/evo-memory

name: evo-memory
description: "Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after research-ideation), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing research-ideation cycles or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use research-ideation)."
allowed-tools: "write_file edit_file read_file think_tool"
metadata:
  author: EvoScientist
  version: '1.0.0'
  tags: [core, meta-learning]

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.

Two Memory Stores

Ideation Memory (M_I)

**Location**: `/memory/ideation-memory.md`

Records what you've learned about research DIRECTIONS — which areas are promising and which are dead ends.

**Two sections**:

| Section | What It Contains | Example Entry | |---------|-----------------|---------------| | Feasible Directions | Directions that showed promise in prior cycles | "Contrastive learning for few-shot classification — confirmed feasible, top-3 in tournament cycle 2" | | Unsuccessful Directions | Directions that were tried and failed, with failure classification | "Autoregressive generation for real-time video — fundamental failure: latency constraint incompatible with autoregressive decoding" |

**Each entry records**: Direction name, one-sentence summary, evidence (which cycle, what results), classification (feasible / implementation failure / fundamental failure), date.

**How it's used**: `research-ideation` reads M_I at the start of Step 0. The paper uses embedding-based retrieval with cosine similarity, selecting the top-k_I most similar items (k_I=2 in experiments). Feasible directions from prior cycles can seed new tree branches. Unsuccessful directions are used during pruning — fundamental failures are pruned; implementation failures may be retried.

See [assets/ideation-memory-template.md](assets/ideation-memory-template.md) for the template.

Experimentation Memory (M_E)

**Location**: `/memory/experiment-memory.md`

Records what you've learned about research STRATEGIES — which technical approaches and configurations work in practice.

The paper defines M_E as storing "reusable data processing and model training strategies." ESE jointly summarizes (i) a **data processing strategy** and (ii) a **model training strategy**. We extend this with two additional practical sections (architecture and debugging) for comprehensive coverage.

**Two core sections (from paper) + two practical extensions**:

| Section | Source | What It Contains | Example Entry | |---------|--------|-----------------|---------------| | Data Processing Strategies | Paper (core) | Preprocessing, augmentation, and data handling patterns | "For noisy sensor data: median filter before normalization reduces training instability by ~40%" | | Model Training Strategies | Paper (core) | Hyperparameters, training tricks, and training schedules | "Learning rate warmup for 10% of steps prevents early divergence in transformer fine-tuning" | | Architecture Strategies | Extension | Design choices, module configurations, and structural patterns | "Residual connections are critical for modules inserted deeper than 10 layers in transformers" | | Debugging Strategies | Extension | Diagnostic patterns that resolved experiment failures | "When loss plateaus after 50% of training: check gradient norm — clipping threshold may be too aggressive" |

**Each entry records**: Strategy name, context (when to use this), evidence (which cycle, what results), generality (domain-specific or broadly applicable), date.

**How it's used**: `experiment-pipeline` reads M_E at the start of each cycle. The paper uses embedding-based retrieval with cosine similarity, selecting the top-k_E most similar items (k_E=1 in experiments). Relevant strategies from prior cycles inform hyperparameter choices, data processing decisions, and debugging approaches, reducing the number of attempts needed.

See [assets/experiment-memory-template.md](assets/experiment-memory-template.md) for the template.

Three Evolution Mechanisms

IDE — Idea Direction Evolution

**Trigger**: After `research-ideation` completes Step 5 and saves `/direction-summary.md` for Step 6.

**Purpose**: Extract promising research directions fr