dbt Model Builder
AltimateAI/data-engineering-skills
Creates or modifies dbt models in line with a project's own conventions, then runs dbt build and dbt show to check the output instead of stopping at compile.
Design the data quality checks for a table or pipeline across the standard dimensions.
Install Data Quality Checks
$ npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-quality-checks --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-quality-checks .claude/skills/data-quality-checks && rm -rf skills-srcUse ~/.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/
Install the "data-quality-checks" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-quality-checks into .claude/skills/data-quality-checks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checks", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-quality-checksType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-quality-checks --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-quality-checks .agents/skills/data-quality-checks && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-quality-checks" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-quality-checks into .agents/skills/data-quality-checks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checks", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-quality-checks --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-quality-checks .cursor/skills/data-quality-checks && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "data-quality-checks" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-quality-checks into .cursor/skills/data-quality-checks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checks", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mohitagw15856/pm-claude-skills.git --path skills/data-quality-checks--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-quality-checks --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-quality-checks .gemini/skills/data-quality-checks && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "data-quality-checks" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-quality-checks into .gemini/skills/data-quality-checks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checks", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mohitagw15856/pm-claude-skills data-quality-checksInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-quality-checks .github/skills/data-quality-checks && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "data-quality-checks" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-quality-checks into .github/skills/data-quality-checks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checks", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-quality-checks --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-quality-checks .opencode/skills/data-quality-checks && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "data-quality-checks" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-quality-checks into .opencode/skills/data-quality-checks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-checks", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
data-quality-checks- GitHub stars
- 1.4k
- Token cost
- ~919 tokens
- SKILL.md length
- 429 words
- Files
- 1
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Design the data quality checks for a table or pipeline across the standard dimensions.
- Asked to add data quality tests
- SKILL.md covers Required Inputs, Output Format, Quality Checks and Anti-Patterns, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Define DQ checks
What it does
Data Quality Checks is an agent skill from mohitagw15856/pm-claude-skills. Design the data quality checks for a table or pipeline across the standard dimensions. Use when asked to add data quality tests, define DQ checks, catch bad data before it hits dashboards, or set up monitoring for a dataset. Produces a checks plan across completeness, validity, uniqueness, freshness, consistency, and accuracy — each with the rule, severity, and where it runs (dbt test / Great Expectations / SQL assertion).
Its SKILL.md is about 920 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Data cleaning, Data pipelines and ETL and SQL. It works with dbt and SQL. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.
When your agent uses it
- Asked to add data quality tests
- Define DQ checks
- Catch bad data before it hits dashboards
- Set up monitoring for a dataset
Example prompts
- “/data-quality-checks”
What it can do on your machine
Read from SKILL.md and the folder at commit 1cbf1f0. 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
No scripts in the folder and no shell commands in SKILL.md.
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 Checks loads about 919 tokens when it runs. Until then it costs about 112 tokens; SKILL.md has 429 words of instructions outside code blocks.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 429 words, ~919 tokens.
Download SKILL.mdSave it as
.claude/skills/data-quality-checks/SKILL.md (or your agent's skills folder).- name
- data-quality-checks
- description
- Design the data quality checks for a table or pipeline across the standard dimensions. Use when asked to add data quality tests, define DQ checks, catch bad data before it hits dashboards, or set up monitoring for a dataset. Produces a checks plan across completeness, validity, uniqueness, freshness, consistency, and accuracy — each with the rule, severity, and where it runs (dbt test / Great Expectations / SQL assertion).
Data Quality Checks Skill
Bad data quietly poisons dashboards and models until someone notices the number is wrong. The fix is checks that fail loudly before that — across the standard DQ dimensions. This skill designs them for a specific table/pipeline: the exact rule per dimension, its severity (block vs. warn), and where it runs (dbt test, Great Expectations, or a SQL assertion), so quality is enforced, not hoped for.
Required Inputs
Ask for these only if they aren't already provided:
- The table/pipeline and what it represents (grain, key columns).
- The columns that matter — keys, required fields, enums, ranges, dates.
- Freshness expectation — how current the data must be.
- Tooling — dbt tests, Great Expectations, Soda, or raw SQL assertions.
