Agent skill

Profiling Tables

Deep-dive data profiling for a specific table. An agent skill from astronomer/agents.

Apache-2.0Auto-check passedData & Analytics

Install Profiling Tables

skills CLI

$ npx skills add astronomer/agents --skill profiling-tables -a claude-code

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

GitHub CLI

$ gh skill install astronomer/agents profiling-tables --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/astronomer/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/profiling-tables .claude/skills/profiling-tables && 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/

Paste into Claude Code

Install the "profiling-tables" agent skill from https://github.com/astronomer/agents/tree/main/skills/profiling-tables into .claude/skills/profiling-tables/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "profiling-tables", 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

$skill-installer install https://github.com/astronomer/agents/tree/main/skills/profiling-tables

Type 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.

skills CLI

$ npx skills add astronomer/agents --skill profiling-tables -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install astronomer/agents profiling-tables --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/astronomer/agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/profiling-tables .agents/skills/profiling-tables && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "profiling-tables" agent skill from https://github.com/astronomer/agents/tree/main/skills/profiling-tables into .agents/skills/profiling-tables/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "profiling-tables", 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.

skills CLI

$ npx skills add astronomer/agents --skill profiling-tables -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install astronomer/agents profiling-tables --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/astronomer/agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/profiling-tables .cursor/skills/profiling-tables && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "profiling-tables" agent skill from https://github.com/astronomer/agents/tree/main/skills/profiling-tables into .cursor/skills/profiling-tables/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "profiling-tables", 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

$ gemini skills install https://github.com/astronomer/agents.git --path skills/profiling-tables

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add astronomer/agents --skill profiling-tables -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install astronomer/agents profiling-tables --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/astronomer/agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/profiling-tables .gemini/skills/profiling-tables && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "profiling-tables" agent skill from https://github.com/astronomer/agents/tree/main/skills/profiling-tables into .gemini/skills/profiling-tables/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "profiling-tables", 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.

GitHub CLI

$ gh skill install astronomer/agents profiling-tables

Installs 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).

skills CLI

$ npx skills add astronomer/agents --skill profiling-tables -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/astronomer/agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/profiling-tables .github/skills/profiling-tables && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "profiling-tables" agent skill from https://github.com/astronomer/agents/tree/main/skills/profiling-tables into .github/skills/profiling-tables/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "profiling-tables", 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.

skills CLI

$ npx skills add astronomer/agents --skill profiling-tables -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install astronomer/agents profiling-tables --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/astronomer/agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/profiling-tables .opencode/skills/profiling-tables && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "profiling-tables" agent skill from https://github.com/astronomer/agents/tree/main/skills/profiling-tables into .opencode/skills/profiling-tables/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "profiling-tables", 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: profiling-tables
GitHub stars: 451
Token cost: ~964 tokens
SKILL.md length: 276 words
Files: 1
Skills in repo: 34
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Deep-dive data profiling for a specific table. An agent skill from astronomer/agents.

Works in 7 steps: Basic Metadata → Size and Shape → Column-Level Statistics → …
The user asks to profile a table
SKILL.md covers Step 1: Basic Metadata, Step 2: Size and Shape, Step 3: Column-Level Statistics and Step 4: Cardinality Analysis, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Profiling Tables is an agent skill from astronomer/agents. Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.

Its SKILL.md is about 960 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, Statistics and Data analysis. The repository describes itself as: AI agent tooling for data engineering workflows. The licence is Apache-2.0.

When your agent uses it

The user asks to profile a table
Wants statistics about a dataset
Asks about data quality
Needs to understand a tables structure and content

Example prompts

“/profiling-tables”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

Basic Metadata
Size and Shape
Column-Level Statistics
Cardinality Analysis
Sample Data
Data Quality Assessment
Output Summary

What it can do on your machine

Read from SKILL.md and the folder at commit 486ee63. 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 (its code samples are sql).
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

Profiling Tables loads about 964 tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 276 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~61
When it runs · the whole SKILL.md, loaded when a task matches: ~964

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 astronomer/agents at commit 486ee63, republished under its Apache-2.0 licence (© astronomer). 276 words, ~964 tokens.

Download SKILL.mdSave it as .claude/skills/profiling-tables/SKILL.md (or your agent's skills folder).

name: profiling-tables
description: Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.

Data Profile

Generate a comprehensive profile of a table that a new team member could use to understand the data.

Step 1: Basic Metadata

Query column metadata:

sql

SELECT COLUMN_NAME, DATA_TYPE, COMMENT
FROM <database>.INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_SCHEMA = '<schema>' AND TABLE_NAME = '<table>'
ORDER BY ORDINAL_POSITION

If the table name isn't fully qualified, search INFORMATION_SCHEMA.TABLES to locate it first.

Step 2: Size and Shape

Run via run_sql:

sql

SELECT
    COUNT(*) as total_rows,
    COUNT(*) / 1000000.0 as millions_of_rows
FROM <table>

Step 3: Column-Level Statistics

For each column, gather appropriate statistics based on data type:

Numeric Columns

sql

SELECT
    MIN(column_name) as min_val,
    MAX(column_name) as max_val,
    AVG(column_name) as avg_val,
    STDDEV(column_name) as std_dev,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY column_name) as median,
    SUM(CASE WHEN column_name IS NULL THEN 1 ELSE 0 END) as null_count,
    COUNT(DISTINCT column_name) as distinct_count
FROM <table>

String Columns

sql

SELECT
    MIN(LEN(column_name)) as min_length,
    MAX(LEN(column_name)) as max_length,
    AVG(LEN(column_name)) as avg_length,
    SUM(CASE WHEN column_name IS NULL OR column_name = '' THEN 1 ELSE 0 END) as empty_count,
    COUNT(DISTINCT column_name) as distinct_count
FROM <table>

