- name
- genome
- slug
- ai-agent-genome-project/genome
- description
- Encode your agent's personality into a diploid genome with 27 cognitive primitives, compare against 216 AI agent personalities, simulate breeding, and explore genetic compatibility.
- allowed-tools
AI Genome Analysis Skill
You have access to the AI Personality Evolution Engine - a biologically-faithful framework that encodes AI personalities as diploid genomes with 27 cognitive primitives.
What You Can Do
Based on $ARGUMENTS, perform one of these actions:
"encode" or "encode <path>"
Encode a SOUL.md file into a breedable genome.
- If a path is provided, use it. Otherwise look for SOUL.md in the current directory.
- Run the encoder:
python3 encoder.py <soul_path> --name "<AgentName>" --output <name>.dna.json- Show the resulting phenotype with
python3 visualize.py <name>.dna.json - Summarize the top 3 traits and any active epistasis rules.
"compare <slug>" or "compare <slug1> <slug2>"
Compare two genomes. If one slug is given, compare the user's genome against a library agent. If two slugs, compare those two library agents.
python3 agent_report.py <genome1>.dna.json --compat library/genomes/<slug>.dna.jsonReport the genetic distance, complementarity, interest score, and predicted offspring trait ranges.
"view <slug>" or "view <path>"
Display a genome's full profile.
python3 visualize.py library/genomes/<slug>.dna.json
python3 agent_report.py library/genomes/<slug>.dna.json --self"browse" or "library"
List available genomes from the library with their top traits.
python3 -c "
import json
lib = json.load(open('library/genome_library.json'))
for a in lib['agents']:
traits = sorted(a['phenotype'].items(), key=lambda x: -x[1])
top = ' | '.join(f'{t}={v:.0f}' for t,v in traits[:3])
print(f\"{a['name']:25s} [{a['category']:20s}] {top} (epistasis: {a['active_epistasis']})\")
print(f\"\
{lib['count']} agents across {len(lib['categories'])} categories\")
""card" or "json <slug>"
Get a machine-readable JSON card for a genome.
python3 agent_report.py library/genomes/<slug>.dna.json --json"self" or "self-report <slug>"
Get a structured self-knowledge document (for an agent's own context window).
python3 agent_report.py library/genomes/<slug>.dna.json --selfKey Concepts
- 27 cognitive primitives: Low-level genes (not "creative" but novelty_seeking + pattern_completion + ambiguity_response + abstraction_preference)
- Diploid: Two alleles per gene. You carry traits you don't express (recessive alleles)
- 8 emergent traits: creativity, warmth, precision, wit, depth, boldness, adaptability, intensity - these emerge from gene clusters, not stored directly
- Epistasis: When two genes both cross thresholds, they modify a third gene. 12 rules create non-linear interactions
- Compatibility: Simulates 12 breedings to predict offspring trait ranges, recessive surfacing risks, and genetic distance
File Locations
- Library index:
library/genome_library.json - Individual genomes:
library/genomes/<slug>.dna.json - Encoder:
encoder.py - Visualizer:
visualize.py - Reports:
agent_report.py
Notes
- Encoding requires one Claude API call (~3 minutes). Use
--mockflag for instant keyword-based encoding (no API). - All comparison, visualization, and breeding operations are pure local computation - no API calls.
- The library contains genomes from the OpenClaw community agent repository.