mentor

Adjust explanation depth based on user-reported familiarity with active workflows.

Updated Jul 21, 2026

Search Tags:#guidance #learning #explanation #support #teaching #overlay

Ready to run

npx skills add https://github.com/roborew/opencode --skill mentor-roborew

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mentor
Source: https://github.com/roborew/opencode/tree/main/skills/mentor
Command: npx skills add https://github.com/roborew/opencode --skill mentor-roborew

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides an optional layer of educational support, helping users understand complex topics or processes by offering explanations tailored to their familiarity level.

Core Features & Use Cases

Adaptive Explanations: Adjusts the depth of information based on user-rated familiarity (0-5).
Layered Learning: Provides summaries, step-by-step guides, analogies, and further reading.
Interactive Learning: Includes teach-back prompts and optional mini-quizzes to reinforce understanding.
Use Case: When working with a new coding framework, a user can activate the mentor Skill to get a clear, step-by-step explanation of core concepts, starting with a high-level overview and drilling down into specifics as needed.

Quick Start

Activate the mentor skill and ask it to explain the current task.

Frequently Asked Questions about mentor

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

FAQPage Schema

How do I get adaptive explanations for complex workflows?▼

Adaptive explanations adjust depth based on your familiarity rating from 0 to 5, providing summaries, walkthroughs, and analogies to match your current understanding level.

Can I learn a new coding framework step-by-step while working?▼

Yes, you can learn a coding framework step-by-step by activating guidance during your active workflow to get high-level overviews and drill down into specifics as needed.

How does interactive learning reinforcement work?▼

Interactive learning reinforcement uses teach-back prompts and optional mini-quizzes to test your retention and solidify your understanding of the explained concepts.

What is the best way to tailor teaching depth to my current knowledge?▼

Tailoring teaching depth requires reporting your familiarity level; the system then calibrates its output between brief summaries and optional further reading accordingly.

Does this guidance tool modify my active phase constraints?▼

No, it operates as a read-only overlay, providing educational support and layered learning without modifying or interfering with your active phase constraints.

What are the limitations of using an educational overlay for workflow support?▼

An educational overlay is limited to read-only support, meaning it cannot execute workflow tasks or make changes, functioning solely as a supplementary teaching layer.

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