Home / ai-analyst-lab / ai-analyst · .claude/skills/data-quality-check/SKILL.md · GitHub

data-quality-check skillA

data-quality-check is agent-read markdown (skill) from ai-analyst-lab/ai-analyst: Validate data completeness, consistency, and coverage before any analysis, flagging issues with severity ratings. Run at the start of every new analysis. Trigger on "check data quality", "is the data clean", "validate the data", "run a quality check", "what's the coverage", and on named-table questions: "tell me about the {table} table", "describe {table}", "what's in {table}"; pair schema answers with a minimum DQ probe..

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

# Skill: Data Quality Check

## Purpose
Validate data completeness, consistency, and coverage before any analysis begins, flagging issues with severity ratings so the analyst knows what blocks analysis vs. what to note as a caveat.

## When to Use
Apply this skill at the start of every new analysis, when connecting to a new data source, or when results look suspicious. Run quality checks BEFORE drawing conclusions from data.

**Also fires on table-scoped questions.** Any question that names a specific table ("tell me about {table}", "describe {table}", "what's in {table}", "show me {table}") triggers this skill. Schema-only answers are insufficient — pair the schema description with a minimum DQ probe:

- Row count
- Null rate per column (flag anything >5%)
- Date range on the primary timestamp column
- Duplicate check on the primary key
- Surface anything from `.knowledge/datasets/{active}/quirks.md` for that table

If the table is large enough that probing is expensive (>100M rows or warehouse cost concerns), tell the user and ask before running the full probe — but always run at minimum row count + PK duplicate check.

## Instructions
…

Read the whole file at its exact version.

How to install

Latest version
mdr add ai-analyst-lab/ai-analyst/data-quality-check@git:20260902.81cce81
Exact content
mdr add ai-analyst-lab/ai-analyst/data-quality-check@sha256:98a2783472ab6a1d

Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.

Badge

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Versions

versioncommittedcommitsizeaudit
git:20260902.81cce81 latest2026-09-02 81cce81 13,209 BA view · diff
git:20260828.86521c02026-08-28 86521c0 17,014 BA view · diff
git:20260827.7ff2e252026-08-27 7ff2e25 17,032 BA view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (13209 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

ai-analyst-lab/ai-analyst · 302 stars · license MIT · pushed 2026-09-22 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_qs3ycyesl55rz3x3
GET https://markdownregistry.com/api/v1/resolve?ref=ai-analyst-lab/ai-analyst/data-quality-check
GET https://markdownregistry.com/api/v1/blob/98a2783472ab6a1dbdf1e03bfa4b9715cd6dec30a727aa8234f2dcc57e93de4f

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