How do I install the Dstack Explore skill?

Run npx skillmds@latest add upsolve-labs/dstack-explore 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 Explore 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 Explore?

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 Explore free to use?

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

Who published Dstack Explore?

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

Dstack Explore by upsolve-labs — Agent Skill

Name: Dstack Explore
Author: Upsolve-Labs

Dstack Explore

Dataset discovery and schema mapping. Use when starting fresh with data, exploring what's available, or mapping a new data source. Opens an Upsolve thread, surfaces available tables and schemas, profiles row counts and key columns, and outputs a structured data map. Strongly recommended as the first step before any analysis. Trigger phrases: "what data do I have?", "show me my tables", "explore this dataset", "what's available?", "I'm new to this data".

Upsolve-Labs a2b4bf2 2.7 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.

/explore

You are helping the user discover and map their data.

Phase 1: Start Thread & Discover Sources

Open an Upsolve thread:

analyze_data("What data sources and tables do I have access to? List all available tables with a brief description of each.")

Save the thread_id — reuse it for all follow-up queries in this session.

Phase 2: Profile Key Tables

For all tables surfaced (up to 10), run in the same thread:

analyze_data("For each table, show me: row count, column names and types, date range if a timestamp column exists, and a sample of 3 rows.", thread_id=<saved_id>)

Phase 3: Freshness Check

analyze_data("Which tables have been updated most recently? Show the latest record timestamp for each.", thread_id=<saved_id>)

Phase 4: Output Data Map

Present a structured summary:

DATA MAP
────────────────────────────────────────
Source: <data source name>
As of:  <current date>

Tables:
  <table_name>
    rows:     ~X,XXX
    columns:  col1 (type), col2 (type), ...
    updated:  <most recent date>
    notes:    <anything notable, e.g. sparse columns, wide cardinality>

  ...

Recommended next steps:
  - Run /profile on <table> before building on top of it
  - Run /investigate if anything looks unexpected
  - Run /metric-brief for a quick KPI summary

Rules

Always reuse the same thread_id throughout the session.
If Upsolve returns an error (no project, no connection), tell the user: "Upsolve MCP is not connected or has no project set up. Run /setup to configure it."
Never invent table names. Only surface what Upsolve returns.
Keep the data map concise — one line per column maximum.

---name: dstack-exploredescription: Dataset discovery and schema mapping. Use when starting fresh with data, exploring what's available, or mapping a new data source. Opens an Upsolve thread, surfaces available tables and schemas, profiles row counts and key columns, and outputs a structured data map. Strongly recommended as the first step before any analysis. Trigger phrases: "what data do I have?", "show me my tables", "explore this dataset", "what's available?", "I'm new to this data".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.# /exploreYou are helping the user discover and map their data.## Phase 1: Start Thread & Discover SourcesOpen an Upsolve thread:```analyze_data("What data sources and tables do I have access to? List all available tables with a brief description of each.")```Save the `thread_id` — reuse it for all follow-up queries in this session.## Phase 2: Profile Key TablesFor all tables surfaced (up to 10), run in the same thread:```analyze_data("For each table, show me: row count, column names and types, date range if a timestamp column exists, and a sample of 3 rows.", thread_id=<saved_id>)```## Phase 3: Freshness Check```analyze_data("Which tables have been updated most recently? Show the latest record timestamp for each.", thread_id=<saved_id>)```## Phase 4: Output Data MapPresent a structured summary:```DATA MAP────────────────────────────────────────Source: <data source name>As of: <current date>Tables: <table_name> rows: ~X,XXX columns: col1 (type), col2 (type), ... updated: <most recent date> notes: <anything notable, e.g. sparse columns, wide cardinality> ...Recommended next steps: - Run /profile on <table> before building on top of it - Run /investigate if anything looks unexpected - Run /metric-brief for a quick KPI summary```## Rules- Always reuse the same `thread_id` throughout the session.- If Upsolve returns an error (no project, no connection), tell the user: "Upsolve MCP is not connected or has no project set up. Run /setup to configure it."- Never invent table names. Only surface what Upsolve returns.- Keep the data map concise — one line per column maximum.

Frequently asked questions