> This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini Enterprise", "publish to Gemini Enterprise", or needs guidance on the agents-cli publish gemini-enterprise command. Also use when the user wants to "manage agents in Agent Registry" or "list/update/delete registered agents". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the Google ADK (Agent Development Kit) skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
npx skills add https://github.com/google/agents-cli --skill google-agents-cli-publish
> Requires: A deployed agent. For Agent Runtime, deployment_metadata.json (created by agents-cli deploy) enables auto-detection. For Cloud Run or GKE, provide the agent card URL and flags directly.
deployment_metadata.json (Agent Runtime only) — Created automatically by agents-cli deploy; contains the agent runtime ID, deployment target, the A2A flag, and the agent directoryroles/run.servicesInvoker granted to the Discovery Engine service account (service-<PROJECT_NUMBER>@gcp-sa-discoveryengine.iam.gserviceaccount.com) on the Cloud Run service.Every scaffolded agent serves the Agent-to-Agent protocol. A2A is the default — and only — registration type on Cloud Run and GKE, which have no reasoning engine, so Gemini Enterprise registers them over A2A. Pass the agent card URL and the command fetches the card and registers it; display name and description default to the card's name/description.
# A2A on Cloud Run / GKE
agents-cli publish gemini-enterprise \
--agent-card-url https://my-service-abc123.us-east1.run.app/a2a/app/.well-known/agent-card.json \
--gemini-enterprise-app-id projects/123456/locations/global/collections/default_collection/engines/my-app
Pass --display-name / --description to override the card defaults. For Agent Runtime, use ADK registration (below).
This is the default and recommended registration for Agent Runtime deployments: Gemini Enterprise invokes the agent natively via :streamQuery on its reasoning engine resource, authenticating end-to-end. Under the hood, :streamQuery dispatches to the AdkApp's streaming_agent_run_with_events method — when debugging an ADK invocation, search the runtime's reasoning_engine_stderr logs for that method name to trace the failure. It's also the path to use when the agent needs an OAuth authorization (--authorization-id). The agent is registered directly via its reasoning engine resource name; no agent card URL is needed.
agents-cli publish gemini-enterprise \
--registration-type adk \
--agent-runtime-id projects/123456/locations/us-east1/reasoningEngines/789 \
--gemini-enterprise-app-id projects/123456/locations/global/collections/default_collection/engines/my-app \
--display-name "My Agent" \
--description "Handles customer queries" \
--tool-description "Answers questions about products"
The command is non-interactive by default — pass all required values via flags or environment variables. This makes it safe for CI/CD pipelines.
agents-cli publish gemini-enterprise \
--agent-runtime-id "$AGENT_RUNTIME_ID" \
--gemini-enterprise-app-id "$GEMINI_ENTERPRISE_APP_ID" \
--display-name "Production Agent" \
--registration-type adk
Most flags have an env var alternative (--metadata-file, --interactive, and --list do not):
export AGENT_RUNTIME_ID="projects/123456/locations/us-east1/reasoningEngines/789"
export GEMINI_ENTERPRISE_APP_ID="projects/123456/locations/global/collections/default_collection/engines/my-app"
export GEMINI_DISPLAY_NAME="Production Agent"
export GEMINI_DESCRIPTION="Handles customer queries"
agents-cli publish gemini-enterprise
--interactive)Pass --interactive (or -i) to be guided through any missing values with interactive prompts. The command will list available Gemini Enterprise apps, offer to auto-detect the agent runtime ID from metadata, and prompt for display name and description.
