Provides Agent Skills, CLI, and stdio MCP tooling for BigQuery ML and BigQuery AI on Google Cloud, enabling task triage, exact syntax lookup for models and ML./AI. functions, search across reference guides and narratives, and access to live-verified notebooks, SQL scripts, workflows, and pipelines.
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Production Agent Skills, zero-dependency CLI, stdio MCP server & Google ADK tools for BigQuery ML, BigQuery AI, and BigQuery MLOps — distilled directly from 106 live-executed notebooks and 89 SQL scripts in Google Cloud.
106 live-verified notebooks · 89 SQL scripts · 4 Composable Agent Skills · 20 Reference & Playbook Guides · 139 <25 KB syntax cards · zero-dependency CLI, MCP & ADK
%%{init: {'flowchart': {'wrappingWidth': 380, 'nodeSpacing': 24, 'rankSpacing': 28}}}%%
flowchart TD
subgraph GT["1. Ground-Truth Notebooks, SQL & Reference Hubs (GitHub)"]
direction LR
BQML["<b>bigquery-ml/</b> (61 Notebooks · 58 SQL)<br/>23 CREATE MODEL · 12 ML.* Families<br/>18 Applied Workflows · 8 MLOps Pipelines"]
BQAI["<b>bigquery-ai/</b> (45 Notebooks · 31 SQL)<br/>31 AI.* & Cloud AI SQL Functions<br/>14 Multi-Function Workflows"]
REFS["<b>13 Deep Reference Guides</b><br/>bigquery-ml/reference/ (5 Guides)<br/>bigquery-ai/reference/ (8 Guides)"]
BQML ~~~ BQAI ~~~ REFS
end
subgraph SKILLS["2. 4 Composable Agent Skills (skills/ · .agents/skills/ · .claude/skills/)"]
direction TB
ROUTER["<b>bigquery-data-science</b> (Umbrella Router)<br/>ML.* vs AI.* Matrix · 10 Head-to-Head Comparisons<br/>6 Multi-Skill Playbooks · 7-Stage Lifecycle Navigator"]
S_ML["<b>bigquery-ml</b> (Trained Models)<br/>23 CREATE MODEL · 49 ML.* Functions<br/>Preprocessing · Causal · Explain"]
S_AI["<b>bigquery-ai</b> (Foundation Models)<br/>31 AI.* & Cloud AI · TimesFM · TabFM<br/>Embeddings · Vector & Hybrid Search"]
S_OPS["<b>bigquery-mlops</b> (Production Ops)<br/>8 Orchestrators · Vertex AI Registry<br/>EXPORT / IMPORT · Drift & Feature Store"]
ROUTER --> S_ML
ROUTER --> S_AI
ROUTER --> S_OPS
end
subgraph LENS["3. 4-Lens Progressive Disclosure & Zero-Dependency Delivery"]
direction LR
L12["<b>Lens 1 & Lens 2 — Route & Compare</b><br/>4 SKILL.md Routers (~8 KB)<br/>20 Decision & Playbook Guides (~25 KB)"]
L34["<b>Lens 3 & Lens 4 — Write & Build</b><br/>139 Surgical Syntax Cards (< 25 KB)<br/>106 Worked Walkthroughs (~20 KB)"]
DELIV["<b>Zero-Dependency CLI, MCP & ADK</b><br/>uvx bigquery-data-science install · mcp<br/>triage · lookup · search · review-sql"]
L12 --> L34 --> DELIV
end
GT --> SKILLS --> LENS
style GT fill:#F8FAFF,stroke:#AECBFA,stroke-width:1.5px,color:#174EA6
style SKILLS fill:#F8FAFF,stroke:#AECBFA,stroke-width:1.5px,color:#174EA6
style LENS fill:#F8FAFF,stroke:#AECBFA,stroke-width:1.5px,color:#174EA6
classDef data fill:#E8F0FE,stroke:#4285F4,stroke-width:2px,color:#174EA6
classDef config fill:#FEF7E0,stroke:#F9AB00,stroke-width:2px,color:#7A5A00
classDef route fill:#FCE8E6,stroke:#EA4335,stroke-width:2px,color:#A50E0E
classDef compute fill:#E6F4EA,stroke:#34A853,stroke-width:2px,color:#0D652D
class BQML,BQAI,S_ML,DELIV data
class REFS,S_OPS,L12 config
class ROUTER route
class S_AI,L34 computeThis repository pairs three tightly connected layers that stay synchronized through automated tooling (bigquery-data-science sweep):
Ground-Truth Notebooks, SQL & Reference Hubs (bigquery-ml/ & bigquery-ai/): Every model type (23), ML.* function (12 families), AI.* / Cloud AI function (31), composed workflow (32), and MLOps orchestration pipeline (8) is validated end-to-end in a live-executed Jupyter notebook (.ipynb with committed outputs and one-click Colab / Vertex AI Workbench / BigQuery Studio headers) paired with a pure SQL companion script (89 .sql files) and 13 deep domain reference guides.
