ss-data-skills

Use when writing a concise daily report, daily worklog, standup update, shift handoff, or end-of-day summary for data analysis, data development, BI, governance, or operations work.

Use when writing or improving data documentation for warehouse tables, metrics, dashboards, SQL jobs, data products, or handover materials so other people and agents can understand data purpose, grain, fields, logic, ownership, and usage constraints.

Use when analyzing upstream and downstream impact for table changes, metric changes, field deprecation, pipeline migration, dashboard breakage, data governance review, or data asset dependency communication.

Use when planning integration with databases, BI tools, data platforms, APIs, workflow systems, storage services, or third-party tools, including connection requirements, API availability, auth strategy, configuration questions, and validation steps.

Use when planning or writing market research for a specific industry, product category, competitor set, business opportunity, user segment, or go-to-market question with structured evidence and clear assumptions.

Use when diagnosing business metric changes or anomalies such as GMV decline, conversion drop, retention loss, churn increase, revenue change, lead decline, or active user fluctuation with a structured root cause analysis plan.

Use when reviewing whether a BI dashboard, management dashboard, metric board, or data product page can support business decisions with clear metrics, trustworthy definitions, and actionable drill-down paths.

Use when writing a data incident postmortem for failed partitions, wrong dashboard numbers, metric definition changes, SQL logic bugs, backfill mistakes, SLA misses, or data pipeline incidents.

Use when designing, checking, or interpreting A/B tests and experiments. Reviews hypothesis, variants, metrics, sample ratio, significance, guardrail metrics, segmentation, validity risks, and business recommendations.

Use when turning analysis results, metrics, SQL outputs, dashboards, or investigation notes into a structured data analysis report with executive summary, evidence, insight, recommendations, caveats, and next steps.

Use when turning data analysis, reports, project updates, or technical material into a slide deck outline or PPT narrative. Builds audience-specific storyline, slide-by-slide structure, chart recommendations, speaker notes, and appendix plan.

Use when generating data quality rules from a table schema, SQL, metric definition, or data pipeline description. Produces validation rules for freshness, row count, uniqueness, nulls, enums, ranges, referential integrity, metric fluctuation, reconciliation, and SQL checks.

Use when a vague data analysis or data development request needs to be turned into a clear, buildable task. Clarifies business goal, metrics, dimensions, time range, data sources, output format, acceptance criteria, risks, and open questions.

Use when exploring a dataset before formal analysis, modeling, reporting, or dashboarding. Profiles fields, missing values, distributions, outliers, relationships, data quality risks, and next analysis directions.

Use when analyzing conversion funnels across ordered business steps, such as registration, activation, payment, signup, retention, onboarding, or checkout. Identifies drop-off points, segment differences, tracking risks, and action recommendations.

Use when reviewing or defining a business metric, KPI, or analytics indicator. Checks business intent, population, event definition, time window, grain, filters, edge cases, status lifecycle, consistency, and questions that must be confirmed.

Use when analyzing retention, cohort behavior, repeat purchase, active user return, lifecycle changes, or user stickiness over time. Builds cohort definitions, retention matrices, segment comparisons, and business interpretations.

Use when reviewing SQL for data development. Checks logic correctness, metric consistency, joins, aggregations, deduplication, null handling, date boundaries, partition filters, performance risks, engine-specific issues, and safer rewrite suggestions.

Use when designing or reviewing a data warehouse table schema. Advises on warehouse layer, table grain, fact or dimension modeling, fields, primary key, partitioning, update strategy, lifecycle, naming, downstream usage, and data quality checks.

Use when writing or polishing weekly reports, monthly reports, project updates, work summaries, performance reviews, or upward status reports for data, analytics, engineering, or operations work.