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Agent skill

Architecting Data

by ancoleman in ancoleman/ai-design-components

Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data…

MITAuto-check passedDatabases

Install Architecting Data

skills CLI
$ npx skills add ancoleman/ai-design-components --skill architecting-data -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ancoleman/ai-design-components architecting-data --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/architecting-data .claude/skills/architecting-data && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
architecting-data
GitHub stars
526
Token cost
~3.6k tokens
SKILL.md length
1,276 words
Files
14 (incl. references)
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data…

  • Works in 7 steps: Storage Paradigms → Data Modeling Approaches → Data Mesh Principles → …
  • Architecting data platforms
  • SKILL.md covers Purpose, When to Use This Skill, Core Concepts and Decision Frameworks, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Architecting Data is an agent skill from ancoleman/ai-design-components. Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including reference files (for example `examples/dbt-project/README.md`, `outputs.yaml` and `references/data-mesh-guide.md`).

It sits in Databases, covering Data warehousing, Data governance and Software architecture. It works with Databricks. The repository describes itself as: Comprehensive UI/UX and Backend component design skills for AI-assisted development with Claude. The licence is MIT.

When your agent uses it

  • Architecting data platforms
  • Choosing between centralized vs decentralized patterns
  • Selecting table formats (Iceberg
  • Designing data governance frameworks

Example prompts

  • “/architecting-data”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Storage Paradigms
  2. Data Modeling Approaches
  3. Data Mesh Principles
  4. Medallion Architecture
  5. Open Table Formats
  6. Modern Data Stack
  7. Data Governance

What it can do on your machine

Read from SKILL.md and the folder at commit 76551b7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are sql).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • iceberg.apache.org
    • docs.getdbt.com
    • datamesh-architecture.com
    • databricks.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Architecting Data loads about 3.6k tokens when it runs, and up to ~23k if it reads all its reference files. Until then it costs about 112 tokens; SKILL.md has 1,276 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~23k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ancoleman/ai-design-components at commit 76551b7, republished under its MIT licence (© ancoleman). 1,276 words, ~3,588 tokens.

Download SKILL.mdSave it as .claude/skills/architecting-data/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.
name
architecting-data
description
Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.

Data Architecture

Purpose

Guide architects and platform engineers through strategic data architecture decisions for modern cloud-native data platforms.

When to Use This Skill

Invoke this skill when:

  • Designing a new data platform or modernizing legacy systems
  • Choosing between data lake, data warehouse, or data lakehouse
  • Deciding on data modeling approaches (dimensional, normalized, data vault, wide tables)
  • Evaluating centralized vs data mesh architecture
  • Selecting open table formats (Apache Iceberg, Delta Lake, Apache Hudi)
  • Designing medallion architecture (bronze, silver, gold layers)
  • Implementing data governance and cataloging

Core Concepts

1. Storage Paradigms

Three primary patterns for analytical data storage:

Data Lake: Centralized repository for raw data at scale

  • Schema-on-read, cost-optimized ($0.02-0.03/GB/month)
  • Use when: Diverse data sources, exploratory analytics, ML/AI training data

Data Warehouse: Structured repository optimized for BI

  • Schema-on-write, ACID transactions, fast queries
  • Use when: Known BI requirements, strong governance needed

Data Lakehouse: Hybrid combining lake flexibility with warehouse reliability

  • Open table formats (Iceberg, Delta Lake), ACID on object storage
  • Use when: Mixed BI + ML workloads, cost optimization (60-80% cheaper than warehouse)

Decision Framework:

  • BI/Reporting only + Known queries → Data Warehouse
  • ML/AI primary + Raw data needed → Data Lake or Lakehouse
  • Mixed BI + ML + Cost optimization → Data Lakehouse (recommended)
  • Exploratory/Unknown use cases → Data Lake

For detailed comparison, see references/storage-paradigms.md.

2. Data Modeling Approaches

Four primary modeling patterns:

Dimensional (Kimball): Star/snowflake schemas for BI

  • Use when: Known query patterns, BI dashboards, trend analysis

Normalized (3NF): Eliminate redundancy for transactional systems

  • Use when: OLTP systems, frequent updates, strong consistency

Data Vault 2.0: Flexible model with complete audit trail

  • Use when: Compliance requirements, multiple sources, agile warehousing

Wide Tables: Denormalized, optimized for columnar storage

  • Use when: ML feature stores, data science notebooks, high-performance dashboards

Decision Framework:

  • Analytical (BI) + Known queries → Dimensional (Star Schema)
  • Transactional (OLTP) → Normalized (3NF)
  • Compliance/Audit → Data Vault 2.0
  • Data Science/ML → Wide Tables

For detailed patterns, see references/modeling-approaches.md.

