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 jpantsjoha/ai-native-developer-experience --skill adk-expertgit clone --depth 1 https://github.com/jpantsjoha/ai-native-developer-experienceWrote 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/jpantsjoha/ai-native-developer-experience/adk-expert)<a href="https://agentmods.dev/skills/jpantsjoha/ai-native-developer-experience/adk-expert"><img src="https://agentmods.dev/badge/skills/jpantsjoha/ai-native-developer-experience/adk-expert.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00047 | $0.00839 |
| Opus 5 | $0.00023 | $0.00419 |
| Sonnet 5 | $0.00009 | $0.00168 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade A, and why
adk-expert 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADK Expert
ADK is a mental model for agent composition, not a framework to learn. The patterns transfer to any orchestration foundation.
This skill covers how to think about agent boundaries, orchestration topology, and tool seams using Google ADK principles. It is not a tutorial on SDK methods — the official docs at adk.dev own that. This skill covers the architecture of agent systems.
When to use
- Designing a multi-agent system on Google ADK
- Deciding where to draw agent boundaries
- Choosing between orchestration topologies (supervisor-worker vs peer-to-peer vs sequential)
- Reviewing an existing ADK-based system for structural problems
- Integrating MCP servers or external tools into an ADK agent graph
Procedure
-
Verify current ADK documentation — before writing any agent topology or referencing API surface, fetch the latest docs from adk.dev. ADK evolves; training data lags.
-
Define agent responsibilities first — each agent in the system must have:
- A single, nameable responsibility
- A defined input contract (what it receives)
- A defined output contract (what it produces)
- A declared set of tools it may use (no raw DB access; use bounded tool seams)
-
Choose the orchestration topology:
Topology When to use Trade-off Supervisor → Worker Audit trails required; routing logic is complex Adds latency; supervisor is a bottleneck Sequential pipeline Tasks are strictly ordered; each step feeds the next Simple but no parallelism Parallel fan-out Independent sub-tasks that merge at a synthesis step Fast; coordination overhead at merge Peer-to-peer Speed over governance; tasks are loosely coupled Hard to audit; compliance risk -
Design the tool seams — tools are the boundary between an agent and the external world. Each tool should:
- Have a typed schema (inputs and outputs)
- Enforce the agent's permission scope (least-privilege)
- Be independently testable
- Return structured errors, not raw exceptions
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.
- 8d ago First seen · 64 lines · 47 tokens per session scan A d44f7a5bd2f2
adk-expert is a skill published in the GitHub repository jpantsjoha/ai-native-developer-experience (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 839 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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