Skill: mohmhm1/ai-genome (version 1.0.0)

--- 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 --- # 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. 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 <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. ```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 <slug>" or "view <path>" 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 <slug>" Get a machine-readable JSON card for a genome. ```bash 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). ```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.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)