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.
What this skill does
# Mozilla BigQuery Query Writing For table selection and aggregation hierarchy, see [data-catalog.md](../../knowledge/data-catalog.md). For query templates and best practices, see [query-writing.md](../../knowledge/query-writing.md). For data platform architecture, see [architecture.md](../../knowledge/architecture.md). For external sources (Metric Hub, Confluence, UDF discovery), see [external-sources.md](../../knowledge/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 1. Identify query type (user counts, specific metric, events, search) 2. 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. 3. Select optimal table using the aggregation hierarchy in knowledge/data-catalog.md 4. Add required filters per knowledge/query-writing.md 5. Write the query following templates in knowledge/query-writing.md 6. 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
Related in Writing & Docs
jax-development
IncludedUse this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
nature-article-writer
IncludedDrafts, rewrites, diagnostically critiques, and style-calibrates primary research manuscripts for Nature and Nature Portfolio journals. Use when the user wants a Nature-style title, summary paragraph or abstract, introduction, results, discussion, methods, figure legends, presubmission enquiry, cover letter, reviewer response, or when a scientific draft sounds generic, jargon-heavy, structurally weak, or AI-ish and needs precise, broad-reader-friendly prose without inventing data, analyses, or references. Best for primary research articles and letters rather than reviews or press releases unless explicitly adapting one.
deckrd
IncludedDocument-driven framework that derives requirements, specifications, implementation plans, and executable tasks from goals through structured AI dialogue. Use when user says "write requirements", "create spec", "plan implementation", "derive tasks", "structure this feature", "break down into tasks", or "document this module". Also use for reverse engineering existing code into docs (/deckrd rev). Do NOT use for direct code writing — use /deckrd-coder after tasks are generated. Do NOT use when the user only wants to run or fix existing code without planning.
clinical-decision-support
IncludedGenerate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
handling-sf-data
IncludedSalesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).
accelint-ac-to-playwright
IncludedConvert and validate acceptance criteria for Playwright test automation. Use when user asks to (1) review/evaluate/check if AC are ready for automation, (2) assess if AC can be converted as-is, (3) validate AC quality for Playwright, (4) turn AC into tests, (5) generate tests from acceptance criteria, (6) convert .md bullets or .feature Gherkin files to Playwright specs, (7) create test automation from requirements. Handles both bullet-style markdown and Gherkin syntax with JSON test plan generation and validation.