engram
Evidence-based learning engine — first-principles curricula, free-recall verification with receipts, FSRS-scheduled memory, and explorable artifacts. Learn anything; keep it.
- 1.5k estrellas
- 107 forks
- 4 issues abiertos
- Lenguaje: Python
- Licencia: MIT
- actualizado
Instalación
/plugin marketplace add nagisanzenin/engram
/plugin install engram- Inside Claude Code, add the marketplace and install the plugin with the commands above.
- Follow any post-install configuration from the README.
- Restart the session if commands or hooks do not show up immediately.
6 items en este repositorio
Learning telemetry, strategy, and schedule — retention stats, calibration, grader audit, n-of-1 experiments, HTML dashboard. Use for "how am I doing", weekly check-ins, strategy questions, auditing the grader, or adjusting how Engram teaches.
Builds interactive HTML explorables for Engram threshold concepts under the binding Explorable Contract. Use after encoding a threshold node, or to re-encode a repeatedly-lapsing node visually.
Independent grader of learner productions for the Engram learning plugin. MUST BE USED for /learn verification and /review audits. Deliberately blind to the tutoring dialogue — receives only items and rubrics, returns receipt JSON.
Decomposes any topic into a first-principles concept DAG for the Engram learning plugin. Use when starting a new learning topic or restructuring one. Returns strict JSON for `engram.py add-topic`.
Learn any topic properly — first-principles curriculum, generation-first tutoring, verified free recall, FSRS scheduling. Use when the user wants to learn, understand, study, or continue studying something.
Clear due memory reviews with free recall — the two-minute habit that makes learning permanent. Use when reviews are due, or the user wants to review, practice, or "do my engram reviews".
Resumen de Plugins
Lo que la gente pregunta sobre engram
¿Qué es nagisanzenin/engram?
nagisanzenin/engram es plugins para el ecosistema de Claude AI. Evidence-based learning engine — first-principles curricula, free-recall verification with receipts, FSRS-scheduled memory, and explorable artifacts. Learn anything; keep it. Tiene 1.5k estrellas en GitHub y su última actualización registrada es del 2026-08-27.
¿Cómo se instala engram?
Puedes instalar engram clonando el repositorio (https://github.com/nagisanzenin/engram) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar nagisanzenin/engram?
Nuestro agente de seguridad ha analizado nagisanzenin/engram y le ha asignado un Trust Score de 92/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene nagisanzenin/engram?
nagisanzenin/engram es mantenido por nagisanzenin. La última actividad registrada en GitHub es del 2026-08-27, con 4 issues abiertos.
¿Hay alternativas a engram?
Sí. En ClaudeWave puedes explorar plugins similares en /categories/plugins, ordenados por popularidad o actividad reciente.