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 agentmods add skills/gemini-cli-extensions/data-agent-kit-starter-pack/bigquery-ai-mlnpx skills add gemini-cli-extensions/data-agent-kit-starter-pack --skill bigquery-ai-mlgit clone --depth 1 https://github.com/gemini-cli-extensions/data-agent-kit-starter-packWrote 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/gemini-cli-extensions/data-agent-kit-starter-pack/bigquery-ai-ml)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/bigquery-ai-ml"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/bigquery-ai-ml.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 | $0.00056 | $0.00733 |
| Opus 5 | $0.00028 | $0.00367 |
| Sonnet 5 | $0.00011 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00073 |
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 6d 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.
This is a copy
88% identical to bigquery-ai-ml — 60 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 68 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.
[!IMPORTANT] You MUST read and follow the global constraints and mandatory function routing rules in ai_function_best_practices.md before writing any BQML AI/ML SQL query.
Reference Directory
-
Best Practices: ai_function_best_practices.md
-
Functions Reference:
- AI.AGG: ai_agg.md - Multi-row semantic aggregation and summarization.
- 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.
- VECTOR_SEARCH: vector_search.md - Vector search best practices.
What ships with it
17 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_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_function_best_practices.md 2.8 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 4.0 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/remote_models.md 871 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.
- 6d ago First seen · 68 lines · 56 tokens per session scan A d8d93967ba92
bigquery-ai-ml is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (179 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 733 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to bigquery-ai-ml, differing in 60 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…