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data-modeling

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업데이트2026년 7월 5일 11:17

Umbrella skill for shaping data once it has landed — warehouse schema design (star/snowflake/OBT/SCD/grain), SQL for analytics (window functions, CTEs, optimization), dbt model layers/tests/macros/incrementals, and Python transforms (pandas/Polars/PySpark performance). Use this whenever the user is designing tables, writing or reviewing analytical SQL, building dbt models, or writing Python data transformations and it isn't yet clear which tool dominates. This skill ROUTES to the focused sub-skills (schema-design, sql-patterns, dbt-patterns, python-data-patterns) and pulls in more than one when a task spans them. Trigger on: fact/dimension design, table grain, SCD, window functions, deduplication, dbt refs/materializations, or slow/memory-heavy DataFrame code.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

Methasit-Pun

Methasit-Pun/data_engineer_claude_skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Data Modeling & Transformation (Router)

This is a router skill. It groups the four skills that deal with structuring and transforming data after it lands in the warehouse or a processing job. Diagnose which sub-area(s) the task touches, then invoke the matching sub-skill(s) with the Skill tool.

How to route

If the task is about…	Invoke sub-skill
Modeling business entities: star/snowflake schema, one-big-table, slowly changing dimensions, grain definition, surrogate keys, normalization tradeoffs	`schema-design`
Writing/reviewing analytical SQL: window functions, CTEs, ranking, running totals, dedup, query optimization, anti-patterns (BigQuery/Snowflake/Redshift/DuckDB)	`sql-patterns`
dbt work: model layers (staging/intermediate/marts), refs, sources, tests, macros, materializations, incremental strategies	`dbt-patterns`
Python transforms: pandas/Polars/PySpark idioms, chunked reads, memory-safe transforms, vectorization, performance	`python-data-patterns`

Routing rules

Start with schema-design when the table doesn't exist yet — decide grain and layout before writing SQL against it. A bad schema is expensive to fix later.
SQL running inside dbt pulls in both sql-patterns (the query itself) and dbt-patterns (materialization, refs, tests).
Transformation in Python vs. SQL undecided? Warehouse-native transforms → sql-patterns/dbt-patterns; external/large-file or ML-adjacent processing → python-data-patterns.
Row-by-row DataFrame iteration or memory errors → python-data-patterns immediately.
Invoke via the Skill tool by name, e.g. Skill(skill="dbt-patterns"). Combine outputs; don't paraphrase from memory.

Related groups

Getting data into the warehouse first → [[data-pipelines]]
Testing/validating the models you build → [[data-reliability]]
Warehouse cost of these queries → [[cloud-data-infra]]

이 저장소의 다른 Skills

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data-lifecycle

Methasit-Pun/data_engineer_claude_skills

Umbrella skill for running a data project end-to-end through its lifecycle stages — discover sources → profile the data → architect the platform → build the medallion pipeline → refactor the code. Use this whenever the user is kicking off a new data project, asks "where do I start" or "what are the steps", or is somewhere mid-lifecycle and unsure which stage skill applies. This skill ROUTES to the stage sub-skills (data-sourcing, data-profiling, data-architecture, medallion-design, notebook-refactor) and sequences them, pulling in the right one for the user's current stage.

2026-07-050

data-sourcing

Methasit-Pun/data_engineer_claude_skills

Data collection and readiness — the step BEFORE a pipeline exists. Catalog candidate data sources for an objective, rank each by importance/impact, and record where to get it (internal system vs. open/public dataset), how to access it, its refresh cadence, licensing, and readiness blockers. Use this skill whenever a project is starting and the sources aren't decided yet, when the user asks "what data do I need for X and where do I get it", when weighing internal vs. open-source data, or when assessing whether a source is accessible/usable before committing to a build. This sits upstream of pipeline-design — it decides WHAT to ingest before deciding HOW.

