GCP Audit Logs
sickn33/agentic-awesome-skills
Configure GCP Cloud Audit Logs for compliance. An agent skill from sickn33/agentic-awesome-skills.
Official agent skill
Google Cloud Solution Agentic AI Borderless Data Lakehouse
by google in google/skills
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents.
Install Google Cloud Solution Agentic AI Borderless Data Lakehouse
$ npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-borderless-data-lakehouse --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-agentic-ai-borderless-data-lakehouse .claude/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse && rm -rf skills-srcUse ~/.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/
Install the "google-cloud-solution-agentic-ai-borderless-data-lakehouse" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse into .claude/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-borderless-data-lakehouse", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouseType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-borderless-data-lakehouse --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse .agents/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "google-cloud-solution-agentic-ai-borderless-data-lakehouse" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse into .agents/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-borderless-data-lakehouse", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-borderless-data-lakehouse --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse .cursor/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "google-cloud-solution-agentic-ai-borderless-data-lakehouse" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse into .cursor/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-borderless-data-lakehouse", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-borderless-data-lakehouse --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse .gemini/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "google-cloud-solution-agentic-ai-borderless-data-lakehouse" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse into .gemini/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-borderless-data-lakehouse", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills google-cloud-solution-agentic-ai-borderless-data-lakehouseInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse .github/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "google-cloud-solution-agentic-ai-borderless-data-lakehouse" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse into .github/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-borderless-data-lakehouse", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-borderless-data-lakehouse --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse .opencode/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "google-cloud-solution-agentic-ai-borderless-data-lakehouse" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse into .opencode/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-borderless-data-lakehouse", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
google-cloud-solution-agentic-ai-borderless-data-lakehouse- GitHub stars
- 21k
- Token cost
- ~2.5k tokens
- SKILL.md length
- 1,027 words
- Files
- 5 (incl. references, assets)
- Skills in repo
- 147
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents.
- Works in 4 steps: Requirements discovery and analysis → Solution design → Implementation plan → …
- Architecting multi-cloud storage infrastructure (Cloud Storage
- SKILL.md covers Product Renaming & Terminology and Workflow
- Calls terraform
What it does
Google Cloud Solution Agentic AI Borderless Data Lakehouse is an agent skill from google/skills, published by the product's own GitHub organization. Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE…
Its SKILL.md is about 2.5k 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 Data warehousing. It works with Google Cloud, Google BigQuery, Amazon S3 and Microsoft Azure. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
When your agent uses it
- Architecting multi-cloud storage infrastructure (Cloud Storage
- Establishing ingestion and AI serving subsystems
- Configuring Cross-Cloud Interconnect
- Deploying Gemini Enterprise Agent Platform and BigQuery data agents
Example prompts
- “Use the google-cloud-solution-agentic-ai-borderless-data-lakehouse skill to discover requirements and designs a borderless open data lakehouse using…”
- “/google-cloud-solution-agentic-ai-borderless-data-lakehouse”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 7d97937. 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):
docs.cloud.google.comgithub.comcodelabs.developers.google.comregistry.terraform.io
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 Agentic AI Borderless Data Lakehouse loads about 2.5k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 167 tokens; SKILL.md has 1,027 words of instructions outside code blocks.
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 7d97937, republished under its Apache-2.0 licence (© google). 1,027 words, ~2,546 tokens.
Download SKILL.mdSave it as
.claude/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse/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-agentic-ai-borderless-data-lakehouse
- description
- Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
- metadata.version
- 1.0.0
- metadata.category
- MultiProductSolutions
Borderless open data lakehouse agentic AI system
Follow this workflow to help users design and implement a custom multi-product solution in the cloud for a given workload, use case, or requirement.
Product Renaming & Terminology
When generating solution designs, architecture diagrams, and documentation, use the updated Google Cloud product names. For details on legacy vs. updated product names and terminology, see references/product_renaming.md.
Workflow
The solution design and implementation workflow consists of the following phases:
- Phase 1: Requirements discovery and analysis: Analyze the workload's requirements, constraints, dependencies, and current state.
- Phase 2: Solution design: Build a technology stack, architecture, and deployment configuration for the workload based on Google Cloud design best practices and recommendations.
- Phase 3: Implementation plan: Generate automation and instructions to deploy the solution.
- Phase 4: Solution validation: Validate that the deployment meets the requirements of the workload.
Phase 1: Requirements discovery and analysis
Step 1: Discover requirements: Understand the functional and non-functional requirements, business goals, and current state (if any) of the workload, including its architecture, dependencies, and constraints. Use the following questions to guide the requirements discovery process:
- What are your primary data sources?
