google-adk-python
Google Agent Development Kit (ADK) for Python. Capabilities: AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration (Google Search, Code Execution), Vertex AI deployment, agent evaluation, human-in-the-loop flows. Actions: build, create, deploy, evaluate, orchestrate AI agents. Keywords: Google ADK, Agent Development Kit, AI agent, multi-agent system, LlmAgent, SequentialAgent, ParallelAgent, LoopAgent, tool integration, Google Search, Code Execution, Vertex AI, Cloud Run, agent evaluation, human-in-the-loop, agent orchestration, workflow agent, hierarchical coordination. Use when: building AI agents, creating multi-agent systems, implementing workflow pipelines, integrating LLM agents with tools, deploying to Vertex AI, evaluating agent performance, implementing approval flows.
68
82%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that comprehensively covers the Google ADK framework with specific capabilities, concrete actions, extensive keywords, and explicit trigger conditions. The description uses proper third-person voice throughout and provides clear differentiation from other AI/agent-related skills through Google-specific terminology and deployment targets.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions including 'AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration, Vertex AI deployment, agent evaluation, human-in-the-loop flows' with explicit action verbs 'build, create, deploy, evaluate, orchestrate'. | 3 / 3 |
Completeness | Clearly answers both 'what' (capabilities and actions sections) AND 'when' with an explicit 'Use when:' clause listing seven specific trigger scenarios like 'building AI agents', 'creating multi-agent systems', 'deploying to Vertex AI'. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say including 'Google ADK', 'AI agent', 'multi-agent system', specific agent types (LlmAgent, SequentialAgent, ParallelAgent, LoopAgent), deployment targets (Vertex AI, Cloud Run), and common task phrases like 'building AI agents' and 'workflow pipelines'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche around Google's specific ADK framework, named agent types (LlmAgent, SequentialAgent, etc.), and Google-specific deployment targets (Vertex AI, Cloud Run). Unlikely to conflict with generic coding or other AI framework skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides solid, actionable guidance for Google ADK with excellent code examples that are immediately executable. The main weaknesses are moderate verbosity in explanatory sections and missing validation/verification steps in the implementation workflow. The content would benefit from tighter prose and explicit checkpoints for deployment and testing operations.
Suggestions
Remove or significantly condense the 'When to Use This Skill' and 'Core Concepts' sections - Claude can infer these from the code examples
Add explicit validation steps to the Implementation Workflow, especially for steps 5-7 (e.g., 'Verify agent responds correctly before deploying', 'Test deployment endpoint returns expected response')
Consider splitting detailed deployment instructions and evaluation guidance into separate reference files, keeping SKILL.md as a concise overview
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains some unnecessary sections like 'When to Use This Skill' which repeats information Claude can infer, and the 'Core Concepts' section explains basic agent types that could be more concise. However, the code examples are appropriately lean. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples for all major patterns including single agents, multi-agent systems, custom tools, sequential/parallel workflows, and human-in-the-loop. Installation commands are concrete and complete. | 3 / 3 |
Workflow Clarity | The 'Implementation Workflow' section provides a clear 8-step sequence, but lacks validation checkpoints or feedback loops. For deployment operations and agent testing, there are no explicit verification steps or error recovery guidance. | 2 / 3 |
Progressive Disclosure | Content is reasonably organized with clear sections, but the document is somewhat monolithic. The Resources section provides external links, but detailed topics like evaluation, deployment specifics, and advanced tool integration could be split into separate reference files. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- samhvw8/dot-claude
- Commit
28c7616
- Reviewed
Table of Contents
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