3. Data Mesh Principles

Decentralized architecture for large organizations (>500 people).

Four Core Principles:

  1. Domain-oriented decentralization
  2. Data as a product (SLAs, quality, documentation)
  3. Self-serve data infrastructure
  4. Federated computational governance

Readiness Assessment (Score 1-5 each):

  1. Domain clarity
  2. Team maturity
  3. Platform capability
  4. Governance maturity
  5. Scale need
  6. Organizational buy-in

Scoring: 24-30: Strong candidate | 18-23: Hybrid | 12-17: Build foundation first | 6-11: Centralized

Red Flags: Small org (<100 people), unclear domains, no platform team, weak governance

For full guide, see references/data-mesh-guide.md.

4. Medallion Architecture

Standard lakehouse pattern: Bronze (raw) → Silver (cleaned) → Gold (business-level)

Bronze Layer: Exact copy of source data, immutable, append-only

Silver Layer: Validated, deduplicated, typed data

Gold Layer: Business logic, aggregates, dimensional models, ML features

Data Quality by Layer:

  • Bronze → Silver: Schema validation, type checks, deduplication
  • Silver → Gold: Business rule validation, referential integrity
  • Gold: Anomaly detection, statistical checks

For patterns, see references/medallion-pattern.md.

5. Open Table Formats

Enable ACID transactions on data lakes:

Apache Iceberg: Multi-engine, vendor-neutral (Context7: 79.7 score)

  • Use when: Avoid vendor lock-in, multi-engine flexibility

Delta Lake: Databricks ecosystem, Spark-optimized

  • Use when: Committed to Databricks

Apache Hudi: Optimized for CDC and frequent upserts

  • Use when: CDC-heavy workloads

Recommendation: Apache Iceberg for new projects (vendor-neutral, broadest support)

For comparison, see references/table-formats.md.

6. Modern Data Stack

Standard Layers:

  • Ingestion: Fivetran, Airbyte, Kafka
  • Storage: Snowflake, Databricks, BigQuery
  • Transformation: dbt (Context7: 87.0 score), Spark
  • Orchestration: Airflow, Dagster, Prefect
  • Visualization: Tableau, Looker, Power BI
  • Governance: DataHub, Alation, Great Expectations

Tool Selection:

  • Fivetran vs Airbyte: Pre-built connectors vs cost-sensitive
  • Snowflake vs Databricks: BI-focused vs ML-focused
  • dbt vs Spark: SQL-based vs large-scale processing

For detailed recommendations, see references/tool-recommendations.md and references/modern-data-stack.md.

7. Data Governance

Data Catalog: Searchable inventory (DataHub, Alation, Collibra)

Data Lineage: Track data flow (OpenLineage, Marquez)

Data Quality: Validation and testing (Great Expectations, Soda, dbt tests)

Access Control:

  • RBAC: Role-based (sales_analyst role)
  • ABAC: Attribute-based (row-level security)
  • Column-level: Dynamic data masking for PII

For governance patterns, see references/governance-patterns.md.

Decision Frameworks

Framework 1: Storage Paradigm Selection

Step 1: Identify Primary Use Case

  • BI/Reporting only → Data Warehouse
  • ML/AI primary → Data Lake or Lakehouse
  • Mixed BI + ML → Data Lakehouse
  • Exploratory → Data Lake

Step 2: Evaluate Budget

  • High budget, known queries → Data Warehouse
  • Cost-sensitive, flexible → Data Lakehouse

Recommendation by Org Size:

  • Startup (<50): Data Warehouse (simplicity)
  • Growth (50-500): Data Lakehouse (balance)
  • Enterprise (>500): Hybrid or unified Lakehouse

See references/decision-frameworks.md.

Framework 2: Data Modeling Approach

Decision Tree:

  • Analytical (BI) workload → Dimensional or Wide Tables
  • Transactional (OLTP) → Normalized (3NF)
  • Compliance/Audit → Data Vault 2.0
  • Data Science/ML → Wide Tables

See references/decision-frameworks.md.

Framework 3: Data Mesh Readiness

Use 6-factor assessment. Score interpretation:

  • 24-30: Proceed with data mesh
  • 18-23: Hybrid approach
  • 12-17: Build foundation first
  • 6-11: Centralized

See references/decision-frameworks.md.

Framework 4: Open Table Format Selection

Decision Tree:

  • Multi-engine flexibility → Apache Iceberg
  • Databricks ecosystem → Delta Lake
  • Frequent upserts/CDC → Apache Hudi

Recommendation: Apache Iceberg for new projects

See references/decision-frameworks.md.