4 Composable Agent Skills (skills/): An umbrella router and multi-skill composer (bigquery-data-science) routes across three specialist skills — trained models & causal inference (bigquery-ml), zero-training foundation models & vector/hybrid search (bigquery-ai), and production orchestration, model registry, hybrid serving, drift monitoring & point-in-time feature stores (bigquery-mlops) — and explicitly recommends multi-skill combinations via cross-skill-playbooks.md.
4-Lens Progressive Disclosure, CLI, Stdio MCP & Google ADK (src/bigquery_data_science/): Instead of dumping 24 MB of raw notebooks or 200+ KB monolithic reference files into an AI coding agent's context window, bigquery-data-science compiles the repository into 4 progressive lenses (SKILL.md $\to$ 20 reference guides $\to$ 139 <25 KB syntax cards $\to$ 106 clean narratives) and exposes them through a zero-dependency CLI (uvx bigquery-data-science), stdio MCP server (bigquery-data-science mcp), and Google ADK toolset (get_bigquery_tools()).
Related MCP server: Engineering Knowledge Graph MCP Server
uvx, uv, pip, pipx, npx skills, or MCP)The core bigquery-data-science package has zero third-party runtime dependencies (< 1.5 MB wheel) and installs the 4 skills (bigquery-data-science, bigquery-ml, bigquery-ai, bigquery-mlops) into .agents/skills/ and .claude/skills/:
# Option A — Zero-install ephemeral CLI via uvx (recommended for instant setup):
uvx bigquery-data-science install # installs into ./.agents/skills & ./.claude/skills
uvx bigquery-data-science install --mcp # also registers stdio MCP server in ./.mcp.json
uvx bigquery-data-science install --global # installs into ~/.agents/skills & ~/.claude/skills
uvx bigquery-data-science install --target agents # only .agents/skills (Antigravity, Cursor, Codex, Windsurf)
uvx bigquery-data-science install --target claude # only .claude/skills (Claude Code)
# Option B — Persistent CLI install via uv tool, pipx, or pip:
uv tool install bigquery-data-science && bigquery-data-science install
pipx install bigquery-data-science && bigquery-data-science install
pip install bigquery-data-science && bigquery-data-science install
# Option C — Add to an existing Python project (uv or pip):
uv add bigquery-data-science && uv run bigquery-data-science install
pip install bigquery-data-science && python3 -m bigquery_data_science.cli install
# Option D — skills.sh CLI (installs all 4 skills or an individual skill from GitHub):
npx skills add statmike/bigquery-data-science
npx skills add statmike/bigquery-data-science --skill bigquery-ml
npx skills add statmike/bigquery-data-science --skill bigquery-mlops
# Option E — Claude Code plugin marketplace & stdio MCP server:
# In Claude Code: /plugin marketplace add statmike/bigquery-data-science
# /plugin install bigquery-data-science
claude mcp add bigquery-data-science -- uvx bigquery-data-science mcp
# Or with a local pip/uv install:
claude mcp add bigquery-data-science -- python3 -m bigquery_data_science.cli mcpRun any catalog command directly with uvx bigquery-data-science ... (zero install) or bigquery-data-science ... (after uv tool install or pip install):
# 1. List bundled skills and lens counts:
uvx bigquery-data-science list
# 2. Triage any BigQuery ML, BigQuery AI, or BigQuery MLOps task (10 comparisons + 6 multi-skill playbooks):
uvx bigquery-data-science triage "forecast daily retail sales with holiday promotions"