- How do you manage and federate metadata across your data sources?
- What are your security and credential management requirements?
- What are the analytical and computational requirements to join and transform this borderless data?
- What types of natural language prompts or user queries do you expect AI agents or end-users to execute against this data?
Step 2: Identify components: Based on the requirements analysis, identify the components of the workload and their relationships. Also identify any borderless components, hybrid components, or on-prem components that the solution needs to integrate with.
Step 3: Generate component decomposition: Generate a technical decomposition of the components of the workload.
Step 4: Ask for confirmation: Ask the user to confirm whether the generated technical decomposition matches their workload requirements.
Step 5: Iterate: If the user requests changes, then generate an updated technical decomposition, and ask the user to confirm the changes. Continue iterating until the user confirms the technical decomposition.
Phase 2: Solution design
Step 1: Retrieve relevant Google Cloud documentation: Use available search or fetch tools to read the content of the following Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase before proceeding.
- Build hybrid and borderless architectures using Google Cloud
- Build a borderless open data lakehouse
- Implement agentic analytics workflows for distributed data
- Analytics Hybrid and Multicloud Pattern
- Google Cloud multi-regional deployment archetype
- Network segmentation and connectivity for distributed applications in Cross-Cloud Network
- Patterns for Connecting Other Cloud Service Providers with Google Cloud
Important: Use the content that you retrieve from Google Cloud documentation to ground the guidance that you generate in the remaining steps of this phase.
Step 2: Map components to Google Cloud products: For each component in the confirmed technical decomposition, identify the appropriate Google Cloud products and features, based on the guidelines in references/product_mapping.md.
Step 3: Create architecture diagram: Create an architecture diagram that shows the components, their relationships, and data/control flows.
- The diagram must be in the Mermaid format: https://github.com/mermaid-js/mermaid.
- The diagram must show a clear distinction between the products in the data ingestion subsystem and the serving subsystem.
- The diagram must show Managed Service for Apache Spark as a shared component for ETL/ingestion processing, bridging the data ingestion and serving subsystems (distinct from interactive IDE analytics workflows).
Step 4: Generate design recommendations: Generate design guidance based on the guidelines in references/design_recommendations.md.
Step 5: Draft solution architecture: Compile the requirements, technical decomposition, product mapping, architecture diagram, and design recommendations into a single Markdown file named
solution-architecture-guide.md, based on the template in assets/output-template.md.Step 6: Request review: Present the generated solution architecture to the user and request their feedback or approval.
Step 7: Iterate: If the user requests changes, generate an updated solution architecture and repeat steps 2-6 until the user approves the solution architecture.
Show full SKILL.md (377 more words)Show less
Phase 3: Implementation plan
Step 1: Retrieve relevant implementation resources:
- Build a Multicloud Open Data Lakehouse with Agentic AI
- Terraform Registry documentation for biglake_iceberg_catalog
- Create an Apache Iceberg table with metadata in Lakehouse runtime catalog
- Accelerate Spark batch workloads and sessions with Lightning Engine
- Create data agents
Important: Use these resources as the technical foundation for the IaC and deployment instructions you generate in the remaining steps of this phase.
Step 2: Identify deployment prerequisites: Document prerequisites for the deployment, including the following:
- Projects and billing associations
- Required Google Cloud APIs
- Required IAM permissions
- Any other prerequisites
Step 3: Generate Infrastructure as Code (IaC): Generate code (e.g., Terraform) and deployment scripts to automate the provisioning of the proposed Google Cloud resources.
Step 4: Write deployment instructions: Draft sequential, step-by-step deployment instructions to execute the IaC and initialize the workload components.
Step 5: Request review: Present the generated deployment instructions to the user for feedback and confirmation.
Step 6: Iterate: If the user requests changes, generate an updated implementation plan and repeat steps 2-5 until the user approves the implementation plan.
Phase 4: Solution validation
Step 1: Retrieve relevant verification resources (optional): If the resources from Phase 3 are not already in your context, retrieve the same implementation resources as the starting point for the validation checks and verification scripts that you generate in this phase.
Step 2: Define validation checks: Outline validation steps to verify that the deployed infrastructure meets the workload requirements:
- Deployment dry-run: Commands like
terraform planto preview changes. - Connectivity and routing: Verification of network paths, load balancer routing, and service endpoints.
- Security policies: Verification of restricted access, firewall rules, and IAM enforcement.