Show full SKILL.md (506 more words)Show less

Common Scenarios

Startup Data Platform

Context: 50-person startup, PostgreSQL + MongoDB + Stripe

Recommendation:

  • Storage: BigQuery or Snowflake
  • Ingestion: Airbyte or Fivetran
  • Transformation: dbt
  • Orchestration: dbt Cloud
  • Architecture: Simple data warehouse

See references/scenarios.md.

Enterprise Modernization

Context: Legacy Oracle warehouse, need cloud migration

Recommendation:

  • Storage: Data Lakehouse (Databricks or Snowflake with Iceberg)
  • Strategy: Incremental migration with CDC
  • Architecture: Medallion (bronze, silver, gold)
  • Cost Savings: 60-80%

See references/scenarios.md.

Data Mesh Assessment

Context: 200-person company, 5-person central data team

Recommendation: NOT YET. Build foundation first.

  • Organization too small (<500 recommended)
  • Central team not yet bottleneck
  • Invest in self-serve platform and governance

See references/scenarios.md.

Tool Recommendations

Research-Validated (Context7, December 2025)

dbt: Score 87.0, 3,532+ code snippets

  • SQL-based transformations, version control, testing
  • Industry standard for data transformation

Apache Iceberg: Score 79.7, 832+ code snippets

  • Open table format, multi-engine, vendor-neutral
  • Production-ready (Netflix, Apple, Adobe)

Tool Stack by Use Case:

Startup: BigQuery + Airbyte + dbt + Metabase (<$1K/month)

Growth: Snowflake + Fivetran + dbt + Airflow + Tableau ($10K-50K/month)

Enterprise: Snowflake + Databricks + Fivetran + Kafka + dbt + Airflow + Alation ($50K-500K/month)

See references/tool-recommendations.md.

Implementation Patterns

Pattern 1: Medallion Architecture
sql
-- Bronze: Raw ingestion
CREATE TABLE bronze.raw_customers (_ingested_at TIMESTAMP, _raw_data STRING);

-- Silver: Cleaned
CREATE TABLE silver.customers AS
SELECT json_extract(_raw_data, '$.id') AS customer_id, ...
FROM bronze.raw_customers
QUALIFY ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY _ingested_at DESC) = 1;

-- Gold: Business-level
CREATE TABLE gold.fact_sales AS
SELECT s.order_id, d.date_key, c.customer_key, ...
FROM silver.sales s
JOIN gold.dim_date d ON s.order_date = d.date;
Pattern 2: Apache Iceberg Table
sql
CREATE TABLE catalog.db.sales (order_id BIGINT, amount DECIMAL(10,2))
USING iceberg
PARTITIONED BY (days(order_date));

-- Time travel
SELECT * FROM catalog.db.sales TIMESTAMP AS OF '2025-01-01';
Pattern 3: dbt Transformation
sql
-- models/staging/stg_customers.sql
WITH source AS (SELECT * FROM {{ source('raw', 'customers') }}),
cleaned AS (
  SELECT customer_id, UPPER(customer_name) AS customer_name
  FROM source WHERE customer_id IS NOT NULL
)
SELECT * FROM cleaned

For complete examples, see examples/.

Best Practices

  1. Start simple: Avoid over-engineering; begin with warehouse or basic lakehouse
  2. Invest in governance early: Catalog, lineage, quality from day one
  3. Medallion architecture: Use bronze-silver-gold for clear quality layers
  4. Open table formats: Prefer Iceberg or Delta Lake to avoid vendor lock-in
  5. Assess mesh readiness: Don't decentralize prematurely (<500 people)
  6. Automate quality: Integrate tests (Great Expectations, dbt) into CI/CD
  7. Monitor pipelines: Observability is critical (freshness, quality, health)
  8. Document as code: Use dbt docs, DataHub, YAML for self-service
  9. Incremental loading: Only load new/changed data (watermark columns)
  10. Business alignment: Align architecture to outcomes, not just technologies

Anti-Patterns

  • ❌ Data swamp: Lake without governance or cataloging
  • ❌ Premature mesh: Mesh before organizational readiness
  • ❌ Tool sprawl: Too many tools without integration
  • ❌ No quality checks: "Garbage in, garbage out"
  • ❌ Centralized bottleneck: Single team in large org (>500 people)
  • ❌ Vendor lock-in: Proprietary formats without migration path
  • ❌ No lineage: Can't answer "where did this come from?"
  • ❌ Over-engineering: Complex architecture for simple use cases

