Agent Development Kit is a Python framework for building, evaluating, deploying, and coordinating AI agents as software applications. Developers use it to compose workflows, delegate tasks between specialized agents, manage state, and connect agents to tools while choosing supported models and deployment environments. The catalogue add-ons provide workflows for using the toolkit.
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 google/adk-python --skill adk-reviewgit clone --depth 1 https://github.com/google/adk-pythonWrote 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/google/adk-python/adk-review)<a href="https://agentmods.dev/skills/google/adk-python/adk-review"><img src="https://agentmods.dev/badge/skills/google/adk-python/adk-review/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/google/adk-python/adk-review"><img src="https://agentmods.dev/badge/skills/google/adk-python/adk-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00164 | $0.01650 |
| Opus 5.5 | $0.00066 | $0.00660 |
| Sonnet 5.5 | $0.00033 | $0.00330 |
| Haiku 4.5 | $0.00016 | $0.00165 |
Grade A, and why
adk-review 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 7d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADK Change Reviewer
Review the working-tree diff against the seven dimensions below, report what is wrong, and stop. Fix only what the user then asks you to fix.
Workflow
git statusandgit diff(add--stagedfor staged work) to get the exact set of added, modified, and deleted files.- Review the diff file by file against the checklist.
- Emit the report in the format below.
- Stop. Do not edit any file, and do not offer to. Wait for the user to ask.
- If the user asks for fixes, apply them, then re-run the affected tests
(
pytest tests/unittests/{path}) andpre-commit run --files {paths}.
Step 4 is the part that is easy to get wrong: an unrequested fix buries the findings the user asked for and mixes review output with new, unreviewed edits.
Checklist
1. Correctness
- Types: no new
mypyerrors. CI diffsmypyoutput against the base branch and fails only on newly introduced errors, so a pre-existing error in a file you touched is not a blocker but a new one is. - Imports: no circular imports; absolute imports where the module already uses them.
- Exceptions: no bare
except:; catch a specific type and log with enough context to identify the caller. - Type discrimination: check an object's type before reading a
type-specific attribute (for example confirm a node is an
LlmAgentbefore inspectingmode), so an unexpected node type raises nothing. - Boundaries:
None, empty collections, zero, and empty strings are handled by validation or a fallback default. - Preconditions: state invariants are checked before the core logic runs.
2. Design
- Complexity: functions or classes that would be clearer split up.
- Coupling: high cohesion, low coupling; no anti-patterns introduced.
- Performance: redundant computation, repeated I/O in a loop, or allocations that scale with input where they need not.
- Security: inputs validated, sensitive data not logged, no injection, resource exhaustion, or exposure of internal state.
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.
- 7d ago Changed · +6 lines 47a4127cd87b
- 14d ago Changed · +11 lines 90b719df88e8
- 24d ago Changed · +8 lines 07fc992d2a3b
- 1mo ago First seen · 119 lines · 164 tokens per session scan A 85933af5c1a0
adk-review is a skill published in the GitHub repository google/adk-python (21,885 stars, last pushed today), licensed Apache-2.0. It adds 164 tokens to every session and 1,650 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
adk-go-self-review
Review an ADK Go change the way a maintainer will — a fresh-context pass over the whole diff, five lenses (correctness and tests, scope, simplicity, style, adk-python parity), and the mutation check that proves your tests pin the change. Use before opening a PR, before any later push that changes code, and when asked…
sage-review
Deep, platform-neutral code review for PRs and CRs in ONE thorough single pass — design reasoning (Problem Worth Solving & Solution Fit) as one dimension alongside the 9 code-level dimensions, with chain-of-consequences, self-critique, and draft-only comments. The single app-owned source of truth.
learn-from-sage
Detection-gap (miss) analysis for Code Review Sage. Learn from shipped fixes, acted-on human comments, and design outcomes to close reviewer blind spots. Inline during review stages a candidate; a human triggers a one-shot AI consolidation into the live ruleset.
review-fix-signoff-loop
Use when writing Agent Relay or Ricky workflows that must loop review, fix, and validation with fresh agent context until independent signoff agents, typically Claude and Codex, both agree the work is comprehensively complete. Covers fresh-context iterations, repairable gates, dual reviewer verdict contracts…
code-review
Reviews code changes and provides actionable feedback. Use when the user asks to review a PR, diff, commit, or code changes. Triggers on "/review", "review this PR", "review my changes", "code review".
code-review
Reviews code diffs and files for security vulnerabilities (OWASP Top 10), error handling, complexity, naming conventions, and performance issues. Use when the user asks to review a PR, pull request, diff, merge request, or code changes.