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/bigquery-data-analytics/bigquery-ai-mlnpx skills add gemini-cli-extensions/bigquery-data-analytics --skill bigquery-ai-mlgit clone --depth 1 https://github.com/gemini-cli-extensions/bigquery-data-analyticsWrote 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/bigquery-data-analytics/bigquery-ai-ml)<a href="https://agentmods.dev/skills/gemini-cli-extensions/bigquery-data-analytics/bigquery-ai-ml"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/bigquery-data-analytics/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 | $0.00031 | $0.00572 |
| Opus 5 | $0.00015 | $0.00286 |
| Sonnet 5 | $0.00006 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
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 3d 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
100% identical to bigquery-ai-ml — 0 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: bigquery-ai-ml
This skill defines the usage and rules for BigQuery AI/ML functions, preferring SQL-based Skills over dedicated BigQuery tools.
1. Skill vs Tool Preference (BigQuery AI/ML)
Agents should prefer using the Skill (SQL via execute_sql()) over
dedicated BigQuery tools for functionalities like Forecasting and Anomaly
Detection.
Use execute_sql() with the standard BigQuery AI.* functions for these tasks
instead of the corresponding high-level tools.
2. Mandatory Reference Routing
This skill file does not contain the syntax for these functions. You MUST read the associated reference file before generating SQL.
CRITICAL: DO NOT GUESS filenames. You MUST only use the exact paths provided below.
| Function | Description | Required Reference File to Retrieve |
|---|---|---|
| AI.FORECAST | Time-series forecasting via the pre-trained TimesFM model | references/bigquery_ai_forecast.md |
| AI.CLASSIFY | Categorize unstructured data into predefined labels | references/bigquery_ai_classify.md |
| AI.DETECT_ANOMALIES | Identify deviations in time-series data via the pre-trained TimesFM model | references/bigquery_ai_detect_anomalies.md |
| AI.GENERATE | General-purpose text and content generation | references/bigquery_ai_generate.md |
| AI.GENERATE_BOOL | Generate a boolean value (TRUE/FALSE) based on a prompt | references/bigquery_ai_generate_bool.md |
| AI.GENERATE_DOUBLE | Generate a floating-point number based on a prompt | references/bigquery_ai_generate_double.md |
| AI.GENERATE_INT | Generate an integer value based on a prompt | references/bigquery_ai_generate_int.md |
| AI.IF | Evaluate a natural-language boolean condition | references/bigquery_ai_if.md |
| AI.SCORE | Rank items by semantic relevance (use with ORDER BY) | references/bigquery_ai_score.md |
| AI.SIMILARITY | Compute cosine similarity between two inputs | references/bigquery_ai_similarity.md |
| AI.SEARCH | Semantic search on tables with autonomous embedding generation | references/bigquery_ai_search.md |
What ships with it
11 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/bigquery_ai_classify.md 3.7 KB
- references/bigquery_ai_detect_anomalies.md 4.5 KB
- references/bigquery_ai_forecast.md 6.5 KB
- references/bigquery_ai_generate_bool.md 1.9 KB
- references/bigquery_ai_generate_double.md 1.9 KB
- references/bigquery_ai_generate_int.md 1.9 KB
- references/bigquery_ai_generate.md 4.2 KB
- references/bigquery_ai_if.md 2.0 KB
- references/bigquery_ai_score.md 1.9 KB
- references/bigquery_ai_search.md 3.3 KB
- references/bigquery_ai_similarity.md 1.8 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.
- 3d ago First seen · 48 lines · 31 tokens per session scan A bf873812bc11
bigquery-ai-ml is a skill published in the GitHub repository gemini-cli-extensions/bigquery-data-analytics (49 stars, last pushed 5d ago), licensed Apache-2.0. It adds 31 tokens to every session and 572 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bigquery-ai-ml, differing in 0 lines, and is treated as a copy.
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