Skill

query-writing

Write efficient BigQuery queries for Mozilla telemetry. Use when user asks about: Firefox DAU/MAU, telemetry queries, BigQuery Mozilla, baseline_clients, events_stream, search metrics, user counts, or Firefox data analysis.

Install

npx claudepluginhub akkomar/mozdata-claude-plugin --plugin mozdata

Tool Access

This skill is limited to using the following tools:

mcp__dataHub__searchmcp__dataHub__get_entitiesmcp__dataHub__list_schema_fieldsmcp__bigquery__execute_sqlmcp__bigquery__list_dataset_idsmcp__bigquery__list_table_idsmcp__bigquery__get_table_infomcp__bigquery__get_dataset_infomcp__atlassian__searchConfluenceUsingCqlmcp__atlassian__getConfluencePagemcp__plugin_mozdata_metric-hub__search_metricsmcp__plugin_mozdata_metric-hub__get_metricmcp__plugin_mozdata_metric-hub__get_metric_sqlmcp__plugin_mozdata_metric-hub__list_data_sourcesmcp__plugin_mozdata_metric-hub__get_data_sourcemcp__plugin_mozdata_metric-hub__search_glean_eventsmcp__plugin_mozdata_metric-hub__get_glean_eventmcp__plugin_mozdata_metric-hub__build_funnel_urlBash(bq show:*)

Preview

For table selection and aggregation hierarchy, see [data-catalog.md](../../knowledge/data-catalog.md).

SKILL.md

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Last CommitApr 16, 2026

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View Source View Plugin View on GitHub View README

Mozilla BigQuery Query Writing

For table selection and aggregation hierarchy, see data-catalog.md. For query templates and best practices, see query-writing.md. For data platform architecture, see architecture.md. For external sources (Metric Hub, Confluence, UDF discovery), see external-sources.md.

Guardrails

Use "clients" or "profiles" not "users" — BigQuery tracks client_id, not actual users

Do not suggest joining across products by client_id — each product has its own namespace

Always check for aggregate tables before suggesting raw tables

Workflow

Identify query type (user counts, specific metric, events, search)

For standard metrics (DAU, MAU, retention, etc.), look up the authoritative definition and SQL via Metric Hub MCP (get_metric_sql) if available. For broader context on metric calculation logic, check Confluence via Atlassian MCP. If neither is available, use the templates in this plugin's knowledge files.

Select optimal table using the aggregation hierarchy in knowledge/data-catalog.md

Add required filters per knowledge/query-writing.md

Write the query following templates in knowledge/query-writing.md

If BigQuery MCP tools are available (mcp__bigquery__*), offer to execute the query directly:

mcp__bigquery__execute_sql to run queries
mcp__bigquery__get_table_info to inspect schemas
mcp__bigquery__list_dataset_ids / mcp__bigquery__list_table_ids to explore data
Always include partition filters and sample_id in executed queries

Mozilla BigQuery Query Writing

Guardrails

Use "clients" or "profiles" not "users" — BigQuery tracks client_id, not actual users
Do not suggest joining across products by client_id — each product has its own namespace
Always check for aggregate tables before suggesting raw tables

Workflow

Identify query type (user counts, specific metric, events, search)
For standard metrics (DAU, MAU, retention, etc.), look up the authoritative definition and SQL via Metric Hub MCP (get_metric_sql) if available. For broader context on metric calculation logic, check Confluence via Atlassian MCP. If neither is available, use the templates in this plugin's knowledge files.
Select optimal table using the aggregation hierarchy in knowledge/data-catalog.md
Add required filters per knowledge/query-writing.md
Write the query following templates in knowledge/query-writing.md
If BigQuery MCP tools are available (mcp__bigquery__*), offer to execute the query directly:
- mcp__bigquery__execute_sql to run queries
- mcp__bigquery__get_table_info to inspect schemas
- mcp__bigquery__list_dataset_ids / mcp__bigquery__list_table_ids to explore data
- Always include partition filters and sample_id in executed queries