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Learning memory

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

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

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

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 4 commands, including `run `find-your-level`` and 3 more.

SKILL.md

3.2 KB, 667 tokens by cl100k_base, 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)

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

Session End (Save)

  1. Extract — Record: concepts covered, understanding outcomes, new weak areas, self-reported confidence.
  2. Update — Merge into profile. Promote "in-progress" → "covered" when confirmed. Add new weak areas.
  3. 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.

What ships with it: 2 files

2.4 KB alongside SKILL.md

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.