovery

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

Complete the following steps strictly in the specified order:

  1. Ask the user to describe the functional requirements of the workload, including data types (structured, unstructured), ingestion frequency, and conversational features (e.g., multi-turn chat, citation requirements).

  2. Ask the user to describe the following non-functional requirements:

    • Security, privacy, and compliance: E.g., network isolation, private endpoints, data residency, and requirements for compliance.
    • Reliability: E.g., scaling, high availability, resilience against zone or regional outages, disaster recovery goals for RTO and RPO.
    • Cost: E.g., cost of compute, storage, and database resources.
    • Operational excellence: E.g., monitoring, alerts, and logging.
    • Performance: E.g., data upload speed, performance expectations for generating embedding vectors, and latency requirements for model responses and data retrieval queries (including vector and hybrid search).
    • Sustainability: E.g., carbon footprint, low-carbon regions.

  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.

  4. Ask the user to describe dependencies, if any, on other workloads, products, or tools (e.g., identity providers, external sources, CRM/ERP database integrations).

  5. Review the input that the user has provided so far, and check whether there are any ambiguities or contradictions.

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

    • Describe the ambiguity or contradiction.
    • Ask the user how they wish to resolve the ambiguity or 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, technical decomposition, or Google Cloud product recommendations.

  6. Important: DON'T start this step if there are unresolved contradictions or ambiguities from Step 5.

    Generate a technical decomposition of the components of the workload. The technical decomposition must break down the solution into logical components, as follows:

    • Data ingestion: Blob storage for raw corporate documents.
    • Data processing and chunking: Containerized pipeline to extract data, clean it, and chunk it.
    • Embedding vectors generation: Containerized service to convert data chunks to embedding vectors.
    • Storing and indexing the embedding vectors: Vector-enabled SQL database for storing embedding vectors.
    • Handling non-vector data: Preparing non-vector data, like tables, views, and aggregations for data retrieval. Analyzing whether any indexing, partitioning, or other performance techniques can be applied on the original data schema.
    • Query and retrieval: Accepting client queries, identifying intent, and routing to a retrieval workflow, which might include conversion of the request to an embedding for semantic search, extracting and applying filters for filtered search or supplying all to the hybrid search.
    • Prompt augmentation: Augmenting the prompts with the retrieved context.
    • Response generation: Requesting and generating responses from the model.
    • Response sanity checks: Evaluating responses using an AI model and performing procedural checks according to defined criteria.

  7. Ask the user to approve the generated technical decomposition.

    Critical: You MUST stop execution immediately, call no more tools (such as file editors, searches, or code tools), and wait for the user to respond with their feedback or approval in the chat. Do NOT compile the architecture, recommend products, construct maps, or write any files/drafts for Phase 2 until the user's explicit approval is received.

  8. If the user requests changes, then generate an updated technical decomposition.

  9. Repeat steps 5 through 8 until the user approves the generated technical decomposition.

  10. Only after the user has explicitly approved the technical decomposition, proceed to Phase 2.

    Important: You are strictly prohibited from recommending product choices, generating the architecture diagram, or drafting design recommendations until the technical decomposition is approved.

Phase 2: Solution architecture

Ground all generated content

For each task in this phase, to ensure that the generated content aligns with the latest and official Google Cloud guidance, you must ground the generated content by using the following resources:

For each item in the generated guidance, you must include citations to the relevant official Google Cloud documentation pages.

Task 2.1: Identify Google Cloud products and features required for the workload.

  1. Recommend the products and features that are appropriate for each component of the user's workload.

    Important: The Google Cloud products and features that you recommend MUST be consistent with the guidance in references/product-selection-recommendations.md.

  2. Present the generated product recommendations and ask the user to approve the recommendations.

  3. If the user requests changes, then make the required changes.

  4. Repeat steps 2 and 3 until the user approves the product recommendations.

  5. After the user approves the product recommendations, proceed to Task 2.2.

Show full SKILL.md (791 more words)Show less
Task 2.2: Generate an architecture diagram.

  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 technical composition that you generated.

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

    • Embedding pipeline (batch/streaming): Data source -> Cloud Storage -> Cloud Storage FUSE -> GKE Ray Worker (Chunking) --> Embedding generation using GemmaEmbedding -> AlloyDB.
    • Serving pipeline (real-time): User client -> GKE Frontend (LangChain Orchestration) -> Database Query (semantic or hybrid search on the vector store) -> Retrieve matching data -> Augment prompt -> Gemma vLLM endpoint API -> Output (Responsible AI filtering) -> User client.

  2. Present the generated diagram to the user and ask the user to approve the architecture diagram.

  3. If the user requests changes, then make the required changes.

  4. Repeat steps 2 and 3 until the user approves the architecture diagram.

  5. After the user approves the architecture diagram, proceed to Task 2.3.

Task 2.3: Generate an architecture description.
  1. Generate a description that explains the purpose of each component, the relationships between the components, and the task flow or data flow.
  2. Present the generated architecture description to the user and ask the user to approve the description.
  3. If the user requests any changes, then make the required changes.
  4. Repeat steps 2 and 3 until the user approves the architecture description.
  5. After the user approves the architecture description, proceed to Task 2.4.
Task 2.4: Generate design recommendations.

