| 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
- Identify the action, affected records, decision rules, source data, and expected business impact.
- Separate user-friendly explanation from technical trace details.
- Include permission, policy, dependency, and approval context.
- Define before-action and after-action explanation requirements.
- 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.
