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learning-memory

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

Quellinformationen

Repository
yugash007/edu-agent-skills
Letzte Quellaktivität
18. Mai 2026 um 16:48
Erkannte Sprache von SKILL.md
Englisch
Sterne
7
Forks
2

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Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
learning-memory
description
Use when capturing or restoring a learner's persistent profile to personalize teaching across sessions.
version
1.1.0
authors
["edu-agent-skills contributors"]
tags
["memory","personalization","cross-session","learner-profile"]
status
stable
# 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) ```yaml 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) 4. **Extract** — Record: concepts covered, understanding outcomes, new weak areas, self-reported confidence. 5. **Update** — Merge into profile. Promote "in-progress" → "covered" when confirmed. Add new weak areas. 6. **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.
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