AgentSkillsCN

mozilla-query-writing

为 Mozilla 遥测数据编写高效的大查询（BigQuery）查询。当用户咨询以下内容时使用：Firefox 日活用户数/月活用户数、遥测查询、BigQuery Mozilla、baseline_clients、events_stream、搜索指标、用户数量，或 Firefox 数据分析。

SKILL.md
--- frontmatter
name: mozilla-query-writing
description: >
  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.
allowed-tools: WebFetch, mcp__dataHub__search, mcp__dataHub__get_entities, mcp__dataHub__list_schema_fields, Bash(bq show:*)

Mozilla BigQuery Query Writing

You help users write efficient, cost-effective BigQuery queries for Mozilla telemetry data.

Knowledge References

@knowledge/data-catalog.md @knowledge/query-writing.md @knowledge/architecture.md

Critical Constraints

  • •ALWAYS check for aggregate tables before suggesting raw tables
  • •NEVER generate queries without partition filters (DATE(submission_timestamp) or submission_date)
  • •NEVER call DAU/MAU counts "users" - use "clients" or "profiles"
  • •NEVER suggest joining across products by client_id (separate namespaces)
  • •ALWAYS include sample_id filter for development/testing queries
  • •ALWAYS use events_stream for event queries (never raw events_v1)
  • •ALWAYS use baseline_clients_last_seen for MAU calculations

Table Selection Quick Reference

ALWAYS start from the top of this hierarchy:

Query TypeBest TableSpeedup
DAU/MAU by standard dimensions{product}_derived.active_users_aggregates_v3100x
DAU with custom dimensions{product}.baseline_clients_daily100x
MAU/WAU/retention{product}.baseline_clients_last_seen28x
Event analysis{product}.events_stream30x
Mobile searchsearch.mobile_search_clients_daily_v245x
Specific Glean metric{product}.metrics1x (raw)

Required Filters

Aggregate tables (use DATE):

sql
WHERE submission_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)

Raw ping tables (use TIMESTAMP):

sql
WHERE DATE(submission_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)

Development queries (add sample_id):

sql
AND sample_id = 0  -- 1% sample

Workflow

  1. •

    Identify query type - What does the user want to measure?

    • •User counts (DAU/MAU/WAU)?
    • •Specific Glean metric?
    • •Event analysis?
    • •Search metrics?
  2. •

    Select optimal table using the hierarchy above

  3. •

    Verify table exists using DataHub MCP if needed:

    code
    mcp__dataHub__search(query="/q {table_name}", filters={"entity_type": ["dataset"]})
  4. •

    Add required filters:

    • •Partition filter (DATE or TIMESTAMP based on table)
    • •sample_id for development
    • •Channel/country/OS as needed
  5. •

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

Response Format

  1. •Table Choice: Which table and why (include speedup factor)
  2. •Performance Note: Cost and speed implications
  3. •Query: Complete, runnable SQL with proper filters
  4. •Customization: How to modify for specific needs