explainable-actions

name	explainable-actions
description	Use when designing explanations for Dataverse AI actions, including why an action is recommended, what data was used, which rules applied, dependencies, and expected impact. Produces explanation guidance only.

Explainable Actions

Purpose

Design explanations that make every AI-planned Dataverse action traceable and understandable.

Use this skill when a user needs to explain:

• why an action is recommended
• which data influenced the action
• which rules or policies apply
• which dependencies or records are affected
• what impact the action will have
• what was done after approval

V1 Boundary

This skill creates explanation structures and review guidance only. It does not inspect or log live action execution.

Workflow

1. Identify the action, affected records, decision rules, source data, and expected business impact.
2. Separate user-friendly explanation from technical trace details.
3. Include permission, policy, dependency, and approval context.
4. Define before-action and after-action explanation requirements.
5. Define audit storage guidance: summary, evidence, actor, timestamp, selected records, decision rationale, and approval trail.

Output Format

Return:

• User explanation: concise business-language rationale.
• Technical trace: data, rules, dependencies, and checks.
• Impact statement: records, processes, and users affected.
• Audit payload: fields that should be stored.
• Questions or gaps: missing evidence before the action can be trusted.