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Skill

source-profiler

@pupfish-llc Updated 2026-05-18

Data source profiling capability for Qlik development. When MCP connections are available, provides query templates and profiling procedures to assess source schemas, field types, cardinality, null rates, sample values, and data quality indicators. When MCP is unavailable, generates a structured Source Profile Template for manual completion. Includes source architecture classification (annotating tables with architectural role, key structure, mutability, incremental load pattern, and consumption implications). Load during requirements gathering or source profiling phases.

Install

With your agent

Install OMGSkills skill "pupfish-llc/qlik-agents:skills/source-profiler" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.

Manual command

git clone https://github.com/Pupfish-LLC/qlik-agents /tmp/qlik-agents && ln -s /tmp/qlik-agents/skills/source-profiler ~/.claude/skills/source-profiler

From README

Source Profiling for Data-Driven Architecture Before designing a Qlik data model, you must understand the source data: what tables exist, what fields are in each table, what data types they use, how many distinct values exist in each field, whether null values are present, and what the source system's architecture pattern is (dimensional warehouse, normalized OLTP, Data Vault 2.0, flat files, etc.). Building a data model on incorrect assumptions about cardinality, key uniqueness, or data types produces models that fail on reload or produce silent data errors. Source profiling is Phase 2 of the pipeline. The requirements-analyst invokes this skill with a two-path design: Path A (MCP Available): The requirements-analyst uses MCP database connections to run profiling queries directly against the source, automatically discovering schema metadata, cardinality, null rates, and sample values.