evo-memory
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 fa
npx skills add BioTender-max/awesome-bio-agent-skills --skill evo-memory --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Keeps a persistent learning layer across ideation and experimentation by storing historical directions and reusable strategies. It implements three evolution mechanisms: IDE to extract and store promising directions after ideation, IVE to classify why a failed method failed and update memory accordingly, and ESE to distill reusable data processing, model training, architecture, and debugging strategies after successful experiments. It provides templates and memory locations for Ideation Memory (M_I) and Experimentation Memory (M_E), plus procedures for loading prior knowledge at cycle start and writing evolution reports.
How it works
- Maintains two memory stores: Ideation Memory (M_I) and Experimentation Memory (M_E).
- IDE (Idea Direction Evolution): after research-ideation completes, reads M_I, runs an IDE prompt, abstracts directions to reusable levels, updates or appends M_I entries, prunes exhausted feasible directions, and writes an evolution report.
- IVE (Idea Validation Evolution): triggers after failed experiments or no executable code within budget; uses an IVE prompt to classify failure as FAILED(NoExecutableWithinBudget), FAILED(WorseThanBaseline), or NOT_FAILED; updates M_I accordingly, and provides a five-question diagnostic to distinguish implementation vs fundamental failure; escalation rules apply for repeated implementation failures.
- ESE (Experiment Strategy Evolution): after a successful experiment-pipeline, runs an ESE prompt to generate DATA SUMMARY and MODEL SUMMARY; populates M_E with data processing and model training strategies, then extracts architecture decisions and debugging patterns from trajectory logs, assesses generality, updates M_E, and writes an evolution report.
- Reading memory at cycle start: loads M_I and M_E, summarizes relevant entries, and injects them into the current context for ideation or experiments.
- Versioning and evolution reports are maintained under the memory framework; pruning rules apply to stale or superseded entries.
When to use it
- After completing a research-ideation cycle to update Ideation Memory (M_I).
- After completing or failing an experiment pipeline to update memory (M_E or M_I via IVE as applicable).
- When starting a new research cycle to load prior knowledge.
- When user asks about research memory, learned patterns, or cross-cycle knowledge.
- When user mentions evo-memory, update memory, research history, or evolution.
What it can touch
- Memory files:
/memory/ideation-memory.mdand/memory/experiment-memory.md. - Templates and example injections described in the skill.
- It operates via prompts (IDE, IVE, ESE) and trajectory logs to extract reusable knowledge.
Caveats
- Do not use evo-memory for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use research-ideation).
- Retrieval relies on embedding-like similarity and provided templates; applicability depends on cycle context and recorded metadata.
- Entries may be pruned if stale or superseded; version tracking and evolution reports are required for audit.
# 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.m
- When to Use This Skill
- The Learning Layer
- Two Memory Stores
- Ideation Memory (MI)
- Experimentation Memory (ME)
- Three Evolution Mechanisms
- IDE — Idea Direction Evolution
- IVE — Idea Validation Evolution
- ESE — Experiment Strategy Evolution
- Reading Memory at Cycle Start
- For research-ideation (inject MI)
- For experiment-pipeline (inject ME)
- Memory Maintenance
- Pruning Stale Entries
What does the evo-memory skill do?
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 fa
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill evo-memory --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
