bsa-persona-enricher

Map canonical AI persona stubs to 19-section governance-ready definition templates.

3|Updated Mar 29, 2026

Search Tags:#template #ai #compliance #governance #persona #model #enrichment

Ready to run

npx skills add https://github.com/SteveGJones/jack-tar-deckhand --skill bsa-persona-enricher

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bsa-persona-enricher
Source: https://github.com/SteveGJones/jack-tar-deckhand/tree/main/.claude/skills/bsa-persona-enricher
Command: npx skills add https://github.com/SteveGJones/jack-tar-deckhand --skill bsa-persona-enricher

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bridges the gap between lightweight AI Persona stubs captured during service discovery and the comprehensive governance-ready definitions required for deployment and governance.

Core Features & Use Cases

Pre-population: Auto-populates the 19-section AI Persona Definition Template from canonical-model data.
Mapping to governance schema: Maps canonical model fields to template sections to ensure governance-aligned persona docs.
Batch enrichment: Processes multiple personas from a single canonical model for scalable governance.
Manual input guidance: Flags sections requiring human input and presents recommended prompts.

Quick Start

Load the canonical model JSON and run the bsa-persona-enricher to generate 19-section enriched persona definitions.

Frequently Asked Questions about bsa-persona-enricher

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert AI persona stubs into governance-ready templates?▼

To convert AI persona stubs into governance-ready templates, load the canonical model JSON into the enricher to auto-populate a 19-section AI Persona Definition Template. This process maps canonical fields directly to the governance schema for immediate deployment readiness.

What is AI persona enrichment for compliance governance?▼

AI persona enrichment for compliance governance is the process of mapping lightweight service discovery stubs to comprehensive definition templates. It bridges the gap between initial persona capture and the full governance documentation required for compliant AI deployment.

Can I process multiple AI personas from a single canonical model?▼

Yes, you can process multiple AI personas from a single canonical model. The enricher supports batch workflows, allowing scalable generation of 19-section governance definitions for all personas identified during service discovery.

How do I identify which governance template sections need manual input?▼

To identify governance template sections requiring manual input, the enricher automatically flags incomplete fields after pre-populating data from the canonical model. It presents recommended prompts to guide users through completing the remaining sections.

What do I need to generate AI persona definition templates?▼

To generate AI persona definition templates, you need a canonical model JSON file containing the AI persona stubs captured during service discovery. No additional dependencies or environment setup are required to run the enrichment workflow.

Are there limitations when mapping canonical models to governance schemas?▼

The primary limitation when mapping canonical models to governance schemas is that automated pre-population only covers fields present in the source data. Sections requiring contextual governance decisions are flagged for manual input and cannot be fully automated.