agents-cli publish gemini-enterprise --interactive
| Flag | Env Var | Description |
|------|---------|-------------|
| --agent-runtime-id | AGENT_RUNTIME_ID | Agent Runtime resource name (auto-detected from deployment_metadata.json) |
| --gemini-enterprise-app-id | ID or GEMINI_ENTERPRISE_APP_ID | Gemini Enterprise app full resource name |
| --display-name | GEMINI_DISPLAY_NAME | Display name in Gemini Enterprise |
| --description | GEMINI_DESCRIPTION | Agent description |
| --tool-description | GEMINI_TOOL_DESCRIPTION | Tool description (ADK mode only, defaults to description) |
| --registration-type | REGISTRATION_TYPE | adk or a2a (defaults to adk on Agent Runtime, a2a on Cloud Run / GKE) |
| --agent-card-url | AGENT_CARD_URL | Agent card URL for A2A registration |
| --deployment-target | DEPLOYMENT_TARGET | agent_runtime, cloud_run, or gke (sets the default registration type — ADK on Agent Runtime, A2A on Cloud Run / GKE — and the A2A auth method) |
| --project-id | GOOGLE_CLOUD_PROJECT | GCP project ID for billing |
| --project-number | PROJECT_NUMBER | GCP project number (used for Gemini Enterprise lookup) |
| --authorization-id | GEMINI_AUTHORIZATION_ID | OAuth authorization resource name |
| --metadata-file | — | Path to deployment metadata (default: deployment_metadata.json) |
| --interactive / -i | — | Enable interactive prompts |
| --list | — | List Gemini Enterprise apps in the current project and exit |
When deployment_metadata.json exists, the command automatically:
remote_agent_runtime_id):streamQuery) on Agent Runtime, and A2A on Cloud Run / GKE (which have no reasoning engine). Override with --registration-type.This means that for the simplest case (an agent on Agent Runtime, registered as ADK), you only need to provide the Gemini Enterprise app ID:
agents-cli publish gemini-enterprise \
--gemini-enterprise-app-id projects/123456/locations/global/collections/default_collection/engines/my-app
Agent Runtime deployments may encounter "Session not found" errors with google-cloud-aiplatform versions <= 1.128.0. In interactive mode (--interactive), the command checks the SDK version from uv.lock and offers to upgrade. In programmatic mode, ensure your SDK is up to date before registering.
Agent Registry (Preview) is the Google Cloud fleet-wide record of your agents.
Agents deployed to a managed runtime (Agent Runtime on Gemini Enterprise
Agent Platform) are auto-registered — no extra step after agents-cli deploy.
Manage them with gcloud (requires roles/agentregistry.editor):
# List / filter
gcloud alpha agent-registry agents list --project PROJECT --location LOCATION
gcloud alpha agent-registry agents list --filter="displayName:my-agent"
# Inspect
gcloud alpha agent-registry agents describe AGENT_NAME
# Update endpoint/metadata — edit the Service resource, not the Agent
gcloud alpha agent-registry services update AGENT_NAME \
--display-name "..." --description "..." \
--interfaces "url=ENDPOINT_URL,protocol=HTTP_JSON"
# Remove: delete the underlying runtime agent (auto-registered) OR, for
# manually registered agents, delete the Service resource
gcloud alpha agent-registry services delete AGENT_NAME
Docs: https://docs.cloud.google.com/agent-registry/manage-agents
| Issue | Solution |
|-------|----------|
| "Session not found" after registration | SDK version issue — upgrade google-cloud-aiplatform (see SDK Compatibility above), redeploy, then re-register |
| --registration-type is required | Non-interactive mode needs --registration-type when no deployment_metadata.json exists |
| "Gemini Enterprise App ID is required" | Provide --gemini-enterprise-app-id or set the ID / GEMINI_ENTERPRISE_APP_ID env var |
| Re-publishing the same agent | Registration is idempotent — re-running updates the existing registration in place instead of creating a duplicate |
| HTTP 403 on registration | Check that your account has Discovery Engine Editor permissions on the Gemini Enterprise project |
| Debugging ADK invocation failures on Agent Runtime | Gemini Enterprise calls the agent via the AdkApp's streaming_agent_run_with_events method (the native :streamQuery contract). Grep the runtime's reasoning_engine_stderr logs for streaming_agent_run_with_events to find the underlying error |
| "Could not fetch agent card" | Verify the agent is running and the URL is correct; for Cloud Run, ensure gcloud auth login is done |
/google-agents-cli-deploy — Deployment targets, CI/CD pipelines, and production workflows (also covers Agent Gateway governed ingress/egress and Semantic Governance awareness)/google-agents-cli-workflow — Development workflow, coding guidelines, and operational rules/google-agents-cli-scaffold — Project creation and enhancement with agents-cli scaffold create / scaffold enhanceAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take google/google-agents-cli-publish from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.