Evo Memory

This is the persistent learning layer for research workflows. It maintains two memory stores: one for research directions (what's promising vs. what's been tried and failed), another for technical strategies (data processing, training tricks, architecture patterns that actually worked). Three evolution mechanisms update these stores after ideation cycles and experiment runs, classifying failures as implementation bugs versus fundamental dead ends. The paper implementation uses embedding-based retrieval to pull relevant knowledge into new cycles. Use it when you finish an ideation tournament, complete an experiment pipeline, or need to check what you learned last time before starting fresh. It's meta-learning infrastructure, not experiment execution.

skills.sh installs: 393
GitHub stars: 488
last updated: Aug 21, 2026

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Install

$ npx -y skills add evoscientist/evoskills --skill evo-memory --agent claude-code

Installs into .claude/skills of the current project.

Files

Evo-Memory

A persistent learning layer that accumulates research knowledge across ideation and experimentation cycles. Maintains two memory stores and implements three evolution mechanisms that feed learned patterns back into future research.

When to Use This Skill

User has completed an research-ideation and needs to update Ideation Memory
User has completed (or failed) an experiment-pipeline and needs to update memory
User is starting a new research cycle and wants to load prior knowledge
User asks about research memory, learned patterns, or cross-cycle knowledge
User mentions "evo-memory", "update memory", "what worked before", "research history", "evolution"

The Learning Layer

Research is iterative. Each cycle — from ideation through experimentation — generates knowledge that should inform the next cycle. Without persistent memory, every new project starts from scratch, repeating mistakes and rediscovering patterns.

Evo-memory solves this by maintaining two structured memory stores and three evolution mechanisms that extract, classify, and inject knowledge across cycles.