Integration with Other Skills

Direct Dependencies:

  • ingesting-data: ETL/ELT mechanics, Fivetran, Airbyte implementation
  • data-transformation: dbt and Dataform detailed implementation
  • streaming-data: Kafka, Flink for real-time pipelines

Complementary:

  • databases-relational: PostgreSQL, MySQL as source systems
  • databases-document: MongoDB, DynamoDB as sources
  • ai-data-engineering: Feature stores, ML training pipelines
  • designing-distributed-systems: CAP theorem, consistency models
  • observability: Monitoring pipeline health, data quality metrics

Downstream:

  • visualizing-data: BI and dashboard patterns
  • sql-optimization: Query performance tuning

Common Workflows:

End-to-End Analytics:

data-architecture (warehouse) → ingesting-data (Fivetran) →
data-transformation (dbt) → visualizing-data (Tableau)

Data Platform for AI/ML:

data-architecture (lakehouse) → ingesting-data (Kafka) →
data-transformation (dbt features) → ai-data-engineering (feature store)

Further Reading

Reference Files:

Examples:

External Resources:

© ancoleman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 13 other files (references) in skills/architecting-data of ancoleman/ai-design-components.

  • SKILL.md
  • examples/dbt-project/README.md
  • examples/dbt-project/stg_customers.sql
  • outputs.yaml
  • references/data-mesh-guide.md
  • references/decision-frameworks.md
  • references/governance-patterns.md
  • references/medallion-pattern.md
  • references/modeling-approaches.md
  • references/modern-data-stack.md
  • references/scenarios.md
  • references/storage-paradigms.md
  • references/table-formats.md
  • references/tool-recommendations.md

Open the folder on GitHubat commit 76551b7

Compare with similar skills

Architecting Data next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Architecting Data compared with similar skills
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Architecting Data this skillancoleman/ai-design-components526—~3.6kAutomated safety check: PassMIT
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Databricks Dbsqldatabricks/databricks-agent-skills3451 repos~2.8kAutomated safety check: PassCustom licence
Altimate Data Warehouse DelegateAltimateAI/data-engineering-skills127—~1.4kAutomated safety check: PassMIT
Rocky New Adapterrocky-data/rocky304—~2kAutomated safety check: PassApache-2.0
SQL Queriesw95/awesome-claude-corporate-skills2353 repos~2.8kAutomated safety check: PassMIT

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Works with

Categories

Questions about Architecting Data

What does Architecting Data do?

Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data…. Architecting Data is an agent skill from ancoleman/ai-design-components. Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns.

When should I use Architecting Data?

Architecting Data fits situations like: architecting data platforms; choosing between centralized vs decentralized patterns; selecting table formats (Iceberg; designing data governance frameworks.

How do I install Architecting Data in Claude Code?

Run `npx skills add ancoleman/ai-design-components --skill architecting-data -a claude-code`. Or copy the skill folder (skills/architecting-data in ancoleman/ai-design-components) into .claude/skills/architecting-data in your project. Claude Code loads it when a task matches its description.

How do I install Architecting Data in Codex?

Run `npx skills add ancoleman/ai-design-components --skill architecting-data -a codex`. Or copy the skill folder (skills/architecting-data in ancoleman/ai-design-components) into .agents/skills/architecting-data in your project. Codex loads it when a task matches its description.

Can I use Architecting Data in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ancoleman/ai-design-components --skill architecting-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/architecting-data, .gemini/skills/architecting-data, .github/skills/architecting-data and .opencode/skills/architecting-data in your project.

What does Architecting Data need to run?

SKILL.md names no scripts, command-line tools or credentials: Architecting Data is instructions for the agent only.

Does Architecting Data access the network?

SKILL.md names 4 domains. As links in the text: iceberg.apache.org, docs.getdbt.com, datamesh-architecture.com and databricks.com. This is read from the text; nothing was executed.

Is Architecting Data safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Architecting Data use?

Architecting Data is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Architecting Data use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 20k tokens, read only when the agent opens those files.

What are the alternatives to Architecting Data?

Skills that share tags, products or a category with Architecting Data: Data Lakehouse Architect (FerroxLabs/wayland, 608 stars), Databricks Dbsql (databricks/databricks-agent-skills, 345 stars), Altimate Data Warehouse Delegate (AltimateAI/data-engineering-skills, 127 stars) and Rocky New Adapter (rocky-data/rocky, 304 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Architecting Data?

ancoleman (a GitHub user) maintains it in ancoleman/ai-design-components, which has 526 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on December 11, 2025.

Source: ancoleman/ai-design-components on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.