Agent Genome Encoding

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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: - Bash - Read - Write - Glob - Grep

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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:

python3 encoder.py  --name "" --output .dna.json

3. Show the resulting phenotype with python3 visualize.py .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.

python3 agent_report.py .dna.json --compat library/genomes/.dna.json

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

"view " or "view "

Display a genome's full profile.

python3 visualize.py library/genomes/.dna.json
python3 agent_report.py library/genomes/.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\"\n{lib['count']} agents across {len(lib['categories'])} categories\")
"

"card" or "json "

Get a machine-readable JSON card for a genome.

python3 agent_report.py library/genomes/.dna.json --json

"self" or "self-report "

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

python3 agent_report.py library/genomes/.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/.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.

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.