How do I install the Relewise Compare Datasets skill?

Run npx skillmds@latest add relewise/relewise-compare-datasets 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 Relewise Compare Datasets 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 Relewise Compare Datasets?

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 Relewise Compare Datasets free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Relewise Compare Datasets?

Relewise (@relewise) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Relewise Compare Datasets by relewise

Name: Relewise Compare Datasets
Author: Relewise

Relewise Compare Datasets

Compare two or more Relewise Datasets using consistent metadata, analytics, or consumption measures. Use when a user asks to compare stores, markets, environments, or other Dataset-backed properties.

Relewise 422b584 2 files · 2.8 KB Updated Sep 17, 2026

File contents

Compare Relewise Datasets

Before any Agent Gateway call, activate and follow the installed relewise-agent-gateway skill from this plugin. Pass it the selected REST operation ID and/or MCP tool plus validated parameters; do not resolve the CLI, choose a transport, or handle authentication in this domain skill. Never invent Dataset IDs. This workflow is read-only.

Establish a fair comparison

Discover accessible Datasets and resolve every requested name independently. If a name is ambiguous, ask the user to choose from concise candidates.
Validate each selected Dataset and its effective Agent Gateway policy. Explain and omit a Dataset when the required area is unavailable; do not silently replace it.
Identify the smallest common set of measures that answers the question. Use Dataset metadata for configuration context, analytics for performance, and consumption summaries for usage.
Keep inputs equivalent across Datasets: the same inclusive period, currency, filters, pagination, and measurement definition. Surface unavoidable differences before interpreting results.
Inspect the selected transport's schema for each unfamiliar capability, then execute it once per Dataset. With the CLI, use schema and call.

Do not compare formatted strings. Calculate from numeric values and preserve units. Do not infer that a configuration difference caused a performance difference without supporting evidence.

Present the result

Lead with the material similarities and differences, followed by a compact Dataset-by-Dataset comparison. State the period, filters, units, and missing or incomparable data. Use display names by default and IDs only for disambiguation. Separate observed facts from interpretations and suggested follow-up analysis.

---name: relewise-compare-datasetsdescription: Compare two or more Relewise Datasets using consistent metadata, analytics, or consumption measures. Use when a user asks to compare stores, markets, environments, or other Dataset-backed properties.---# Compare Relewise DatasetsBefore any Agent Gateway call, activate and follow the installed `relewise-agent-gateway` skill from this plugin. Pass it the selected REST operation ID and/or MCP tool plus validated parameters; do not resolve the CLI, choose a transport, or handle authentication in this domain skill. Never invent Dataset IDs. This workflow is read-only.## Establish a fair comparison1. Discover accessible Datasets and resolve every requested name independently. If a name is ambiguous, ask the user to choose from concise candidates.2. Validate each selected Dataset and its effective Agent Gateway policy. Explain and omit a Dataset when the required area is unavailable; do not silently replace it.3. Identify the smallest common set of measures that answers the question. Use Dataset metadata for configuration context, analytics for performance, and consumption summaries for usage.4. Keep inputs equivalent across Datasets: the same inclusive period, currency, filters, pagination, and measurement definition. Surface unavoidable differences before interpreting results.5. Inspect the selected transport's schema for each unfamiliar capability, then execute it once per Dataset. With the CLI, use `schema` and `call`.Do not compare formatted strings. Calculate from numeric values and preserve units. Do not infer that a configuration difference caused a performance difference without supporting evidence.## Present the resultLead with the material similarities and differences, followed by a compact Dataset-by-Dataset comparison. State the period, filters, units, and missing or incomparable data. Use display names by default and IDs only for disambiguation. Separate observed facts from interpretations and suggested follow-up analysis.

Frequently asked questions