How do I install the Dstack Profile skill?

Run npx skillmds@latest add upsolve-labs/dstack-profile in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Dstack Profile safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Dstack Profile?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Dstack Profile free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Dstack Profile?

Upsolve-Labs (@upsolve-labs) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Dstack Profile by upsolve-labs — Agent Skill

Name: Dstack Profile
Author: Upsolve-Labs

Dstack Profile

Data quality audit for a table or dataset. Checks null rates, cardinality, duplicates, value distributions, date ranges, and referential integrity. Outputs a quality scorecard with OK/WARN/FAIL ratings per dimension. Run before building models, pipelines, or reports on top of a dataset. Trigger phrases: "audit this table", "check data quality", "profile this dataset", "how clean is this data?", "is this table safe to use?".

Upsolve-Labs 5b1bce1 2.9 KB Updated Sep 17, 2026

File contents

Update Check (run first)

_UPD=$(~/.claude/skills/data-stack/bin/data-stack-update-check 2>/dev/null || .claude/skills/data-stack/bin/data-stack-update-check 2>/dev/null || true)
[ -n "$_UPD" ] && echo "$_UPD" || true

If output shows UPGRADE_AVAILABLE <old> <new>: read ~/.claude/skills/data-stack/skills/dstack-upgrade/SKILL.md and follow the "Inline upgrade flow". If JUST_UPGRADED <from> <to>: tell user "Running data-stack v{to} (just updated!)" and continue.

/profile

You are running a data quality audit on a specific table or dataset.

Before Starting

If the user hasn't specified a table, AskUserQuestion: "Which table or dataset do you want to profile?"

Phase 1: Schema & Volume

Open an Upsolve thread:

analyze_data("Profile the table <table_name>: show total row count, column names and data types, and a sample of 5 rows.")

Phase 2: Null & Completeness Check

analyze_data("For each column in <table_name>, show: null count, null percentage, and distinct value count.", thread_id=<id>)

Flag: >5% nulls → WARN, >20% nulls → FAIL.

Phase 3: Duplicates

analyze_data("Check for duplicate rows in <table_name>. Show exact duplicate count and, if there is a primary key column, show the duplicate key count.", thread_id=<id>)

Phase 4: Distribution & Outliers

analyze_data("For numeric columns in <table_name>: show min, max, mean, median, and values >3 standard deviations from the mean. For date columns: show min date, max date, and flag any gaps greater than 7 days.", thread_id=<id>)

Phase 5: Output Scorecard

DATA QUALITY REPORT: <table_name>
As of: <date>
────────────────────────────────────────
Volume:    X,XXX rows    [OK / WARN / FAIL]
Nulls:     X cols >5%    [OK / WARN / FAIL]
Dupes:     X duplicates  [OK / WARN / FAIL]
Ranges:    <summary>     [OK / WARN / FAIL]

COLUMN DETAILS:
  col_name   type    nulls%   distinct   notes
  ...

ISSUES FOUND:
  FAIL  <specific issue>
  WARN  <specific issue>

RECOMMENDATION:
  <1–2 sentences on whether this data is safe to use and for what purpose>

Thresholds: OK = no issues, WARN = minor concern, FAIL = data is unreliable for this dimension.

Rules

Run all 4 phases before outputting the scorecard — never skip.
If a table has >100 columns, profile the first 50 plus any columns the user calls out.
Report what the data shows. Don't speculate about what it should mean.

---name: dstack-profiledescription: Data quality audit for a table or dataset. Checks null rates, cardinality, duplicates, value distributions, date ranges, and referential integrity. Outputs a quality scorecard with OK/WARN/FAIL ratings per dimension. Run before building models, pipelines, or reports on top of a dataset. Trigger phrases: "audit this table", "check data quality", "profile this dataset", "how clean is this data?", "is this table safe to use?".license: MIT---## Update Check (run first)```bash_UPD=$(~/.claude/skills/data-stack/bin/data-stack-update-check 2>/dev/null || .claude/skills/data-stack/bin/data-stack-update-check 2>/dev/null || true)[ -n "$_UPD" ] && echo "$_UPD" || true```If output shows `UPGRADE_AVAILABLE <old> <new>`: read `~/.claude/skills/data-stack/skills/dstack-upgrade/SKILL.md` and follow the "Inline upgrade flow". If `JUST_UPGRADED <from> <to>`: tell user "Running data-stack v{to} (just updated!)" and continue.# /profileYou are running a data quality audit on a specific table or dataset.## Before StartingIf the user hasn't specified a table, AskUserQuestion: "Which table or dataset do you want to profile?"## Phase 1: Schema & VolumeOpen an Upsolve thread:```analyze_data("Profile the table <table_name>: show total row count, column names and data types, and a sample of 5 rows.")```## Phase 2: Null & Completeness Check```analyze_data("For each column in <table_name>, show: null count, null percentage, and distinct value count.", thread_id=<id>)```Flag: >5% nulls → WARN, >20% nulls → FAIL.## Phase 3: Duplicates```analyze_data("Check for duplicate rows in <table_name>. Show exact duplicate count and, if there is a primary key column, show the duplicate key count.", thread_id=<id>)```## Phase 4: Distribution & Outliers```analyze_data("For numeric columns in <table_name>: show min, max, mean, median, and values >3 standard deviations from the mean. For date columns: show min date, max date, and flag any gaps greater than 7 days.", thread_id=<id>)```## Phase 5: Output Scorecard```DATA QUALITY REPORT: <table_name>As of: <date>────────────────────────────────────────Volume: X,XXX rows [OK / WARN / FAIL]Nulls: X cols >5% [OK / WARN / FAIL]Dupes: X duplicates [OK / WARN / FAIL]Ranges: <summary> [OK / WARN / FAIL]COLUMN DETAILS: col_name type nulls% distinct notes ...ISSUES FOUND: FAIL <specific issue> WARN <specific issue>RECOMMENDATION: <1–2 sentences on whether this data is safe to use and for what purpose>```Thresholds: OK = no issues, WARN = minor concern, FAIL = data is unreliable for this dimension.## Rules- Run all 4 phases before outputting the scorecard — never skip.- If a table has >100 columns, profile the first 50 plus any columns the user calls out.- Report what the data shows. Don't speculate about what it should mean.

Frequently asked questions