Meta Test Orchestrator
GoogleCloudPlatform/DataflowTemplates
Template-agnostic Orchestrator Skill for generating and executing exhaustive testing suites for any migration template.
Official agent skill
Google Cloud Solution Agentic AI Bidirectional Streaming
by google in google/skills
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and…
Install Google Cloud Solution Agentic AI Bidirectional Streaming
$ npx skills add google/skills --skill google-cloud-solution-agentic-ai-bidirectional-streaming -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-bidirectional-streaming --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-bidirectional-streaming .claude/skills/google-cloud-solution-agentic-ai-bidirectional-streaming && 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-bidirectional-streaming" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming into .claude/skills/google-cloud-solution-agentic-ai-bidirectional-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-bidirectional-streaming", 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-bidirectional-streamingType 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-bidirectional-streaming -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-bidirectional-streaming --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-bidirectional-streaming .agents/skills/google-cloud-solution-agentic-ai-bidirectional-streaming && 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-bidirectional-streaming" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming into .agents/skills/google-cloud-solution-agentic-ai-bidirectional-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-bidirectional-streaming", 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-bidirectional-streaming -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-bidirectional-streaming --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-bidirectional-streaming .cursor/skills/google-cloud-solution-agentic-ai-bidirectional-streaming && 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-bidirectional-streaming" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming into .cursor/skills/google-cloud-solution-agentic-ai-bidirectional-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-bidirectional-streaming", 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-bidirectional-streaming--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-bidirectional-streaming -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills google-cloud-solution-agentic-ai-bidirectional-streaming --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-bidirectional-streaming .gemini/skills/google-cloud-solution-agentic-ai-bidirectional-streaming && 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-bidirectional-streaming" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming into .gemini/skills/google-cloud-solution-agentic-ai-bidirectional-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-bidirectional-streaming", 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-bidirectional-streamingInstalls 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-bidirectional-streaming -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-bidirectional-streaming .github/skills/google-cloud-solution-agentic-ai-bidirectional-streaming && 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-bidirectional-streaming" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming into .github/skills/google-cloud-solution-agentic-ai-bidirectional-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-bidirectional-streaming", 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-bidirectional-streaming -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-bidirectional-streaming --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-bidirectional-streaming .opencode/skills/google-cloud-solution-agentic-ai-bidirectional-streaming && 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-bidirectional-streaming" agent skill from https://github.com/google/skills/tree/main/skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming into .opencode/skills/google-cloud-solution-agentic-ai-bidirectional-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "google-cloud-solution-agentic-ai-bidirectional-streaming", 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-bidirectional-streaming- GitHub stars
- 21k
- Token cost
- ~2.3k tokens
- SKILL.md length
- 930 words
- Files
- 4 (incl. references, assets)
- Skills in repo
- 145
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and…
- Works in 4 steps: Requirements discovery and analysis → Solution design → Implementation plan → …
- Simple text-based chat applications
- Calls terraform
- Workloads without real-time streaming requirements
What it does
Google Cloud Solution Agentic AI Bidirectional Streaming is an agent skill from google/skills, published by the product's own GitHub organization. Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat…
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 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 Agent Workflows. It works with Google Cloud. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
When your agent uses it
- Simple text-based chat applications
- Workloads without real-time streaming requirements
Example prompts
- “Use the google-cloud-solution-agentic-ai-bidirectional-streaming skill to guide agents to interactively discover customer requirements for live…”
- “/google-cloud-solution-agentic-ai-bidirectional-streaming”
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 8a1ac05. 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.comadk.devcodelabs.developers.google.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 Agentic AI Bidirectional Streaming loads about 2.3k tokens when it runs, and up to ~4.7k if it reads all its reference files. Until then it costs about 159 tokens; SKILL.md has 930 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 8a1ac05, republished under its Apache-2.0 licence (© google). 930 words, ~2,309 tokens.
Download SKILL.mdSave it as
.claude/skills/google-cloud-solution-agentic-ai-bidirectional-streaming/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- google-cloud-solution-agentic-ai-bidirectional-streaming
- description
- Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.
- metadata.version
- 1.0.0
- metadata.category
- MultiProductSolutions
Live bidirectional multimodal streaming agentic AI solution
This skill guides agents through the workflow to design and implement a tailored multi-product solution in the cloud for a live, bidirectional multimodal streaming workload, use case, or requirement.
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 the primary input modalities (audio, video, or text) and what is the target latency for real-time, narrated feedback?
- Do you require real-time safety monitoring, hazard detection, or visual inspection? If so, then what specific safety hazards, operational risks, or incorrect steps need to be monitored and detected in the video stream?
- What existing systems, knowledge bases, product documentation, or schematic repositories must the AI agents access for grounded guidance?
- What are the client-side device constraints and network limitations?
Step 2: Identify components: Based on the requirements analysis, identify the components of the workload and their relationships. Also identify any cross-cloud 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. The technical decomposition must break down the solution into logical components.