uvx bigquery-data-science triage "schedule retraining pipeline with drift gate and Vertex AI Model Registry"
# 3. Fetch the surgical syntax card (< 25 KB) for any model or function:
uvx bigquery-data-science lookup ARIMA_PLUS
uvx bigquery-data-science lookup AI.FORECAST
uvx bigquery-data-science lookup ML.ENTITY_FEATURES_AT_TIME
# 4. Search across all 139 syntax cards, 20 reference guides, and 106 narratives:
uvx bigquery-data-science search "point-in-time feature leakage"
# 5. Statically audit a BigQuery SQL query or .sql file for prerequisites & gotchas:
uvx bigquery-data-science review-sql "SELECT AI.GENERATE('Summarize', endpoint => 'gemini-3.8-flash')"
# 6. Add --json to any catalog command (list, lookup, search, triage, review-sql) for automation:
uvx bigquery-data-science lookup AI.AGG --jsonuv or pip)Every notebook in bigquery-ml/ and bigquery-ai/ includes committed outputs for immediate reading on GitHub and one-click launch links for Colab, Colab Enterprise, Vertex AI Workbench, and BigQuery Studio.
To run notebooks locally, choose the dependency extra that matches the folders you want to execute:
Extra | Scope | With | With |
| Skills, CLI ( |
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| Core runtime for all 106 notebooks ( |
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| Adds causal inference, survival analysis, and ONNX/XGBoost import/export ( |
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| Adds GCP orchestration SDKs for |
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| Complete notebook, workflow, and pipeline suite |
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git clone https://github.com/statmike/bigquery-data-science.git
cd bigquery-data-science
# With uv (recommended — uses the locked uv.lock environment):
uv sync --extra all
uv run bigquery-data-science kernel --user
# Or with standard Python venv + pip:
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[all]"
bigquery-data-science kernel --userskills/)Skill | Canonical Role | What It Covers | Entry Point |
Umbrella Router & Multi-Skill Composer ( | Routes across | ||
Trained Models, Preprocessing & Pipelines ( | 23 | ||
Foundation Models, Embeddings, Search & Document AI ( | 31 SQL functions across | ||
Production MLOps, Registry, Serving, Monitoring & Feature Store ( | Operationalizes both |
Lens | Skill Path | Typical Size | When the Agent Loads It |
Lens 1 — Router & Guardrails |
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| Always first. Task routing tree, critical SQL generation rules, cross-cutting empirical gotchas, and pre-delivery verification checklist. |
Lens 2 — Decision & Gotcha Guide |
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| When comparing options within a family (e.g., choosing among forecasting models, orchestration engines, or |
Lens 3 — Surgical Syntax Card |
| When writing SQL for a specific model or function. Sliced automatically by H2 ( | |
Lens 4 — Worked Textbook Narrative |
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| Only when building a full end-to-end pipeline (including GCP IAM, Cloud Resource Connections, reservations, and multi-step SQL / BigFrames choreography). Never loads raw |
bigquery-ml/ & bigquery-ai/)ML.* vs AI.* SQL NamespacesBigQuery exposes machine learning and generative AI across two SQL prefixes (ML.* and AI.*) that reflect when a function was introduced and whether it requires a CREATE MODEL DDL object. Full matrix: skills/bigquery-data-science/reference/namespace-matrix.md.
