adk-memory-self-improvement

SKILLWorkflowcommunauté
v0.0.0Folken2MITMis à jour il y a 14 jSource →

EXPERIMENTAL, opt-in memory self-improvement for long-lived ADK agents — relevance-conditioned preload, the periodic consolidation "dream" pass, the after-turn judge fork, fire-and-forget sibling runs, per-session throttling, and the skill curator that proposes SKILL.md files. Read before enabling a

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il y a 14 jDernière mise à jour
Skill
AuteurFolken2
Version0.0.0
LicenceMIT
CatégorieWorkflow
Formatsskill.md
PromptNon publié
Compatibilité
Claude✓ Pris en charge
Cursor
Copilot
ChatGPT
Gemini
À propos

EXPERIMENTAL, opt-in memory self-improvement for long-lived ADK agents — relevance-conditioned preload, the periodic consolidation "dream" pass, the after-turn judge fork, fire-and-forget sibling runs, per-session throttling, and the skill curator that proposes SKILL.md files. Read before enabling any NUVEL_MEMORY_* or NUVEL_SKILL* flag, when budgeting the extra LLM calls these add per turn, or wh

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