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Agent skill

Data Quality Audit

by nimrodfisher in nimrodfisher/data-analytics-skills

Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations.

MITAuto-check passedData & Analytics

Install Data Quality Audit

skills CLI
$ npx skills add nimrodfisher/data-analytics-skills --skill data-quality-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nimrodfisher/data-analytics-skills data-quality-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/01-data-quality-validation/data-quality-audit .claude/skills/data-quality-audit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
data-quality-audit
GitHub stars
468
Token cost
~685 tokens
SKILL.md length
303 words
Files
10 (incl. scripts, references, assets)
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations.

  • Works in 7 steps: Null and completeness audit — run… → Duplicate detection — run… → Referential integrity check — run… → …
  • Tasks that involve Data cleaning
  • Runs Python scripts from its folder
  • Tasks that involve Data pipelines and ETL

What it does

Data Quality Audit is an agent skill from nimrodfisher/data-analytics-skills. Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.

Its SKILL.md is about 690 tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts, reference files and assets (for example `assets/quality_rubric.md`, `references/business_rule_patterns.md` and `references/quality_dimensions.md`).

It sits in Data & Analytics, covering Data cleaning and Data pipelines and ETL. The repository describes itself as: A comprehensive list of Claude & Codex skills for a wide range of data analytics tasks. The licence is MIT.

When your agent uses it

  • Tasks that involve Data cleaning
  • Tasks that involve Data pipelines and ETL

Example prompts

  • “/data-quality-audit”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Null and completeness audit — run scripts/null_counter.py for a column-by-column null profile. Flag columns above acceptable thresholds…
  2. Duplicate detection — run scripts/duplicate_finder.py to identify full-row and key-level duplicates. Determine if duplicates are…
  3. Referential integrity check — run scripts/referential_integrity.py to validate that foreign key values in child tables exist in parent…
  4. Value range validation — run scripts/value_range_validator.py with business rules defined in references/business_rule_patterns.md. Flag…
  5. Freshness check — run scripts/freshness_check.py to verify the dataset is up to date — compare the latest record timestamp against the…
  6. Score and classify findings — map each finding to a quality dimension using references/quality_dimensions.md. Assign severity (CRITICAL /…
  7. Produce deliverables — fill assets/audit_report_template.html for a shareable report; fill assets/quality_rubric.md for a concise scorecard.

What it can do on your machine

Read from SKILL.md and the folder at commit 9449d36. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 5 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Data Quality Audit loads about 685 tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 303 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~685
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from nimrodfisher/data-analytics-skills at commit 9449d36, republished under its MIT licence (© nimrodfisher). 303 words, ~685 tokens.

Download SKILL.mdSave it as .claude/skills/data-quality-audit/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
data-quality-audit
description
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.

When to use

  • A data pipeline has just loaded new data and needs validation before downstream reports consume it
  • A stakeholder has flagged data quality concerns (wrong totals, unexpected nulls, stale data)
  • You need to produce a formal data quality scorecard for a data asset as part of a data governance process
  • You are onboarding a new data source and need to understand its quality profile before building on it

Process

  1. Null and completeness audit — run scripts/null_counter.py for a column-by-column null profile. Flag columns above acceptable thresholds for the business context.
  2. Duplicate detection — run scripts/duplicate_finder.py to identify full-row and key-level duplicates. Determine if duplicates are intentional (versioning) or errors (pipeline fan-out).
  3. Referential integrity check — run scripts/referential_integrity.py to validate that foreign key values in child tables exist in parent tables. Report orphan rate per relationship.
  4. Value range validation — run scripts/value_range_validator.py with business rules defined in references/business_rule_patterns.md. Flag values outside acceptable ranges.
  5. Freshness check — run scripts/freshness_check.py to verify the dataset is up to date — compare the latest record timestamp against the expected lag for this pipeline.
  6. Score and classify findings — map each finding to a quality dimension using references/quality_dimensions.md. Assign severity (CRITICAL / HIGH / MEDIUM / LOW).
  7. Produce deliverables — fill assets/audit_report_template.html for a shareable report; fill assets/quality_rubric.md for a concise scorecard.

