genome-manager

# 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: ```bash # 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 ```python # 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 ```json { "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 ```python 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