Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ihatesea69/kiro-kit --skill google-adk-pythongit clone --depth 1 https://github.com/ihatesea69/kiro-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ihatesea69/kiro-kit/google-adk-python)<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/google-adk-python"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/google-adk-python/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/google-adk-python"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/google-adk-python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.00342 |
| Opus 5.5 | $0.00017 | $0.00137 |
| Sonnet 5.5 | $0.00008 | $0.00068 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
google-adk-python scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Google ADK Python
Activate this skill when building AI agents with Google's Agent Development Kit.
When to Use
- Creating multi-agent orchestration systems
- Building tool-using AI agents
- Integrating with Google Cloud AI services
- Designing agent communication protocols
- Implementing RAG pipelines with ADK
Core Concepts
- Agent: Autonomous unit with instructions, tools, and model
- Tool: Function callable by the agent (Python functions, APIs)
- Session: Conversation state management
- Runner: Execution engine for agent invocations
- Orchestrator: Multi-agent coordination patterns
Setup
from google.adk import Agent, Tool, Runner
agent = Agent(
name="data-analyst",
model="gemini-2.0-flash",
instructions="You analyze datasets and produce insights.",
tools=[query_database, generate_chart],
)
runner = Runner(agent=agent)
response = runner.run("Analyze Q4 sales trends")
Patterns
- Sequential agent chains for multi-step analysis
- Parallel agents for independent data processing
- Supervisor pattern for quality control
- Tool composition for complex workflows
Rules
- Define clear agent boundaries and responsibilities
- Use typed tool parameters with Pydantic models
- Handle tool failures gracefully with retries
- Keep agent instructions concise and focused
- Test agents with deterministic inputs first
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 57 lines · 42 tokens per session scan A 70bc2d62f0df
google-adk-python is a skill published in the GitHub repository ihatesea69/kiro-kit (19 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 342 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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