- Deployment dry-run: Commands like
Step 3: Generate verification scripts: Draft lightweight scripts or command-line instructions (e.g. using
curlorgcloud) that the user can run to perform these validation checks.Step 4: Compile validation report: Document the validation steps, verification scripts, and expected outcomes in a single Markdown file.
Step 5: Conduct validation and finalize: Assist the user in executing the validation checks and troubleshooting any deployment issues. After the solution is validated successfully, request final approval from the user.
Step 6: Iterate: If the user requests changes, then generate an updated validation plan and repeat steps 2-5 until the user approves the validation plan.
© 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-agentic-ai-borderless-data-lakehouse of google/skills.
- SKILL.md
- assets/output-template.md
- references/design_recommendations.md
- references/product_mapping.md
- references/product_renaming.md
Open the folder on GitHubat commit 7d97937
Compare with similar skills
Google Cloud Solution Agentic AI Borderless Data Lakehouse 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Google Cloud Solution Agentic AI Borderless Data Lakehouse this skillgoogle/skills | 21k | — | ~2.5k | Automated safety check: Pass | Apache-2.0 | |
| GCP Audit Logssickn33/agentic-awesome-skills | 47k | 2 repos | ~3.8k | Automated safety check: Pass | MIT | |
| Imaging Data CommonsK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~7.8k | Automated safety check: Pass | MIT | |
| Deploying On GCPancoleman/ai-design-components | 526 | — | ~3.9k | Automated safety check: Pass | MIT | |
| Analyze Usageopenshift-eng/ai-helpers | 120 | — | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Bigquery ML Model Creatorjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~571 | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Google Cloud Solution Agentic AI Borderless Data Lakehouse
What does Google Cloud Solution Agentic AI Borderless Data Lakehouse do?
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Google Cloud Solution Agentic AI Borderless Data Lakehouse is an agent skill from google/skills, published by the product's own GitHub organization. Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents.
When should I use Google Cloud Solution Agentic AI Borderless Data Lakehouse?
Google Cloud Solution Agentic AI Borderless Data Lakehouse fits situations like: architecting multi-cloud storage infrastructure (Cloud Storage; establishing ingestion and AI serving subsystems; configuring Cross-Cloud Interconnect; deploying Gemini Enterprise Agent Platform and BigQuery data agents.
How do I install Google Cloud Solution Agentic AI Borderless Data Lakehouse in Claude Code?
Run `npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse -a claude-code`. Or copy the skill folder (skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse in google/skills) into .claude/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse in your project. Claude Code loads it when a task matches its description.
How do I install Google Cloud Solution Agentic AI Borderless Data Lakehouse in Codex?
Run `npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse -a codex`. Or copy the skill folder (skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse in google/skills) into .agents/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse in your project. Codex loads it when a task matches its description.
Can I use Google Cloud Solution Agentic AI Borderless Data Lakehouse 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-agentic-ai-borderless-data-lakehouse -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-agentic-ai-borderless-data-lakehouse, .gemini/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse, .github/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse and .opencode/skills/google-cloud-solution-agentic-ai-borderless-data-lakehouse in your project.
What does Google Cloud Solution Agentic AI Borderless Data Lakehouse need to run?
Going by SKILL.md and its folder, Google Cloud Solution Agentic AI Borderless Data Lakehouse needs the command-line tools its instructions call (terraform).
Does Google Cloud Solution Agentic AI Borderless Data Lakehouse access the network?
SKILL.md names 4 domains. As links in the text: docs.cloud.google.com, github.com, codelabs.developers.google.com and registry.terraform.io. This is read from the text; nothing was executed.
Is Google Cloud Solution Agentic AI Borderless Data Lakehouse 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 Agentic AI Borderless Data Lakehouse use?
Google Cloud Solution Agentic AI Borderless Data Lakehouse 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 Agentic AI Borderless Data Lakehouse use?
About 2.5k tokens (SKILL.md is roughly 10k 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 2.6k tokens, read only when the agent opens those files.
What are the alternatives to Google Cloud Solution Agentic AI Borderless Data Lakehouse?
Skills that share tags, products or a category with Google Cloud Solution Agentic AI Borderless Data Lakehouse: GCP Audit Logs (sickn33/agentic-awesome-skills, 47k stars), Imaging Data Commons (K-Dense-AI/scientific-agent-skills, 48k stars), Deploying On GCP (ancoleman/ai-design-components, 526 stars) and Analyze Usage (openshift-eng/ai-helpers, 120 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 Agentic AI Borderless Data Lakehouse?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,032 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 8, 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.