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Found 10 Skills
Master metaphysics and ontology - the study of being, existence, and fundamental reality. Use for: existence, being, substance, identity, causation, modality, time, universals. Triggers: 'ontological', 'metaphysical', 'what exists', 'substance', 'essence', 'existence', 'being', 'identity', 'persistence', 'causation', 'modality', 'possible worlds', 'universals', 'particulars', 'properties', 'abstract objects', 'time', 'change', 'composition'.
Specification-first AI development powered by Ouroboros. Socratic questioning exposes hidden assumptions before writing code. Evolutionary loop (Interview → Seed → Execute → Evaluate → Evolve) runs until ontology converges. Ralph mode persists until verification passes — the boulder never stops. Use when user says "ralph", "ooo", "don't stop", "must complete", "until it works", "keep going", "interview me", or "stop prompting".
Master African philosophical traditions including Ubuntu, Africana philosophy, and postcolonial thought. Use for: communitarian ethics, personhood, African metaphysics, decolonial philosophy. Triggers: 'Ubuntu', 'African philosophy', 'Africana', 'communitarian', 'postcolonial', 'decolonial', 'sage philosophy', 'ethnophilosophy', 'Negritude', 'African humanism', 'ubuntu ethics', 'communalism', 'African ontology', 'personhood Africa', 'I am because we are'.
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.
Expert in aggregating, processing, and synthesizing information from multiple sources into coherent insights. Use when building knowledge graphs, ontologies, RAG systems, or extracting insights across documents. Triggers include "knowledge graph", "ontology", "synthesize information", "GraphRAG", "insight extraction", "cross-document analysis".
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Use when designing database schemas, need to model domain entities and relationships clearly, building knowledge graphs or ontologies, creating API data models, defining system boundaries and invariants, migrating between data models, establishing taxonomies or hierarchies, user mentions "schema", "data model", "entities", "relationships", "ontology", "knowledge graph", or when scattered/inconsistent data structures need formalization.
Generates hierarchical knowledge graphs via Recursive Pareto Principle for optimised schema construction. Produces four-level structures (L0 meta-graph through L3 detail-graph) where each level contains 80% fewer nodes while grounding 80% of its derivative, achieving 51% coverage from 0.8% of nodes via Pareto³ compression. Use when creating domain ontologies or knowledge architectures requiring: (1) Atomic first principles with emergent composites, (2) Pareto-optimised information density, (3) Small-world topology with validated node ratios (L1:L2 2-3:1), or (4) Bidirectional construction. Integrates with graph (η≥4 validation), abduct (refactoring), mega (SuperHyperGraphs), infranodus (gap detection). Triggers: 'schema generation', 'ontology creation', 'Pareto hierarchy', 'recursive graph', 'first principles decomposition'.
Create production-ready skills from expert knowledge. Extracts domain expertise and system ontologies, uses scripts for deterministic work, loads knowledge progressively. Use when building skills that must work reliably in production.