engram
Reduces knowledge retention risk by embedding spaced repetition and verification into Claude-assisted learning workflows, ensuring teams internalize rather t…
Evidence-based learning engine for Claude Code — first-principles curricula, free-recall verification with receipts, FSRS-scheduled memory, and explorable artifacts. Learn anything; keep it.
- Ask Claude to create a structured learning path for a new programming language with spaced repetition schedules.
- Generate verification quizzes that test recall without looking at previous lesson materials or notes.
- Automate skill tracking across your team by logging completed lessons and monitoring knowledge retention over time.
Reduces knowledge retention risk by embedding spaced repetition and verification into Claude-assisted learning workflows, ensuring teams internalize rather than repeatedly re-query the same concepts.
Engineering teams building institutional knowledge and onboarding processes that require measurable skill retention and recall verification.
https://github.com/nagisanzenin/engram
By nagisanzenin
How to Get It
claude plugins install nagisanzenin/engram
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Create a structured learning path for a new programming language with spaced repetition schedules
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 506 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 506 GitHub stars; contributors unknown; last commit 0d ago; license MIT.