Root Deepener — Identity & Mastery Skill

Name: Root Deepener — Identity & Mastery Skill
Availability: InStock
Author: nsuberi

Convert user background and mastery evidence into persistent identity roots.

Updated Jul 5, 2025

Search Tags:#onboarding #identity #evidence gating #personalized tutoring #graph connections #learning mastery #root inference

Ready to run

npx skills add https://github.com/nsuberi/ai-prototype-hub --skill root-deepener-identity-mastery-skill

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Root Deepener — Identity & Mastery Skill
Source: https://github.com/nsuberi/ai-prototype-hub/tree/main/prototypes/research-workspace/vault-seed/.claude/skills/root-deepener
Command: npx skills add https://github.com/nsuberi/ai-prototype-hub --skill root-deepener-identity-mastery-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns a user's background and learning signals into persistent “roots” so their identity and mastery can guide future interactions.

Core Features & Use Cases

Onboarding root inference: Asks warm, open questions, then infers roots with stated vs inferred sourcing and writes them into .tree.json.
Mastery-driven internalization: Detects mastery evidence in .root-signals.jsonl, proposes internalization, and—upon confirmation—updates branch status and creates new rooted connections.
Evidence-gated decisions: Requires multiple signals across conversations before internalizing a branch to reduce overreach.

Quick Start

Ask the assistant to run Root Deepener to infer or internalize user roots based on the first conversation or accumulated mastery signals.

Frequently Asked Questions about Root Deepener — Identity & Mastery Skill

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

FAQPage Schema

What is persistent user identity personalization in conversational learning?▼

Persistent user identity personalization converts background and learning signals into stable roots, enabling long-term guidance of future interactions. It applies evidence gating to validate mastery signals before internalizing branches into a persistent tree structure.

How do I create persistent user roots from onboarding conversations?▼

To create persistent user roots, ask open questions during onboarding, then infer roots with stated versus inferred sourcing. The system writes these validated identity roots directly into a .tree.json file for long-term personalization.

How does evidence gating work for learning mastery internalization?▼

Evidence gating requires multiple mastery signals across conversations before internalizing a branch. This validation reduces overreach by ensuring accumulated evidence in .root-signals.jsonl meets specific thresholds before creating new rooted connections.

When should I use mastery-driven internalization for personalized tutoring?▼

Use mastery-driven internalization during delegated internalization when a branch shows accumulated mastery signals. Upon user confirmation of the proposed internalization, the system updates branch status and creates new rooted graph connections.

Does this personalized tutoring approach work without external dependencies?▼

Yes, this approach works without external dependencies. It relies entirely on internal data structures, updating .tree.json and .root-signals.jsonl to track graph connections and validate evidence thresholds for learning mastery.