  1. Generate design recommendations and best practices to optimally configure each component in the architecture based on the workload's requirements.

    Important: The design recommendations and best practices that you generate MUST be consistent with the guidance in the resources that are listed in the following files:

    • references/related-documentation.md
    • references/design-recommendations.md

  2. Present the generated recommendations to the user and ask whether the user needs any changes.

  3. If the user needs changes, then make the required changes.

  4. Repeat steps 2 and 3 until the user confirms that the generated design recommendations meet their requirements.

  5. Proceed to Task 2.5.

Task 2.5: Generate deployment guidance.

  1. Generate guidance to deploy the solution, including the following:

    • Terraform code to create the required infrastructure resources.
    • Steps or scripts to deploy workloads, such as Ray-on-GKE (KubeRay coordinator and worker nodes) and the LangChain frontend deployment.

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

  2. Present the generated deployment guidance to the user and ask whether the user needs any changes.

  3. If the user requests changes, then make the required changes.

  4. Repeat steps 2 and 3 until the user confirms that the generated deployment guidance meets their requirements.

  5. Proceed to Phase 3.

Phase 3: Solution validation

  1. Create a plan to validate the generated solution. The plan must outline the steps that are necessary to verify that the generated solution meets the workload's requirements. The following are examples of validation steps:
    • Deployment dry-run: Run commands like terraform plan to preview the infrastructure resources that will be provisioned.
    • Connectivity and routing: Verify network paths, load balancer routing, and service endpoints.
    • Vector index: Verify that vector indexes are correctly created and populated in the AlloyDB database. This can involve querying the database to check index status and content.
    • Embedding pipeline: Test the end-to-end embedding pipeline from data ingestion to vector storage, ensuring documents are chunked, embedded, and stored correctly.
    • Retrieval latency: Measure the latency of vector and hybrid search queries against the AlloyDB vector store to ensure performance meets requirements.
    • Retrieval accuracy: Perform sample queries and evaluate the relevance of retrieved documents or chunks.
    • Security policies: Verify restricted access, firewall rules, and IAM enforcement.
  2. Present the validation plan to the user and request feedback or approval.
  3. If the user requests changes, update the plan as required.
  4. Repeat steps 2 and 3 until the user approves the validation plan.
  5. Generate scripts or commands using tools like curl or gcloud to perform the steps in the approved validation plan.
  6. Request permission from the user to perform the validation checks.
  7. If the user gives permission, run the validation checks and troubleshoot any deployment issues.
  8. When all the validation checks pass, proceed to Phase 4.

Phase 4: Solution packaging and presentation

  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 assets/output-template.md.
  2. Request the user's permission to write the code files in the user's workspace.
  3. After the user gives permission, write the final code files in the user's workspace.

Supporting references

  • references/product-selection-recommendations.md
  • references/design-recommendations.md
  • references/related-documentation.md

© 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-rag-enterprise-search-gke-sqldb of google/skills.

  • SKILL.md
  • assets/output-template.md
  • references/design-recommendations.md
  • references/product-selection-recommendations.md
  • references/related-documentation.md

Open the folder on GitHubat commit 4b940dd

Compare with similar skills

Google Cloud Solution RAG Enterprise Search Gke Sqldb 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 RAG Enterprise Search Gke Sqldb compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Google Cloud Solution RAG Enterprise Search Gke Sqldb this skillgoogle/skills21k—~4kAutomated safety check: PassApache-2.0
Retail Product Search Agentgoogle/adk-recipes10k—~3kAutomated safety check: PassApache-2.0
Cortexdbliliang-cn/cortexdb274—~18kAutomated safety check: WarnMIT
DBoracle/skills877—~1.4kAutomated safety check: PassUPL-1.0
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k7 repos~2.3kAutomated safety check: PassMIT
AI SDK Developmenttrypostit/trypost6911 repos~3.5kAutomated safety check: PassMIT

Similar skills

  • Retail Product Search Agent

    google/adk-recipes

    Official

    Builds a retail product search agent on Google Cloud, from catalog ingestion into BigQuery and Vector Search to ADK scaffolding, evaluation and Cloud Run deployment.

    10k GitHub stars~3k tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Cortexdb

    liliang-cn/cortexdb

    Use CortexDB for local-first AI memory, vector search, RAG, knowledge graphs, SPARQL/RDFS/SHACL, corpus-to-graph workflows, external structured-data import (CSV / SQL dumps), and MCP/tool calling.

    274 GitHub stars~18k tokensUpdated 2 days ago
    Knowledge ManagementAuto-check: warnings

  • DB

    oracle/skills

    Official

    Oracle Database guidance for SQL, PL/SQL, SQLcl, ORDS, Oracle Vector SDK, administration, app development, performance, security, migrations, and agent-safe database workflows.

