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Execute qualquer Skill no Manus com um clique

data-lifecycle

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Atualizado5 de julho de 2026 às 11:17

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.

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Methasit-Pun

Methasit-Pun/data_engineer_claude_skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

SKILL.md

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name	data-lifecycle
description	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.
origin	grouped

Data Project Lifecycle (Router)

This is a router skill covering a data project from "we have an objective" to "the code is clean." Figure out which stage the user is in, then invoke the matching stage skill with the Skill tool. A greenfield project runs the stages in order; a mid-flight project jumps to its stage.

The lifecycle

Stage	Question it answers	Invoke sub-skill
1. Discover	What data do I need and where do I get it?	`data-sourcing`
2. Profile	What does the data actually look like? (checks + ER)	`data-profiling`
3. Architect	How should the whole platform be structured, on what infra, at what cost?	`data-architecture`
4. Build	Design the bronze/silver/gold pipeline, gold-first	`medallion-design`
5. Refactor	Make the resulting notebook/code reviewable	`notebook-refactor`

Routing rules

"New project / where do I start" → begin at stage 1 (data-sourcing) and proceed in order, confirming each stage's output before advancing.
User already has data in hand → start at stage 2 (data-profiling).
Sources + shape known, needs a plan → stage 3 (data-architecture).
Architecture decided, ready to build layers → stage 4 (medallion-design).
Working code that's messy → stage 5 (notebook-refactor) — can run anytime, independent of the others.
Invoke by name, e.g. Skill(skill="data-architecture"). For a task spanning stages, invoke each in sequence and carry the output forward.

How this router relates to the others

The lifecycle produces work that the topic routers deepen:

Stage 3 data-architecture delegates cloud + cost to → [[cloud-data-infra]]
Stage 4 medallion-design defers pipeline mechanics to → [[data-pipelines]] and the model to → [[data-modeling]]
Profiling & building both hand validation/governance to → [[data-reliability]]
ML objectives branch to → [[ml-feature-engineering]]

Use this router for "what stage am I in"; use the topic routers for "go deep on pipelines/modeling/reliability/cloud."

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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.

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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.

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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.

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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.

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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.

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name	data-lifecycle
description	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.
origin	grouped

Data Project Lifecycle (Router)

The lifecycle

Stage	Question it answers	Invoke sub-skill
1. Discover	What data do I need and where do I get it?	`data-sourcing`
2. Profile	What does the data actually look like? (checks + ER)	`data-profiling`
3. Architect	How should the whole platform be structured, on what infra, at what cost?	`data-architecture`
4. Build	Design the bronze/silver/gold pipeline, gold-first	`medallion-design`
5. Refactor	Make the resulting notebook/code reviewable	`notebook-refactor`

Routing rules

"New project / where do I start" → begin at stage 1 (data-sourcing) and proceed in order, confirming each stage's output before advancing.
User already has data in hand → start at stage 2 (data-profiling).
Sources + shape known, needs a plan → stage 3 (data-architecture).
Architecture decided, ready to build layers → stage 4 (medallion-design).
Working code that's messy → stage 5 (notebook-refactor) — can run anytime, independent of the others.
Invoke by name, e.g. Skill(skill="data-architecture"). For a task spanning stages, invoke each in sequence and carry the output forward.

How this router relates to the others

The lifecycle produces work that the topic routers deepen:

Stage 3 data-architecture delegates cloud + cost to → [[cloud-data-infra]]
Stage 4 medallion-design defers pipeline mechanics to → [[data-pipelines]] and the model to → [[data-modeling]]
Profiling & building both hand validation/governance to → [[data-reliability]]
ML objectives branch to → [[ml-feature-engineering]]

Use this router for "what stage am I in"; use the topic routers for "go deep on pipelines/modeling/reliability/cloud."