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adk-deployment-specialist

'Deploy and orchestrate Vertex AI ADK agents using A2A protocol. Manages

Install / Use

npx skills add jeremylongshore/tons-of-skills-marketplace --skill adk-deployment-specialist

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

80/100

Category

Automation

Supported Platforms

Universal

Our assessment of adk-deployment-specialist

adk-deployment-specialist scores 80/100 on our quality scale, 1974th of 2,607 Automation skills we index.

Its SKILL.md is 2.7 KB long, well organised into 8 sections and no code examples: a solid amount of guidance for an agent.

With 2,785 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
13/20
Description
12/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 6 days ago, so adk-deployment-specialist is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

adk-deployment-specialist compared with similar skills

All 4 of these similar skills score higher than adk-deployment-specialist; compare them before choosing.

SkillScoreStarsUpdatedFormat
adk-deployment-specialist (this skill)by jeremylongshore802.8k6d agoSKILL.md
Agent-Reachby Panniantong10086.3k14d agoCLAUDE.md
rufloby ruvnet10073.6ktodayCLAUDE.md
Scraplingby D4Vinci10084.6ktodayMCP Server
algorithmic-artby anthropics100177.9k7d agoSKILL.md

Frequently asked questions

How do I install adk-deployment-specialist?
Run npx skills add jeremylongshore/tons-of-skills-marketplace --skill adk-deployment-specialist. The install tabs above show the steps for each supported agent.
Which AI agents does adk-deployment-specialist work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is adk-deployment-specialist safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is adk-deployment-specialist still maintained?
The repository was last updated 6 days ago, so adk-deployment-specialist is actively maintained.

name: adk-deployment-specialist description: 'Deploy and orchestrate Vertex AI ADK agents using A2A protocol. Manages AgentCard discovery, task submission, Code Execution Sandbox, and Memory Bank. Use when asked to "deploy ADK agent" or "orchestrate agents". Trigger with phrases like ''deploy'', ''infrastructure'', or ''CI/CD''.

' allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*) version: 2.29.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT effort: high argument-hint: <agent-name or project-id> tags:

  • ai
  • deployment
  • ci-cd compatibility: Designed for Claude Code

Adk Deployment Specialist

Overview

Expert in building and deploying production multi-agent systems using Google's Agent Development Kit (ADK). Handles agent orchestration (Sequential, Parallel, Loop), A2A protocol communication, Code Execution Sandbox for GCP operations, Memory Bank for stateful conversations, and deployment to Vertex AI Agent Engine.

Prerequisites

  • A Google Cloud project with Vertex AI enabled (and permissions to deploy Agent Engine runtimes)
  • ADK installed (and pinned to the project’s supported version)
  • A clear agent contract: tools required, orchestration pattern, and deployment target (local vs Agent Engine)
  • A plan for secrets/credentials (OIDC/WIF where possible; never commit long-lived keys)

Instructions

  1. Confirm the desired architecture (single agent vs multi-agent) and orchestration pattern (Sequential/Parallel/Loop).
  2. Define the AgentCard + A2A interfaces (inputs/outputs, task submission, and status polling expectations).
  3. Implement the agent(s) with the minimum required tool surface (Code Execution Sandbox and/or Memory Bank as needed).
  4. Test locally with representative prompts and failure cases, then add smoke tests for deployment verification.
  5. Deploy to Vertex AI Agent Engine and validate the generated endpoints (/.well-known/agent-card, task send/status APIs).
  6. Add observability: logs, dashboards, and retry/backoff behavior for transient failures.

Output

  • Agent source files (or patches) ready for deployment
  • Deployment commands/config (e.g., vertexai.Client.agent_engines.create() invocation + required parameters)
  • A verification checklist for Agent Engine endpoints (AgentCard + task APIs) and security posture

Error Handling

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed examples.

Resources

  • ADK docs:
  • Workload Identity (CI/CD): https://cloud.google.com/iam/docs/workload-identity-federation
  • A2A / AgentCard patterns: see 000-docs/6767-a-SPEC-DR-STND-claude-code-plugins-standard.md

Related Skills

View on GitHub
GitHub Stars2.8k
CategoryAutomation
Updated6d ago
Forks404

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions