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-ai-mlgit 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-ai-ml)<a href="https://agentmods.dev/skills/google/adk-python/bigquery-ai-ml"><img src="https://agentmods.dev/badge/skills/google/adk-python/bigquery-ai-ml/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-ai-ml"><img src="https://agentmods.dev/badge/skills/google/adk-python/bigquery-ai-ml.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 | $0.00131 | $0.00941 |
| Opus 5.5 | $0.00052 | $0.00376 |
| Sonnet 5.5 | $0.00026 | $0.00188 |
| Haiku 4.5 | $0.00013 | $0.00094 |
Grade A, and why
bigquery-ai-ml 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bigquery-ai-ml — 100% identical, 99 lines differ
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BigQuery AI & ML
BigQuery integrates with Vertex AI to provide powerful machine learning and
generative AI capabilities directly within SQL queries using built-in functions
like AI.FORECAST, AI.KEY_DRIVERS, AI.DETECT_ANOMALIES, and AI.GENERATE.
Reference Directory
-
Functions Reference:
- AI.AGG: ai_agg.md - Multi-row semantic aggregation and summarization.
- AI.CAUSAL_EFFECT: ai_causal_effect.md - Quantifies the impact of an intervention on a time series.
- AI.CLASSIFY: ai_classify.md - Classify text.
- AI.DETECT_ANOMALIES: ai_detect_anomalies.md - Detect anomalies.
- AI.EVALUATE: ai_evaluate.md - Evaluate models.
- AI.FORECAST: ai_forecast.md - Time-series forecasting.
- AI.GENERATE: ai_generate.md - Generate text using LLMs.
- AI.GENERATE_EMBEDDING: ai_generate_embedding.md - Generate embeddings.
- AI.GENERATE_TABLE: ai_generate_table.md - Table-valued AI generation.
- AI.IF: ai_if.md - Evaluate semantic conditions.
- AI.KEY_DRIVERS: ai_key_drivers.md - Identifies key drivers, this is a TVF.
- AI.SCORE: ai_score.md - Score data.
- AI.SEARCH: ai_search.md - Semantic search.
- AI.SIMILARITY: ai_similarity.md - Semantic similarity.
- Remote Models: remote_models.md - Working with remote models (Vertex AI).
- CONTRIBUTION_ANALYSIS:
ml_contribution_analysis.md
- Finds contributing factors, key drivers of change. Requires creating a MODEL entity.
- ML.CORRELATION: ml_correlation.md - Calculates correlation between columns, optionally sliced by dimensions.
- ML.DETECT_CHANGE_POINTS: ml_detect_change_points.md - Detects structural breaks or sustained shifts in a time series.
- ML.SEASONALITY: ml_seasonality.md - Extracts seasonal components from a time series.
- ML.TREND: ml_trend.md - Extracts the long-term trend component from a time series.
- VECTOR_SEARCH: vector_search.md - Vector search best practices.
What ships with it
21 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.
- references/ai_agg.md 3.5 KB
- references/ai_causal_effect.md 11 KB
- references/ai_classify.md 3.7 KB
- references/ai_detect_anomalies.md 4.5 KB
- references/ai_evaluate.md 2.2 KB
- references/ai_forecast.md 6.5 KB
- references/ai_generate_embedding.md 1.6 KB
- references/ai_generate_table.md 2.9 KB
- references/ai_generate.md 3.9 KB
- references/ai_if.md 2.0 KB
- references/ai_key_drivers.md 7.1 KB
- references/ai_score.md 1.9 KB
- references/ai_search.md 3.7 KB
- references/ai_similarity.md 2.7 KB
- references/ml_contribution_analysis.md 4.8 KB
- references/ml_correlation.md 8.9 KB
- references/ml_detect_change_points.md 5.5 KB
- references/ml_seasonality.md 6.6 KB
- references/ml_trend.md 4.1 KB
- references/remote_models.md 872 B
- references/vector_search.md 4.0 KB
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 · +29 lines · +100 tokens per session ee7741139346
- 1mo ago First seen · 48 lines · 31 tokens per session scan A bf873812bc11
bigquery-ai-ml is a skill published in the GitHub repository google/adk-python (21,864 stars, last pushed today), licensed Apache-2.0. It adds 131 tokens to every session and 941 once invoked, about $0.0005 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
coverage-tracker
Run a Google Alerts-style keyword coverage tracker. Uses news-search for recent keyword queries, lets the LLM dedupe and classify real features versus junk, stores decisions in SQLite, and alerts only on new real coverage.
byted-bytehouse-hybrid-search
A ByteHouse search tool that combines exact word matching with meaning-based similarity search, then combines the results using a ranking method.
ai-sdk
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI…
byted-milvus
Manages Milvus on Volcano Engine (Volcengine): provision/inspect/scale/delete clusters and run collection + CRUD/search operations via bundled CLIs. Use when the user mentions Milvus + Volcengine/Volcano Engine or asks to operate Milvus there.
big-data-cloud-automation
Automate Big Data Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
building-agents
Use when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server — model-agnostic across OpenAI/Anthropic/Gemini/OSS so a model swap is a config change. NOT vector-store SQL alone (that is postgresdb) or service…