control-namespace-health

star 1

Report fill-rate and orphan health across the control namespace instance data. Read-only data quality audit. Triggers on: 'control health', 'fill rate', 'orphan nodes', 'data quality', 'control completeness', 'sparse nodes'.

mhoh79 By mhoh79 schedule Updated 6/8/2026

name: control-namespace-health description: "Report fill-rate and orphan health across the control namespace instance data. Read-only data quality audit. Triggers on: 'control health', 'fill rate', 'orphan nodes', 'data quality', 'control completeness', 'sparse nodes'."

Control Namespace Health

When to Use

  • User wants a data-quality overview of control namespace instances
  • User asks for fill-rate statistics (which properties are populated)
  • User wants to find orphan or sparse nodes in the control graph
  • User asks for a health dashboard before a phase-gate review

Workflow

  1. Enumerate control models. List all models in the namespace:

    gm_model_list(namespace="control", summary_only=True)
    
  2. Profile each model. Run data profiling per model to get fill-rate statistics:

    gm_data_profile(model_ref="alarm-management", namespace="control")
    

    Repeat for each control model that has instance data in Neo4j.

  3. Validate instance data. Run instance validation to detect orphans and sparse nodes:

    gm_data_validate(model_ref="alarm-management", namespace="control")
    
  4. Aggregate fill rates. Collect per-label, per-property fill percentages across all models. Flag properties below 50% fill as warnings and below 20% as errors.

  5. Render health dashboard. Present findings as a markdown report:

    • Summary table: model, label count, relationship count, avg fill %
    • Low-fill properties table: model, label, property, fill %
    • Orphan/sparse node list with model and label
    • Overall health score (% of properties above threshold)

Pitfalls

  • This skill is read-only — it reports data quality but does not fix it. Instance fixes use gm_cypher(mode="write"); type fixes go through the ontologist.
  • Low fill rates on stub nodes are expected — stubs intentionally carry fewer properties than their source-of-truth counterparts.
  • Models without instance data in Neo4j will return empty profiles — this is not an error, just an indication that the model has not been materialized or populated yet.

Related Skills

Install via CLI
npx skills add https://github.com/mhoh79/leibniz --skill control-namespace-health
Repository Details
star Stars 1
call_split Forks 0
navigation Branch main
article Path SKILL.md
More from Creator