Inputs the skill needs

  • Required: dataset (CSV / Parquet / database table reference)
  • Required: schema relationships — which columns are primary keys, which are foreign keys to which tables
  • Required: business rules — acceptable value ranges, expected value sets, freshness SLA
  • Optional: acceptable error rates — at what threshold does a failure become CRITICAL vs. HIGH
  • Optional: pipeline schedule — to assess freshness relative to expected update frequency

Output

  • assets/audit_report_template.html (filled) — full quality report, shareable with stakeholders
  • assets/quality_rubric.md (filled) — one-page quality scorecard with dimension scores
  • Script console output — per-check pass/fail counts for each validation script

© nimrodfisher, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (scripts, references, assets) in 01-data-quality-validation/data-quality-audit of nimrodfisher/data-analytics-skills.

  • SKILL.md
  • assets/audit_report_template.html
  • assets/quality_rubric.md
  • references/business_rule_patterns.md
  • references/quality_dimensions.md
  • scripts/duplicate_finder.py
  • scripts/freshness_check.py
  • scripts/null_counter.py
  • scripts/referential_integrity.py
  • scripts/value_range_validator.py

Open the folder on GitHubat commit 9449d36

Compare with similar skills

Data Quality Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Data Quality Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Data Quality Audit this skillnimrodfisher/data-analytics-skills468—~685Automated safety check: PassMIT
Credit Risk Data Cleaninggithub/awesome-copilot40k1 repos~1.5kAutomated safety check: PassMIT
Data Pipelineagulli/atlas-agents579—~714Automated safety check: PassMIT
Authoritative Data Harvesteryushui2022/MathModel-Skill4531 repos~1.1kAutomated safety check: PassMIT
Data Quality Frameworkswshobson/agents40k11 repos~1.1kAutomated safety check: PassMIT
Dbt Transformation Patternswshobson/agents40k9 repos~781Automated safety check: PassMIT

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Categories

Questions about Data Quality Audit

What does Data Quality Audit do?

Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Data Quality Audit is an agent skill from nimrodfisher/data-analytics-skills. Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations.

When should I use Data Quality Audit?

Data Quality Audit fits situations like: tasks that involve Data cleaning; tasks that involve Data pipelines and ETL.

How do I install Data Quality Audit in Claude Code?

Run `npx skills add nimrodfisher/data-analytics-skills --skill data-quality-audit -a claude-code`. Or copy the skill folder (01-data-quality-validation/data-quality-audit in nimrodfisher/data-analytics-skills) into .claude/skills/data-quality-audit in your project. Claude Code loads it when a task matches its description.

How do I install Data Quality Audit in Codex?

Run `npx skills add nimrodfisher/data-analytics-skills --skill data-quality-audit -a codex`. Or copy the skill folder (01-data-quality-validation/data-quality-audit in nimrodfisher/data-analytics-skills) into .agents/skills/data-quality-audit in your project. Codex loads it when a task matches its description.

Can I use Data Quality Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nimrodfisher/data-analytics-skills --skill data-quality-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-quality-audit, .gemini/skills/data-quality-audit, .github/skills/data-quality-audit and .opencode/skills/data-quality-audit in your project.

What does Data Quality Audit need to run?

Going by SKILL.md and its folder, Data Quality Audit needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Data Quality Audit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Data Quality Audit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Data Quality Audit use?

Data Quality Audit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Data Quality Audit use?

About 685 tokens (SKILL.md is roughly 2.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.1k tokens, read only when the agent opens those files.

What are the alternatives to Data Quality Audit?

Skills that share tags, products or a category with Data Quality Audit: Credit Risk Data Cleaning (github/awesome-copilot, 40k stars), Data Pipeline (agulli/atlas-agents, 579 stars), Authoritative Data Harvester (yushui2022/MathModel-Skill, 453 stars) and Data Quality Frameworks (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Data Quality Audit?

nimrodfisher (a GitHub user) maintains it in nimrodfisher/data-analytics-skills, which has 468 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on September 25, 2026.

Source: nimrodfisher/data-analytics-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.