Date/Timestamp Columns

sql

SELECT
    MIN(column_name) as earliest,
    MAX(column_name) as latest,
    DATEDIFF('day', MIN(column_name), MAX(column_name)) as date_range_days,
    SUM(CASE WHEN column_name IS NULL THEN 1 ELSE 0 END) as null_count
FROM <table>

Step 4: Cardinality Analysis

For columns that look like categorical/dimension keys:

sql

SELECT
    column_name,
    COUNT(*) as frequency,
    ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER(), 2) as percentage
FROM <table>
GROUP BY column_name
ORDER BY frequency DESC
LIMIT 20

This reveals:

High-cardinality columns (likely IDs or unique values)
Low-cardinality columns (likely categories or status fields)
Skewed distributions (one value dominates)

Step 5: Sample Data

Get representative rows:

sql

SELECT *
FROM <table>
LIMIT 10

If the table is large and you want variety, sample from different time periods or categories.

Step 6: Data Quality Assessment

Summarize quality across dimensions:

Completeness

Which columns have NULLs? What percentage?
Are NULLs expected or problematic?

Uniqueness

Does the apparent primary key have duplicates?
Are there unexpected duplicate rows?

Freshness

When was data last updated? (MAX of timestamp columns)
Is the update frequency as expected?

Validity

Are there values outside expected ranges?
Are there invalid formats (dates, emails, etc.)?
Are there orphaned foreign keys?

Consistency

Do related columns make sense together?
Are there logical contradictions?

Step 7: Output Summary

Provide a structured profile:

Overview

2-3 sentences describing what this table contains, who uses it, and how fresh it is.

Schema

Column	Type	Nulls%	Distinct	Description
...	...	...	...	...

Key Statistics

Row count: X
Date range: Y to Z
Last updated: timestamp

Data Quality Score

Completeness: X/10
Uniqueness: X/10
Freshness: X/10
Overall: X/10

Potential Issues

List any data quality concerns discovered.

Recommended Queries

3-5 useful queries for common questions about this data.

Files

Just SKILL.md in skills/profiling-tables of astronomer/agents.

Open the folder on GitHubat commit 486ee63

Compare with similar skills

Profiling Tables 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.

Profiling Tables compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Profiling Tables this skillastronomer/agents	451	—	~964	Automated safety check: Pass	Apache-2.0	yesterday
Code EngineeropenJiuwen-ai/sciencediscovery	151	—	~2.8k	Automated safety check: Pass	Apache-2.0	yesterday
Data Analysisxiaoyuge886/aigc	198	1 repos	~794	Automated safety check: Pass	MIT	2 mo ago
Data Explorerliangdabiao/claude-data-analysis-ultra-main	290	—	~2.1k	Automated safety check: Pass	None	5 mo ago
Stat Edaasgard-ai-platform/skills	241	—	~954	Automated safety check: Pass	MIT	4 mo ago
Data Researchermajiayu000/claude-skill-registry	666	1 repos	~4.6k	Automated safety check: Pass	MIT	yesterday

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Questions about Profiling Tables

What does Profiling Tables do?

Deep-dive data profiling for a specific table. An agent skill from astronomer/agents. Profiling Tables is an agent skill from astronomer/agents. Deep-dive data profiling for a specific table.

When should I use Profiling Tables?

Profiling Tables fits situations like: the user asks to profile a table; wants statistics about a dataset; asks about data quality; needs to understand a tables structure and content.

How do I install Profiling Tables in Claude Code?

Run `npx skills add astronomer/agents --skill profiling-tables -a claude-code`. Or copy the skill folder (skills/profiling-tables in astronomer/agents) into .claude/skills/profiling-tables in your project. Claude Code loads it when a task matches its description.

How do I install Profiling Tables in Codex?

Run `npx skills add astronomer/agents --skill profiling-tables -a codex`. Or copy the skill folder (skills/profiling-tables in astronomer/agents) into .agents/skills/profiling-tables in your project. Codex loads it when a task matches its description.

Can I use Profiling Tables 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 astronomer/agents --skill profiling-tables -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/profiling-tables, .gemini/skills/profiling-tables, .github/skills/profiling-tables and .opencode/skills/profiling-tables in your project.

What does Profiling Tables need to run?

SKILL.md names no scripts, command-line tools or credentials: Profiling Tables is instructions for the agent only.

Does Profiling Tables 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 Profiling Tables 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 Profiling Tables use?

Profiling Tables is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Profiling Tables use?

About 964 tokens (SKILL.md is roughly 3.9k 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 Profiling Tables?

Skills that share tags, products or a category with Profiling Tables: Code Engineer (openJiuwen-ai/sciencediscovery, 151 stars), Data Analysis (xiaoyuge886/aigc, 198 stars), Data Explorer (liangdabiao/claude-data-analysis-ultra-main, 290 stars) and Stat Eda (asgard-ai-platform/skills, 241 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Profiling Tables?

astronomer (a GitHub organization) maintains it in astronomer/agents, which has 451 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on October 7, 2026.

Source: astronomer/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Profiling Tables

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Data Profile

Step 1: Basic Metadata

Step 2: Size and Shape

Step 3: Column-Level Statistics

Numeric Columns

String Columns

Date/Timestamp Columns

Step 4: Cardinality Analysis

Step 5: Sample Data

Step 6: Data Quality Assessment

Completeness

Uniqueness

Freshness

Validity

Consistency

Step 7: Output Summary

Overview

Schema

Key Statistics

Data Quality Score

Potential Issues

Recommended Queries

Files

Compare with similar skills

Similar skills

More from astronomer/agents

Related

Questions about Profiling Tables