Can it handle complex assessment hierarchies?

Yes, it includes patterns for mapping questions to parent quizzes and grouping activities within larger course or program structures.

Which xAPI components does it cover?

The skill covers the core xAPI components: Actor, Verb, Object, Result, and Context, providing specific data source identification for each.

Does this skill help with xAPI validation?

It provides a validation checklist to verify actor uniqueness, verb appropriateness, object completeness, and ISO 8601 timestamp formatting.

What is the main purpose of the xAPI Prompt Engineering skill?

It helps users analyze assessment scenarios and identify the specific metadata needed to generate valid xAPI statements for tracking learner activities.

xAPI Prompt Engineer for Assessments

Name: xAPI Prompt Engineer for Assessments
Author: tim80411

bytim80411

0•

학습 및 문서화

Analyzes and structures AI prompts to generate valid xAPI statements for educational assessment tracking and learning analytics.

The xAPI Prompt Engineering skill provides a specialized framework for developers and instructional designers to translate educational scenarios into technical xAPI (Experience API) data. It guides users through a rigorous analysis process to identify necessary materials—such as actor identifiers, activity metadata, and scoring results—ensuring that generated AI prompts result in valid, LRS-compatible statements. By mapping complex learning interactions to standardized verbs and context hierarchies, this skill bridges the gap between pedagogical intent and technical data implementation.

주요 기능

010 GitHub stars

02Hierarchical context definitions for nested activities like questions within quizzes

03Standardized patterns for multi-language support and attempt tracking

04Comprehensive material collection checklists for runtime and static data

05Decision trees for automated xAPI verb selection based on activity type

06Context material mapping for Actor, Verb, Object, and Result components

사용 사례

01Mapping certification pass/fail criteria to standardized xAPI statement structures

02Designing prompts to track student interactions and quiz results in an LMS

03Optimizing AI-generated assessment data for seamless Learning Record Store (LRS) integration

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tim80411/ai-agent-extension prompt-engineering

For use in Claude.ai and ChatGPT

주요 기능

010 GitHub stars

02Hierarchical context definitions for nested activities like questions within quizzes

03Standardized patterns for multi-language support and attempt tracking

04Comprehensive material collection checklists for runtime and static data

05Decision trees for automated xAPI verb selection based on activity type

06Context material mapping for Actor, Verb, Object, and Result components

사용 사례

01Mapping certification pass/fail criteria to standardized xAPI statement structures

02Designing prompts to track student interactions and quiz results in an LMS

03Optimizing AI-generated assessment data for seamless Learning Record Store (LRS) integration