2026-07-050

data-profiling

Methasit-Pun/data_engineer_claude_skills

Profile and map raw data BEFORE designing a schema. One-time exploratory analysis to learn the true shape of a dataset — row/column counts, null rates, cardinality, value distributions, ranges, data types, candidate keys, duplicates, referential relationships — then a source-to-target field mapping and an ER diagram. Use this skill whenever the user has data in hand and needs to understand it before modeling, is about to design a schema, asks "what does this data actually look like", needs to find the primary/composite key of an unfamiliar table, or must map source fields to a target model. This is exploratory and one-time; ongoing production validation is data-quality's job, and modeling patterns are schema-design's job.

2026-07-050

dbt-patterns

Methasit-Pun/data_engineer_claude_skills

dbt model design, ref chains, sources, tests, macros, incremental strategies, materializations, and documentation best practices. Use this skill whenever the user is writing or reviewing dbt models, configuring dbt tests, designing model layers (staging/intermediate/marts), asking about incremental models, choosing materializations, writing macros, setting up sources.yml, or troubleshooting dbt run/test failures. Also trigger when the user mentions dbt refs, lineage graphs, model dependencies, dbt Cloud, the dbt CLI, or when they want to transform data already in the warehouse using SQL. If the project uses dbt at all, this skill should be active for any transformation questions.

2026-07-050

python-data-patterns

Methasit-Pun/data_engineer_claude_skills

Pandas, Polars, and PySpark idioms for production data engineering — chunked reads, memory-safe transforms, vectorized operations, type optimization, and performance patterns. Use this skill whenever the user is writing a Python data transformation script and running into memory issues, slow performance, or correctness bugs with large datasets. Also trigger when the user asks how to handle large CSV/Parquet files, process data in batches, use Polars instead of Pandas, optimize a PySpark job, or reduce DataFrame memory usage. If you see someone iterating row-by-row over a DataFrame, this skill should trigger immediately.

2026-07-050

schema-design

Methasit-Pun/data_engineer_claude_skills

Data modeling for analytical workloads — star schema, snowflake schema, one big table (OBT), slowly changing dimensions (SCD), normalization tradeoffs, grain definition, and surrogate key strategies. Use this skill whenever the user is designing or reviewing a data warehouse schema, planning a fact/dimension table layout, deciding how to model a business entity (customer, order, event, product), or asking how to handle historical changes to dimension attributes. Also trigger when the user asks about dbt model design, table granularity, or how to structure data for BI tools like Looker, Tableau, or Power BI. Get this right before building the pipeline — a bad schema is expensive to fix later.

2026-07-050

name

data-modeling

description

Data Modeling & Transformation (Router)

How to route

If the task is about…	Invoke sub-skill
Modeling business entities: star/snowflake schema, one-big-table, slowly changing dimensions, grain definition, surrogate keys, normalization tradeoffs	`schema-design`
Writing/reviewing analytical SQL: window functions, CTEs, ranking, running totals, dedup, query optimization, anti-patterns (BigQuery/Snowflake/Redshift/DuckDB)	`sql-patterns`
dbt work: model layers (staging/intermediate/marts), refs, sources, tests, macros, materializations, incremental strategies	`dbt-patterns`
Python transforms: pandas/Polars/PySpark idioms, chunked reads, memory-safe transforms, vectorization, performance	`python-data-patterns`

Routing rules

Start with schema-design when the table doesn't exist yet — decide grain and layout before writing SQL against it. A bad schema is expensive to fix later.
SQL running inside dbt pulls in both sql-patterns (the query itself) and dbt-patterns (materialization, refs, tests).
Transformation in Python vs. SQL undecided? Warehouse-native transforms → sql-patterns/dbt-patterns; external/large-file or ML-adjacent processing → python-data-patterns.
Row-by-row DataFrame iteration or memory errors → python-data-patterns immediately.
Invoke via the Skill tool by name, e.g. Skill(skill="dbt-patterns"). Combine outputs; don't paraphrase from memory.

Related groups

Getting data into the warehouse first → [[data-pipelines]]
Testing/validating the models you build → [[data-reliability]]
Warehouse cost of these queries → [[cloud-data-infra]]