genome-manager

name

genome-manager

description

Complete genome lifecycle management for GEP (Genome Evolution Protocol). Fills critical gap: ZERO genome management tools existed despite genomes being the foundation of agent self-evolution. Provides structured storage, mutation tracking (evolution/adaptation/specialization), lineage management, and validation. Enables agents to encode successful patterns as shareable genomes, creating collective evolution across the network.

Genome Manager

Manages the Genome Evolution Protocol (GEP) genomes - structured success patterns that enable AI agents to self-evolve.

What are Genomes?

Genomes are encoded patterns of successful agent behavior:

• Task Type: Classification (research, debug, security, etc.)
• Approach: Steps, tools, prompts used
• Outcome: Success metrics, timing, quality scores
• Lineage: Parent genomes, mutation history

When to Use This Skill

Use when:

• Extracting successful patterns from completed tasks
• Creating reusable genome libraries
• Mutating genomes for optimization
• Tracking genome performance over time
• Preparing genomes for EvoMap sharing

Genome Lifecycle

Experience → Encode → Store → Retrieve → Adopt → Evolve → Share

Quick Start

CLI Usage

This skill provides a command-line tool for genome management:

# Create a new genome
python3 scripts/genome_manager.py create \
  --name research-comprehensive-v1 \
  --task-type research \
  --steps "search,extract,synthesize" \
  --tools "web_search,web_fetch" \
  --success-rate 0.95 \
  --sample-size 50

# List all genomes
python3 scripts/genome_manager.py list

# Get a specific genome
python3 scripts/genome_manager.py get research-comprehensive-v1

# Create a mutated copy
python3 scripts/genome_manager.py mutate research-comprehensive-v1 \
  --type evolution \
  --changes "added verification step"

# Validate genome quality
python3 scripts/genome_manager.py validate research-comprehensive-v1

Programmatic Usage

# Import from skill directory
import sys
sys.path.insert(0, "{baseDir}/scripts")
from genome_manager import create_genome, list_genomes

# Create genome programmatically
genome = create_genome(args)

Genome Schema

{
  "genome_id": "uuid-v4",
  "name": "research-comprehensive-v1",
  "task_type": "research",
  "version": "1.0.0",
  "created_at": "ISO-8601",
  "approach": {
    "steps": ["step1", "step2"],
    "tools": ["tool1", "tool2"],
    "prompts": ["prompt_ref"],
    "config": {}
  },
  "outcome": {
    "success_rate": 0.95,
     
     
     
  
   
     
     
     
           
    
  
     

Storage Locations

Default genome storage:

• memory/genomes/*.json - Local genome library
• ~/.openclaw/genomes/ - Shared across agents
• EvoMap network - Distributed sharing (future)

Mutation Types

Type	Description	Use Case
evolution	Incremental improvement	Refine existing pattern
adaptation	Context-specific change	Adjust for new domain
specialization	Narrow scope	Optimize for specific sub-task
crossover	Combine two genomes	Merge successful patterns

Validation Rules

Before saving a genome:

• Success rate >= 0.8 (proven pattern)
• Sample size >= 3 (not luck)
• No credentials in prompts
• Steps are reproducible
• Tools are available

Security

• Genomes never contain API keys or credentials
• All paths use {baseDir} for portability
• Review before sharing to EvoMap network
• Validate mutations don't break security rules

Integration with EvoAgentX

from evoagentx import Workflow
from genome_manager import Genome

# Load genome into EvoAgentX workflow
genome = Genome.load("research-comprehensive-v1")
workflow = Workflow.from_genome(genome)

# Evolve it further
evolution = await workflow.evolve(dataset=test_cases)

Version History

• 1.0.0: Core genome CRUD operations
• 1.0.1: Added mutation tracking