Output Format
Data Quality Checks: [table]
Checks organised by dimension — each with the rule, severity (🔴 block the pipeline / 🟡 warn), and where it runs:
| Dimension | Check | Rule | Severity | Implement as |
|---|---|---|---|---|
| Completeness | required fields non-null | not_null on [cols] | 🔴 | dbt test |
| Uniqueness | grain key unique | unique on [key] | 🔴 | dbt test |
| Validity | values in allowed set/range | accepted_values / range | 🟡 | GE / SQL |
| Freshness | data is current | max(loaded_at) within SLA | 🔴 | dbt source freshness |
| Consistency | cross-field / cross-table | e.g. totals reconcile, FK exists | 🟡 | SQL assertion |
| Accuracy | matches a source of truth | reconcile vs. system-of-record | 🟡 | SQL assertion |
Notes:
- Severity discipline — only block on checks that should stop the pipeline (a duplicated grain key, stale critical data). Over-blocking trains people to ignore alerts.
- Where to check — at ingestion (catch early) vs. in the model vs. post-build; recommend per check.
- On failure — what happens (halt, quarantine rows, alert + continue) and who's paged.
Show full SKILL.md (172 more words)Show less
Quality Checks
- Covers the core dimensions (completeness, uniqueness, validity, freshness, consistency)
- Each check has an explicit rule and a severity (block vs. warn)
- Severity is disciplined — only truly critical checks block the pipeline
- Freshness has a measurable SLA, not "should be recent"
- Each check names where it runs and what happens on failure
Anti-Patterns
- Do not block the pipeline on every check — alert fatigue makes people ignore the real failures; reserve 🔴 for critical
- Do not only test the happy path — the grain key, nulls, and freshness are where the real breakage hides
- Do not write checks with no failure action — a test that fails into the void changes nothing
- Do not skip freshness — stale data that looks fine is the most dangerous kind
- Do not check only one table in isolation — cross-table consistency (FKs, reconciliations) catches integration bugs
Based On
Data-quality practice — the six DQ dimensions, dbt tests / Great Expectations / source-freshness, severity-tiered enforcement.
Example Trigger Phrases
- "Define DQ checks."
- "Catch bad data before it hits dashboards."
- "Set up monitoring for a dataset."
© mohitagw15856, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/data-quality-checks of mohitagw15856/pm-claude-skills.
Open the folder on GitHubat commit 1cbf1f0
Compare with similar skills
Data Quality Checks 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Data Quality Checks this skillmohitagw15856/pm-claude-skills | 1.4k | — | ~919 | Automated safety check: Pass | MIT | |
| dbt Model BuilderAltimateAI/data-engineering-skills | 128 | — | ~890 | Automated safety check: Pass | MIT | |
| dbt Error DebuggingAltimateAI/data-engineering-skills | 128 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Analytics Engineerborghei/Claude-Skills | 886 | — | ~3.4k | Automated safety check: Pass | MIT | |
| Migrating SQL To DbtAltimateAI/data-engineering-skills | 128 | — | ~762 | Automated safety check: Pass | MIT | |
| Databricks JobsKilo-Org/kilo-marketplace | 190 | 1 repos | ~3.1k | Automated safety check: Pass | Custom licence |
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Related
Categories
Questions about Data Quality Checks
What does Data Quality Checks do?
Design the data quality checks for a table or pipeline across the standard dimensions. Data Quality Checks is an agent skill from mohitagw15856/pm-claude-skills. Design the data quality checks for a table or pipeline across the standard dimensions.
When should I use Data Quality Checks?
Data Quality Checks fits situations like: asked to add data quality tests; define DQ checks; catch bad data before it hits dashboards; set up monitoring for a dataset.
How do I install Data Quality Checks in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks -a claude-code`. Or copy the skill folder (skills/data-quality-checks in mohitagw15856/pm-claude-skills) into .claude/skills/data-quality-checks in your project. Claude Code loads it when a task matches its description.
How do I install Data Quality Checks in Codex?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks -a codex`. Or copy the skill folder (skills/data-quality-checks in mohitagw15856/pm-claude-skills) into .agents/skills/data-quality-checks in your project. Codex loads it when a task matches its description.
Can I use Data Quality Checks 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 mohitagw15856/pm-claude-skills --skill data-quality-checks -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-checks, .gemini/skills/data-quality-checks, .github/skills/data-quality-checks and .opencode/skills/data-quality-checks in your project.
What does Data Quality Checks need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Quality Checks is instructions for the agent only.
Does Data Quality Checks 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 Checks 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. Review the folder before installing.
What licence does Data Quality Checks use?
Data Quality Checks 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 Checks use?
About 919 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Data Quality Checks?
Skills that share tags, products or a category with Data Quality Checks: dbt Model Builder (AltimateAI/data-engineering-skills, 128 stars), dbt Error Debugging (AltimateAI/data-engineering-skills, 128 stars), Analytics Engineer (borghei/Claude-Skills, 886 stars) and Migrating SQL To Dbt (AltimateAI/data-engineering-skills, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Quality Checks?
mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,433 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 8, 2026.
Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.