data-exploration-completeness-score

Sub-skill of data-exploration: Completeness Score (+3).

5 stars

Best use case

data-exploration-completeness-score is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of data-exploration: Completeness Score (+3).

Teams using data-exploration-completeness-score should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/completeness-score/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/data/analytics/data-exploration/completeness-score/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/completeness-score/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How data-exploration-completeness-score Compares

Feature / Agentdata-exploration-completeness-scoreStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Sub-skill of data-exploration: Completeness Score (+3).

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Completeness Score (+3)

## Completeness Score


Rate each column:
- **Complete** (>99% non-null): Green
- **Mostly complete** (95-99%): Yellow -- investigate the nulls
- **Incomplete** (80-95%): Orange -- understand why and whether it matters
- **Sparse** (<80%): Red -- may not be usable without imputation


## Consistency Checks


Look for:
- **Value format inconsistency**: Same concept represented differently ("USA", "US", "United States", "us")
- **Type inconsistency**: Numbers stored as strings, dates in various formats
- **Referential integrity**: Foreign keys that don't match any parent record
- **Business rule violations**: Negative quantities, end dates before start dates, percentages > 100
- **Cross-column consistency**: Status = "completed" but completed_at is null


## Accuracy Indicators


Red flags that suggest accuracy issues:
- **Placeholder values**: 0, -1, 999999, "N/A", "TBD", "test", "xxx"
- **Default values**: Suspiciously high frequency of a single value
- **Stale data**: Updated_at shows no recent changes in an active system
- **Impossible values**: Ages > 150, dates in the far future, negative durations
- **Round number bias**: All values ending in 0 or 5 (suggests estimation, not measurement)


## Timeliness Assessment


- When was the table last updated?
- What is the expected update frequency?
- Is there a lag between event time and load time?
- Are there gaps in the time series?

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