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:
- Enable live bidirectional multimodal streaming
- Multi-agent AI system in Google Cloud
- Choose your agentic AI architecture components
- Multi-agent private networking patterns in 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 and agentic design pattern, identify the appropriate Google Cloud products and features, based on the guidelines in references/product-mapping.md.
Step 3: Create architecture diagram: Generate an architecture diagram in Mermaid format: https://github.com/mermaid-js/mermaid.
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 (391 more words)Show less
Phase 3: Implementation plan
Step 1: Retrieve relevant implementation resources:
- Host AI agents on Cloud Run
- Triggering Cloud Run with WebSockets
- Start and Manage a Gemini Live API Session
- ADK Streaming Tools
- ADK Streaming Configuration
- Codelab: Way Back Home Level 4 instructions (and solution code)
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, like 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. Update deployment instructions in
solution-architecture-guide.md, based on the template in assets/output-template.md.Step 5: Request review: Present the generated deployment instructions to the user for feedback and confirmation.
Step 6: Iterate: If the user requests changes, then 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, such as 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
solution-architecture-guide.md, based on the template in assets/output-template.md.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 3 other files (references, assets) in skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming of google/skills.
- SKILL.md
- assets/output-template.md
- references/design-recommendations.md
- references/product-mapping.md
Open the folder on GitHubat commit 8a1ac05
Compare with similar skills
Google Cloud Solution Agentic AI Bidirectional Streaming 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 Bidirectional Streaming this skillgoogle/skills | 21k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Meta Test OrchestratorGoogleCloudPlatform/DataflowTemplates | 1.3k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Harness Secretsruvnet/metaharness | 688 | — | ~345 | Automated safety check: Pass | MIT | |
| Bootstrap Google Toolsgoogle/adk-recipes | 10k | — | ~5k | Automated safety check: Warn | Apache-2.0 | |
| Validate Harnessruvnet/metaharness | 688 | — | ~372 | Automated safety check: Pass | MIT | |
| GCP Compute Opsautomateyournetwork/netclaw | 674 | — | ~1.7k | Automated safety check: Pass | Apache-2.0 |
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Questions about Google Cloud Solution Agentic AI Bidirectional Streaming
What does Google Cloud Solution Agentic AI Bidirectional Streaming do?
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and…. Google Cloud Solution Agentic AI Bidirectional Streaming is an agent skill from google/skills, published by the product's own GitHub organization. Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring.
When should I use Google Cloud Solution Agentic AI Bidirectional Streaming?
Google Cloud Solution Agentic AI Bidirectional Streaming fits situations like: simple text-based chat applications; workloads without real-time streaming requirements.
How do I install Google Cloud Solution Agentic AI Bidirectional Streaming in Claude Code?
Run `npx skills add google/skills --skill google-cloud-solution-agentic-ai-bidirectional-streaming -a claude-code`. Or copy the skill folder (skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming in google/skills) into .claude/skills/google-cloud-solution-agentic-ai-bidirectional-streaming in your project. Claude Code loads it when a task matches its description.
How do I install Google Cloud Solution Agentic AI Bidirectional Streaming in Codex?
Run `npx skills add google/skills --skill google-cloud-solution-agentic-ai-bidirectional-streaming -a codex`. Or copy the skill folder (skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming in google/skills) into .agents/skills/google-cloud-solution-agentic-ai-bidirectional-streaming in your project. Codex loads it when a task matches its description.
Can I use Google Cloud Solution Agentic AI Bidirectional Streaming 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-bidirectional-streaming -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-bidirectional-streaming, .gemini/skills/google-cloud-solution-agentic-ai-bidirectional-streaming, .github/skills/google-cloud-solution-agentic-ai-bidirectional-streaming and .opencode/skills/google-cloud-solution-agentic-ai-bidirectional-streaming in your project.
What does Google Cloud Solution Agentic AI Bidirectional Streaming need to run?
Going by SKILL.md and its folder, Google Cloud Solution Agentic AI Bidirectional Streaming needs the command-line tools its instructions call (terraform).
Does Google Cloud Solution Agentic AI Bidirectional Streaming access the network?
SKILL.md names 4 domains. As links in the text: docs.cloud.google.com, github.com, adk.dev and codelabs.developers.google.com. This is read from the text; nothing was executed.
Is Google Cloud Solution Agentic AI Bidirectional Streaming 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 Bidirectional Streaming use?
Google Cloud Solution Agentic AI Bidirectional Streaming 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 Bidirectional Streaming use?
About 2.3k tokens (SKILL.md is roughly 9.2k 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 2.4k tokens, read only when the agent opens those files.
What are the alternatives to Google Cloud Solution Agentic AI Bidirectional Streaming?
Skills that share tags, products or a category with Google Cloud Solution Agentic AI Bidirectional Streaming: Meta Test Orchestrator (GoogleCloudPlatform/DataflowTemplates, 1.3k stars), Harness Secrets (ruvnet/metaharness, 688 stars), Bootstrap Google Tools (google/adk-recipes, 10k stars) and Validate Harness (ruvnet/metaharness, 688 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 Bidirectional Streaming?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 20,994 GitHub stars. The repository holds 145 skills in this directory. The repository was last updated on October 6, 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.