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 hamzabellouch/agent-skills --skill bigquery-ai-mlgit clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote 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/hamzabellouch/agent-skills/bigquery-ai-ml)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/bigquery-ai-ml"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/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/hamzabellouch/agent-skills/bigquery-ai-ml"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/bigquery-ai-ml.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00299 |
| Opus 5 | $0.00028 | $0.00150 |
| Sonnet 5 | $0.00011 | $0.00060 |
| Haiku 4.5 | $0.00006 | $0.00030 |
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 9d 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.
What it actually says
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
-
AI Forecast: Leveraging pre-trained TimesFM model for forecasting without custom training.
-
AI Detect Anomalies: Identify deviations in time series data using pre-trained TimesFM model.
-
AI Generate: General-purpose text and content generation using Gemini models.
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AI Key Drivers: Automatically identify dimensional segments most responsible for driving changes in a metric.
Related Skills
- BigQuery Basics Skill: SKILL.md file for core BigQuery concepts, resource management, CLI, and client libraries.
What ships with it
4 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.
- 9d ago First seen · 36 lines · 56 tokens per session scan A 1231ec826c3b
bigquery-ai-ml is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 299 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
Other skills, from other repositories
prompt-engineering
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic…
fixing-prompt
Prompt: Prompt Refinement and Optimization.
feature-engineering
When building training datasets, designing feature pipelines, or debugging training-serving skew.
streaming-patterns
When designing Kafka consumers/producers or implementing real-time pipelines.
inference-serving
When deploying a model to an API endpoint or optimizing inference latency.
model-evaluation
When evaluating a trained model, comparing versions, or performing fairness analysis.