Learning memory

Skill yugash007/edu-agent-skills/skills/memory/learning-memory

Reusable educational skills for AI coding agents. Turn agents (Gemini CLI, Claude Code, Cursor, etc.) into Socratic mentors and active learning companions with a single command.

Install

npx -y skills add yugash007/edu-agent-skills --skill learning-memory

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Use when capturing or restoring a learner's persistent profile to personalize teaching across sessions.

SKILL.md

3.2 KB, as published. Nobody here has run it

Purpose

Maintain a structured learner profile across sessions so agents start at the right depth, avoid re-teaching covered material, and prioritize known weak areas.

Activation

New session begins and prior context may exist. Learner references prior sessions or covered topics. Session ends and state should be preserved. Agent needs to personalize without re-asking onboarding questions.
Skip if: one-off session with no continuity desired, no persistent storage available, or narrow stateless task.
Routing: run at session start (restore) and end (save). Feed weak areas to weak-area-tracker. Profile drives find-your-level for returning learners with uncertain level.

Inputs

Prior session summary/profile, current session transcript, concepts + outcomes, learner self-reports, error patterns from assessment skills.

Profile Schema (compact)

learner_profile:
  level: beginner | intermediate | advanced
  stated_goal: "<goal>"
  learning_style: code-first | concept-first | mixed
  weak_areas: [{topic, type, last_seen, correction_attempted}]
  covered_topics: [{topic, confidence: low|medium|high, last_confirmed}]
  active_checkpoint: "<last concept/milestone in progress>"
  session_count: N
  last_session: "<ISO date>"

Workflow

Session Start (Restore)

Retrieve — Check for prior profile. If exists: summarize and confirm accuracy with learner. If none: run find-your-level.
Staleness Check — Last session 2+ weeks ago → flag for light review. Goal changed → update target.
Inject — Feed relevant profile data (level, weak areas, last checkpoint) into current session. Don't re-explain confirmed material unless requested.

Session End (Save)

Extract — Record: concepts covered, understanding outcomes, new weak areas, self-reported confidence.
Update — Merge into profile. Promote "in-progress" → "covered" when confirmed. Add new weak areas.
Handoff — Output compact summary: where to resume, what to skip, top 1–2 priorities for next session.

Rules

DO: confirm profile accuracy with learner at session start — never assume stale data is current.
DO: base profile updates on observable evidence, not assumptions.
DO: cap weak-area list at 5 active items.
DO: frame all profile data as "what we'll focus on" not "what went wrong."
DON'T: re-teach confirmed topics without request or regression evidence.
DON'T: skip the session-end save — always output the handoff note.
DON'T: treat "covered" as "fully mastered" — watch for regression signals.

Output

Session start: restored profile summary + confirmation prompt. Session end: concepts covered with outcomes, new/resolved weak areas, and handoff note (resume point + priorities + skip list). Format naturally.

Checklist

Profile confirmed with learner at session start.
Staleness check performed if 2+ week gap.
Session outcomes logged with evidence.
Handoff note produced at session end.