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-analyticsnpx skills add gemini-cli-extensions/bigquery-data-analytics --skill bigquery-analyticsgit clone --depth 1 https://github.com/gemini-cli-extensions/bigquery-data-analyticsWhat 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 | $0.00044 | $0.01055 |
| Opus 5 | $0.00022 | $0.00528 |
| Sonnet 5 | $0.00009 | $0.00211 |
| Haiku 4.5 | $0.00004 | $0.00105 |
Grade A, and why
bigquery-analytics 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Usage
All scripts can be executed using Node.js. Replace <param_name> and <param_value> with actual values.
Bash:
node <skill_dir>/scripts/<script_name>.js '{"<param_name>": "<param_value>"}'
PowerShell:
node <skill_dir>/scripts/<script_name>.js '{\"<param_name>\": \"<param_value>\"}'
Note: The scripts automatically load the environment variables from various .env files. Do not ask the user to set vars unless skill executions fails due to env var absence.
Scripts
analyze_contribution
Use this skill to analyze the contribution about changes to key metrics in multi-dimensional data.
Parameters
| Name | Type | Description | Required | Default |
|---|---|---|---|---|
| input_data | string | The data that contain the test and control data to analyze. Can be a fully qualified BigQuery table ID or a SQL query. | Yes | |
| contribution_metric | string | The name of the column that contains the metric to analyze. |
Provides the expression to use to calculate the metric you are analyzing.
To calculate a summable metric, the expression must be in the form SUM(metric_column_name),
where metric_column_name is a numeric data type.
To calculate a summable ratio metric, the expression must be in the form
SUM(numerator_metric_column_name)/SUM(denominator_metric_column_name),
where numerator_metric_column_name and denominator_metric_column_name are numeric data types.
To calculate a summable by category metric, the expression must be in the form
SUM(metric_sum_column_name)/COUNT(DISTINCT categorical_column_name). The summed column must be a numeric data type.
The categorical column must have type BOOL, DATE, DATETIME, TIME, TIMESTAMP, STRING, or INT64. | Yes | |
| is_test_col | string | The name of the column that identifies whether a row is in the test or control group. | Yes | |
| dimension_id_cols | array | An array of column names that uniquely identify each dimension. | No | |
| top_k_insights_by_apriori_support | integer | The number of top insights to return, ranked by apriori support. | No | 30 |
| pruning_method | string | The method to use for pruning redundant insights. Can be 'NO_PRUNING' or 'PRUNE_REDUNDANT_INSIGHTS'. | No | PRUNE_REDUNDANT_INSIGHTS |
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.
- 2d ago First seen · 102 lines · 44 tokens per session scan A 06115b8bb22e
bigquery-analytics is a skill published in the GitHub repository gemini-cli-extensions/bigquery-data-analytics (49 stars, last pushed 4d ago), licensed Apache-2.0. It adds 44 tokens to every session and 1,055 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
conductor-setup
Scaffolds the project and sets up the Conductor environment. Use this whenever a project needs to be initialized or if the Conductor configuration is missing.
conductor-new-track
Plans a new track (feature or bug fix), generates spec/plan documents, and updates the registry.
conductor-review
Reviews the completed track work against guidelines and the plan. Acts as a Principal Software Engineer to ensure quality and compliance.
google-antigravity-sdk
Design, implement, and debug autonomous AI agents and multi-agent systems using the Google Antigravity (AGY) SDK. ACTIVATE this skill when the user wants to create, configure, or orchestrate Google Antigravity agents.
conductor-implement
Executes the tasks defined in the specified track's plan. Use this to start or continue working on a feature, bug fix, or chore.
conductor-revert
Reverts previous work (tracks, phases, or tasks) by identifying associated commits and performing Git reverts.