Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Genome Manager
Manage Genome Evolution Protocol (GEP) genomes for AI agent self-evolution. Use when creating, storing, retrieving, mutating, or tracking genomes - the encod...
skill openclawclawhub Free
Manage Genome Evolution Protocol (GEP) genomes for AI agent self-evolution. Use when creating, storing, retrieving, mutating, or tracking genomes - the encod...
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/genome_manager.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- KyleChen26
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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 β Encode β Store β Retrieve β Adopt β Evolve β Share
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
TypeDescriptionUse CaseevolutionIncremental improvementRefine existing patternadaptationContext-specific changeAdjust for new domainspecializationNarrow scopeOptimize for specific sub-taskcrossoverCombine two genomesMerge 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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts
- SKILL.md
- scripts/genome_manager.py