dvcrn 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,
"avg_duration_seconds": 180,
"user_satisfaction": 0.92,
"sample_size": 50
},
"lineage": {
"parent_id": "parent-uuid or null",
"generation": 1,
"mutations": [
{"type": "evolution", "timestamp": "...", "changes": "..."}
]
},
"tags": ["research", "comprehensive", "verified"]
}
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