/datasource

SkillDev tools

Create a DataSource note documenting a specific database, table, API endpoint, dataset, or data entity with schema, quality metrics, and access information.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the /datasource skill

About this capability

A comprehensive knowledge management system for Solutions Architects using AI

What this skill tells your AI

The instructions your AI receives, as published by davidroliverba/architectkb in .claude/skills/datasource/SKILL.md and read by ahel’s review.

Create a DataSource note documenting a specific database, table, API endpoint, dataset, or data entity with schema, quality metrics, and access information.

Usage

/datasource <name>
/datasource "SAP Invoices"
/datasource "DataPlatform Revenue Fact Table"
/datasource "Snowflake Customers"

Instructions

Phase 1: Parse Input & Link to System

  1. Extract data source name
  2. Ask which system owns this data:
    Which system owns this data source?
    Search: [user searches for System]
    Or create new System? (Y/n)
    
  3. Confirm link: "Link to [[System - {{system}}]]? (Y/n)"

Phase 2: Essential Information

Creating DataSource: {{name}} (owned by {{system}})

1️⃣ Data Type:
   - database-table (relational table)
   - database-view (virtual table)
   - api-endpoint (REST/GraphQL data)
   - kafka-topic (event stream)
   - data-warehouse-table (Snowflake/BigQuery)
   - data-lake (file-based storage)
   - cache (Redis/Memcached)
   Default: database-table
   User input: [selection]

2️⃣ Record Count (approximate):
   Default: null
   User input: [number, e.g., 5000000]

3️⃣ Data Volume per Day:
   Default: null
   User input: [e.g., "2.5GB", "500K records"]

4️⃣ Refresh Frequency:
   - real-time
   - hourly
   - daily
   - weekly
   - on-demand
   Default: daily
   User input: [selection]

5️⃣ Classification:
   - public
   - internal
   - confidential
   - secret
   Default: internal
   User input: [selection]

Phase 3: Data Quality (Optional)

Ask: "Add data quality metrics? (Y/n)"

If YES:

- Completeness (%): 98.5
- Uniqueness (%): 99.9
- Accuracy: high | medium | low
- Timeliness: how fresh (< 5 minutes, < 1 hour, etc.)

Phase 4: Schema & Key Fields

Key Fields (comma-separated):
invoice_id, vendor_id, amount

Then ask:

Schema details needed? (Y/n)
- Parent entities (sources of this data)
- Child entities (what feeds from this)
- Related tables

Phase 5: Access & Consumers

How is this data accessed?
- REST API
- GraphQL
- Direct database query
- Kafka topic
- Batch export / S3
- Other
Default: [based on data type]

Which systems consume this data?
Search: [[System - DataPlatform]]
Add: [[System - Analytics]]

Phase 6: Generate Frontmatter

type: DataSource
title: "{{name}}"

sourceId: "{{sourceId}}"
sourceSystem: "[[System - {{system}}]]"
owner: "[[{{person}}]]"

dataType: {{type}}
recordCount: {{count}}
volumePerDay: "{{volume}}"
refreshFrequency: {{frequency}}

classification: {{classification}}
gdprApplicable: {{gdpr}}
piiFields: [{{pii}}]

completeness: {{completeness}}
uniqueness: {{uniqueness}}
accuracy: {{accuracy}}
timeliness: {{timeliness}}

exposedVia: [rest-api, kafka-topic]
consumerCount: {{consumer_count}}
criticalConsumers: [{{critical_systems}}]

confidence: medium
freshness: current
verified: false

created: 2026-01-14
tags: [type/data-source, {{sourceSystem|lower}}]

Phase 7: Generate Body Content

  1. Overview: What data this contains
  2. Data Volume & Performance: Size, growth, refresh
  3. Data Schema: Sample table structure
  4. Field Mapping: Key fields explained
  5. Data Quality: Completeness, accuracy metrics
  6. Access & Integration: How systems access it
  7. Security & Governance: Classification, PII, GDPR
  8. Related Integration Notes: What uses this data

Phase 8: Create File

Filename: DataSource - {{name}}.md

Location: Vault root

Output:

✅ Created: DataSource - {{name}}.md

Linked to:
- [[System - {{owning system}}]]

Next steps:
1. Create integration: /integration {{source}} {{target}}
2. Document consumer systems
3. Add to architecture diagram

Example Interaction

User: /datasource "SAP Invoices"

System: Which system owns this data?
> SAP S/4HANA

Data Type:
> database-table

Record Count:
> 200000000

Volume per Day:
> 5GB

Refresh Frequency:
> hourly

Classification:
> confidential

Data Quality:
Completeness: 98.5
Uniqueness: 99.9
Accuracy: high
Timeliness: < 5 minutes

Key Fields:
> invoice_id, vendor_id, company_code, amount, invoice_date

Consumers (search for Systems):
> DataPlatform
> Snowflake
> Analytics

✅ Created: DataSource - SAP Invoices.md

Updated:
- [[System - SAP S/4HANA]] added to owns/exposes
- [[System - DataPlatform]] added to consumers
- [[System - Snowflake]] added to consumers

Signals

GitHub stars
52
Forks
12
Last commit
Mar 2026
Advanced
Catalog kind
skill
Gateway key
datasource
Source
github.com/davidroliverba/architectkb