Agent Genome Encoding

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.

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 "

Encode a SOUL.md file into a breedable genome.

1. If a path is provided, use it. Otherwise look for SOUL.md in the current directory.
2. Run the encoder:

bash

python3 encoder.py <soul_path> --name "<AgentName>" --output <name>.dna.json

3. Show the resulting phenotype with python3 visualize.py <name>.dna.json
4. Summarize the top 3 traits and any active epistasis rules.

"compare " or "compare "

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.

bash

python3 agent_report.py <genome1>.dna.json --compat library/genomes/<slug>.dna.json

Report the genetic distance, complementarity, interest score, and predicted offspring trait ranges.

"view " or "view "

Display a genome's full profile.

bash

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.

bash

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\"\n{lib['count']} agents across {len(lib['categories'])} categories\")
"

"card" or "json "

Get a machine-readable JSON card for a genome.

bash

python3 agent_report.py library/genomes/<slug>.dna.json --json

"self" or "self-report "

Get a structured self-knowledge document (for an agent's own context window).

bash

python3 agent_report.py library/genomes/<slug>.dna.json --self

Key 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 --mock flag 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.