    877 GitHub stars~1.4k tokensUpdated yesterday
    DatabasesAuto-check passed

  • Chroma Vector Database

    Orchestra-Research/AI-Research-SKILLs

    Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.

    13k GitHub starsUsed in 7 repos~2.3k tokens
    AI & LLM EngineeringAuto-check passed

  • AI SDK Development

    trypostit/trypost

    TRIGGER when working with ai-sdk which is Laravel official first-party AI SDK.

    691 GitHub starsUsed in 1 repo~3.5k tokens
    AI & LLM EngineeringAuto-check passed

  • Pgvector Semantic Search

    timescale/pg-aiguide

    A skill your agent uses for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.

    1.9k GitHub stars~3.8k tokensUpdated 3 days ago
    AI & LLM EngineeringAuto-check passed

More from google/skills

All 150 skills in this repo

  • Cloud Trace Querying

    google/skills

    Official

    Query Cloud Trace spans, filter by latency thresholds or error status, correlate distributed traces with Cloud Logging, and diagnose latency bottlenecks across Google Cloud services.

    21k GitHub stars~1.7k tokensUpdated yesterday
    Auto-check passed

  • Google Cloud PAM Helper

    google/skills

    Official

    Manages Google Cloud Privileged Access Manager entitlements and grants: create and edit entitlements, request temporary access, and approve or deny pending grants.

    21k GitHub stars~3.2k tokensUpdated yesterday
    Auto-check passed

  • Agent Platform Alert Configuration

    google/skills

    Official

    Writes Terraform alerting policies for AI agents that emit OpenTelemetry metrics, covering reliability, cost, safety, security and quality signals on Google Cloud.

    21k GitHub stars~4.2k tokensUpdated yesterday
    Auto-check passed

  • Model Garden Deployment

    google/skills

    Official

    Deploys open models or custom weights from Model Garden to Agent Platform endpoints, checks deployment status and cleans up endpoints, confirming before any change.

    21k GitHub stars~5k tokensUpdated yesterday
    Auto-check passed

  • Agent Platform Skill Registry

    google/skills

    Official

    Searches, manages and scaffolds skills in the Gemini Enterprise Agent Platform Skill Registry using bundled Python scripts and Google Cloud credentials.

    21k GitHub stars~584 tokensUpdated yesterday
    Auto-check passed

  • GCP Application Design Center Deploy

    google/skills

    Official

    Designs GCP infrastructure as local Terraform, validates and scans it against best practices, then imports it to Application Design Center for deployment and troubleshooting.

    21k GitHub stars~4.4k tokensUpdated yesterday
    Auto-check passed

Works with

Categories

Questions about Google Cloud Solution RAG Enterprise Search Gke Sqldb

What does Google Cloud Solution RAG Enterprise Search Gke Sqldb do?

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Google Cloud Solution RAG Enterprise Search Gke Sqldb 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 a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud.

When should I use Google Cloud Solution RAG Enterprise Search Gke Sqldb?

Google Cloud Solution RAG Enterprise Search Gke Sqldb fits situations like: users need a vector-enabled SQL database as the store and index for the embedding vectors; an open model and open-source inferencing framework; Kubernetes containers to host all the application components; fully-managed RAG.

How do I install Google Cloud Solution RAG Enterprise Search Gke Sqldb in Claude Code?

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

How do I install Google Cloud Solution RAG Enterprise Search Gke Sqldb in Codex?

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

Can I use Google Cloud Solution RAG Enterprise Search Gke Sqldb 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-rag-enterprise-search-gke-sqldb -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-rag-enterprise-search-gke-sqldb, .gemini/skills/google-cloud-solution-rag-enterprise-search-gke-sqldb, .github/skills/google-cloud-solution-rag-enterprise-search-gke-sqldb and .opencode/skills/google-cloud-solution-rag-enterprise-search-gke-sqldb in your project.

What does Google Cloud Solution RAG Enterprise Search Gke Sqldb need to run?

Going by SKILL.md and its folder, Google Cloud Solution RAG Enterprise Search Gke Sqldb needs the command-line tools its instructions call (terraform).

Does Google Cloud Solution RAG Enterprise Search Gke Sqldb access the network?

SKILL.md names 4 domains. As links in the text: developers.google.com, developerknowledge.googleapis.com, docs.cloud.google.com and github.com. This is read from the text; nothing was executed.

Is Google Cloud Solution RAG Enterprise Search Gke Sqldb 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 RAG Enterprise Search Gke Sqldb use?

Google Cloud Solution RAG Enterprise Search Gke Sqldb 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 RAG Enterprise Search Gke Sqldb 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 3.1k tokens, read only when the agent opens those files.

What are the alternatives to Google Cloud Solution RAG Enterprise Search Gke Sqldb?

Skills that share tags, products or a category with Google Cloud Solution RAG Enterprise Search Gke Sqldb: Retail Product Search Agent (google/adk-recipes, 10k stars), Cortexdb (liliang-cn/cortexdb, 274 stars), DB (oracle/skills, 877 stars) and Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k 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 RAG Enterprise Search Gke Sqldb?

google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,097 GitHub stars. The repository holds 150 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.