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 bigquery-graphgit 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/bigquery-graph)<a href="https://agentmods.dev/skills/google/adk-python/bigquery-graph"><img src="https://agentmods.dev/badge/skills/google/adk-python/bigquery-graph/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/bigquery-graph"><img src="https://agentmods.dev/badge/skills/google/adk-python/bigquery-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 167 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00052 | $0.04713 |
| Opus 5.5 | $0.00021 | $0.01885 |
| Sonnet 5.5 | $0.00010 | $0.00943 |
| Haiku 4.5 | $0.00005 | $0.00471 |
Grade A, and why
bigquery-graph 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.
How it starts
The opening of the file, as written. The whole thing — 503 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Query Language (GQL) Query Generation Guidelines
You are querying a property graph consisting of nodes and edges. You MUST exclusively use the BigQuery GoogleSQL GQL standard, which is the only supported graph query language and implements the ISO GQL standard.
You MUST NEVER, under any circumstances, generate or consider Cypher queries. Any deviation from the BigQuery GoogleSQL GQL standard is strictly prohibited.
Reference Directory
- Semantic Graph Guidelines: Guidelines for generating SQL queries against a semantic graph.
- Graph Schema Best Practices: Best practices for defining BigQuery property graph schemas.
- Graph Schema DDL Reference: DDL syntax reference for property graphs.
- Graph Limitations & Feature Parity: Current limitations and feature parity for BigQuery Graph.
- Graph Schema DDL Advisor: Assists in defining and optimizing schemas.
Pre-generation Checklist
Before generating any GQL, you MUST:
- Identify Output Intent: Determine if the user intends to visualize a
graph network (requires
TO_JSON()) or view tabular data (requires specific properties). - Verify Language Standard: Confirm the query will use BigQuery GoogleSQL GQL. NEVER use Cypher.
Core Directives for Agent Query Generation
When generating graph queries, you must adhere to the following global directives:
- Default Query Construction (Standalone GQL): Write standalone GQL
queries using the
RETURNstatement natively. Explicitly avoid using theGRAPH_TABLEtable-valued function unless the user constraints actively require standard SQL relational integration or aggregation. - Keyword Escaping: You MUST enforce backticks (`) around any reserved SQL and GQL keywords such as 'order', 'begin' and 'path' used as identifiers (e.g., column names, label names, variable names).
- Strictly Follow Graph Schema: Ensure all labels (e.g.,
:Person,:Account) and properties (e.g.,n.id,e.amount) used in the query strictly match the provided graph schema. Do NOT guess or hallucinate schema elements. - Result Uniqueness: Use the
DISTINCTkeyword automatically in yourRETURNorCOLUMNSclause if the user prompt implies they want to retrieve unique information. - Graph Path Variables: When a query involves "paths", "path traversal",
"path finding", or finding relationships between nodes, you MUST assign
the matched pattern to a path variable (e.g.,
MATCH p = ...).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 503 lines · 52 tokens per session scan A aa26739a4653
bigquery-graph is a skill published in the GitHub repository google/adk-python (21,864 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 4,713 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 2026-08-30.
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