How do I install the Dstack Compare skill?

Run npx skillmds@latest add upsolve-labs/dstack-compare 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 Compare 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 Compare?

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 Compare free to use?

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

Who published Dstack Compare?

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

Dstack Compare by upsolve-labs — Agent Skill

Name: Dstack Compare
Author: Upsolve-Labs

Dstack Compare

Period-over-period or cohort comparison. Use to understand what changed between two time periods, groups, or experiments. Breaks down the delta by key dimensions to find where the difference comes from. Trigger phrases: "last week vs prior week", "before vs after launch", "compare these two groups", "A/B results", "what changed?", "why is X different?".

Upsolve-Labs 5730059 3.0 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.

/compare

You are helping the user compare two periods, cohorts, or groups.

Before Starting

AskUserQuestion: "What are you comparing, and what metric? (e.g. 'last 7 days vs prior 7 days for revenue', 'users who saw feature A vs B on conversion rate', 'before Jan 15 vs after Jan 15 on DAU')"

Phase 1: Top-Level Delta

Open an Upsolve thread:

analyze_data("Compare <metric> between <period/group A> and <period/group B>. Show the value for each, the absolute change, and the percentage change.")

Phase 2: Dimension Breakdown

analyze_data("Break down the difference in <metric> between A and B by: [most relevant dimensions — geography, product, channel, device, user segment, etc.]. Rank dimensions by their contribution to the total delta.", thread_id=<id>)

Identify the 2–3 dimensions that explain the most of the gap.

Phase 3: Statistical Significance (A/B only)

If this is an experiment or A/B test, run:

analyze_data("Is the difference in <metric> between group A and group B statistically significant? Show sample sizes, means, and p-value if calculable.", thread_id=<id>)

Skip this phase for time-period comparisons.

Phase 4: Output Comparison Report

COMPARISON: <A> vs <B>
────────────────────────────────────────
Metric: <metric>

A (<label>):  <value>
B (<label>):  <value>
Δ:            <absolute change> (<pct>%)

TOP DRIVERS OF DIFFERENCE:
  1. <dimension>: <A value> vs <B value> — explains ~X% of delta
  2. <dimension>: <A value> vs <B value> — explains ~X% of delta
  3. <dimension>: <A value> vs <B value> — explains ~X% of delta

STATISTICAL NOTE:
  <significant at p<0.05 / not significant / not applicable>

INTERPRETATION:
  <2–3 sentence plain-language summary of what the data shows>

Rules

Always run Phase 1 before dimensional breakdown.
If the user specifies one metric, don't expand to others without asking.
Keep interpretation factual — report what the data shows, not what to do about it.
For time comparisons, ensure the periods are the same length before comparing.

---name: dstack-comparedescription: Period-over-period or cohort comparison. Use to understand what changed between two time periods, groups, or experiments. Breaks down the delta by key dimensions to find where the difference comes from. Trigger phrases: "last week vs prior week", "before vs after launch", "compare these two groups", "A/B results", "what changed?", "why is X different?".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.# /compareYou are helping the user compare two periods, cohorts, or groups.## Before StartingAskUserQuestion: "What are you comparing, and what metric? (e.g. 'last 7 days vs prior 7 days for revenue', 'users who saw feature A vs B on conversion rate', 'before Jan 15 vs after Jan 15 on DAU')"## Phase 1: Top-Level DeltaOpen an Upsolve thread:```analyze_data("Compare <metric> between <period/group A> and <period/group B>. Show the value for each, the absolute change, and the percentage change.")```## Phase 2: Dimension Breakdown```analyze_data("Break down the difference in <metric> between A and B by: [most relevant dimensions — geography, product, channel, device, user segment, etc.]. Rank dimensions by their contribution to the total delta.", thread_id=<id>)```Identify the 2–3 dimensions that explain the most of the gap.## Phase 3: Statistical Significance (A/B only)If this is an experiment or A/B test, run:```analyze_data("Is the difference in <metric> between group A and group B statistically significant? Show sample sizes, means, and p-value if calculable.", thread_id=<id>)```Skip this phase for time-period comparisons.## Phase 4: Output Comparison Report```COMPARISON: <A> vs <B>────────────────────────────────────────Metric: <metric>A (<label>): <value>B (<label>): <value>Δ: <absolute change> (<pct>%)TOP DRIVERS OF DIFFERENCE: 1. <dimension>: <A value> vs <B value> — explains ~X% of delta 2. <dimension>: <A value> vs <B value> — explains ~X% of delta 3. <dimension>: <A value> vs <B value> — explains ~X% of deltaSTATISTICAL NOTE: <significant at p<0.05 / not significant / not applicable>INTERPRETATION: <2–3 sentence plain-language summary of what the data shows>```## Rules- Always run Phase 1 before dimensional breakdown.- If the user specifies one metric, don't expand to others without asking.- Keep interpretation factual — report what the data shows, not what to do about it.- For time comparisons, ensure the periods are the same length before comparing.

Frequently asked questions