Pattern | SQL Prefix | Requires | Requires Cloud Resource Connection? | Representative Examples | Primary Hub |
1. Customer-Trained Models & Lifecycle |
| Yes (trained on your table) | Only for |
| |
2. Model-Free Feature Engineering, Drift & Point-in-Time Joins |
| No (operates directly on tables/queries) | No |
| |
3. Zero-Training Foundation Model Functions (Gemini, TimesFM, TabFM, Document AI, Translation) |
| No ( | For multimodal |
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4. Remote Cloud AI APIs & First-Gen Vertex Wrappers |
| Yes (thin metadata pointer to Vertex / Cloud AI API) | Yes ( |
| |
5. Production MLOps, Model Registry, Export/Import & Universal Monitoring |
| Varies ( | Only for |
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Whether you are reading the repository as a human practitioner or routing an AI coding agent, every workflow and pipeline maps to one of 7 problem-first stages (full playbook & SQL choreography: skills/bigquery-data-science/reference/cross-skill-playbooks.md):
Instead of scrolling through 106 individual rows here, jump directly into the domain hubs and category indexes below:
Domain Hub | Sub-Folder / Resource | Count | What You Will Find Inside | Direct Entry Points |
BigQuery ML ( | Start Here & Reference Index |
| Interactive tour of all BigQuery ML capabilities, environment setup, and the complete |
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| Every |
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| End-to-end applied ML workflows: customer LTV, churn & survival analysis, demand forecasting at scale, fraud detection, propensity scoring, price elasticity, A/B uplift & causal inference, recommendation systems, anomaly detection, and model monitoring. |
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| Production MLOps orchestration across 8 GCP & SQL engines: |
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BigQuery AI ( | Start Here & Reference Index |
| Interactive tour of BigQuery AI functions, synthetic multimodal/PDF dataset generator ( |
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| General-purpose generation ( |
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| Composed multi-function pipelines: semantic search, RAG, hybrid catalog search (BM25 + vector + RRF + |
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Agent Skills ( | Umbrella Router + 3 Specialist Skills |
| Multi-skill task routing ( |
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Contributions are welcome — see CONTRIBUTING.md, CODE_OF_CONDUCT.md, and SECURITY.md.
GitHub Actions CI & Release Workflows: .github/workflows/ci.yml runs ruff, mypy, pytest, bigquery-data-science sweep, sync_headers.py --check, and ./scripts/check_install.sh across Python 3.11, 3.12, and 3.13 on every push and pull request. .github/workflows/release.yml publishes release candidates (v*rc*) to TestPyPI and production tags (v*) to PyPI via OIDC Trusted Publishing.
Local Pre-Commit Hooks (.pre-commit-config.yaml): Install with uv run pre-commit install to run ruff, mypy, bigquery-data-science sweep, and sync_headers.py --check automatically before each commit.
Run the complete local verification and cold-install gate before submitting a pull request:
uv run ruff check src tests && \
uv run mypy src && \
uv run pytest -q && \
uv run bigquery-data-science sweep && \
python3 architectures/headers/sync_headers.py --check && \
./scripts/check_install.shbigquery-data-science adheres to Semantic Versioning (Major.Minor.Patch):
Major (X.0.0) — Skills architecture, names & structural contract shifts: Changes in skills hierarchy/naming (skills/), packaging overhauls, or breaking changes to CLI subcommands, MCP tool schemas, or library exports.
Minor (X.Y.0) — Multi-area monthly sweeps & capability rollouts: Scheduled monthly sweeps across BigQuery release notes — introducing new foundation models, options, notebooks, SQL workflows, and synchronized 4-lens skill updates.
Patch (X.Y.Z) — Targeted single-area updates & fixes: Isolated bug fixes, typo corrections, single-notebook adjustments, or targeted hotfixes.
See CONTRIBUTING.md for the full release process and TestPyPI/PyPI tag flow.
Apache-2.0 — see LICENSE.
This is not an officially supported Google product. It is built on Google products and is neither endorsed nor supported by Google.
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Hosted BigQuery MCP server for Claude, ChatGPT and Cursor, with each action switched per account.
The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.
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Knowledge base MCP for AI agents on iknow.dev. Search, read, and maintain via OAuth.
