SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A specification method for making a business action into a tool an AI agent can call safely. It defines the action’s inputs, outputs, permissions, preconditions, expected effects, error handling, and audit information.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for cataloguing and governing the different kinds of data used by AI systems, including training examples, current facts, rules, model outputs, and process logs.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for connecting observed facts, business rules, and permitted actions into one traceable loop. It records what happened, explains why a conclusion follows, defines allowed actions, and captures the results.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for explaining a business decision through three parts: current facts, business rules and causes, and the goals being balanced. It keeps sources, uncertainty, alternative choices, and the reasoning path visible.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A five-stage method for planning an enterprise ontology, a structured model of the concepts and relationships used across a business, from source material through ongoing use.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for loading only the ontology knowledge, facts, rules, and tools needed for the user’s current intent. An ontology is a structured model of the concepts and relationships in a business area.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for choosing how an AI system should handle a business problem after deciding that an ontology—a structured map of concepts and relationships—is suitable. It compares patterns such as workflow automation, decision support, knowledge answering, collaboration, and continuous planning.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for deciding whether an ontology is suitable for an enterprise AI scenario and testing that choice with a small end-to-end sample. An ontology is a structured model of concepts, rules, and relationships.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for deciding how an ontology’s knowledge should reach an AI model: directly in its instructions, through RAG, or through fine-tuning. RAG retrieves relevant information at answer time; fine-tuning changes the model using training examples.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A testing method for an ontology, which is a structured model of business concepts and rules. It starts with real business questions and expected answers, then adds boundary, missing-data, conflict, permission, and regression tests.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A quality-review process for an ontology—a structured map of concepts and relationships—created by an AI model. It combines independent review, software checks, and business-expert checks before production use.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for turning a completed ontology—a structured map of concepts and relationships—into a shared service that AI agents can query. It covers versions, access permissions, staged releases, rollback, monitoring, and audit records.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A planning method for expanding an ontology from one proven business scenario to shared concepts across a field and then across fields. “Point, line, plane” describes these increasing levels of reuse.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for deciding whether an agent should perform a business action automatically, ask for approval, or work with people across systems. It assesses risk, rule clarity, recoverability, and coordination needs.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A method for mapping confirmed business knowledge into an ontology, a structured description of concepts, relationships, rules, permissions, and actions. The “7+1” parts also define the agent’s goal and how success is judged.
SuperChason/ontology-driven-ai-data-management-skills
Skill Claude CodeCodex
A structured interview method for turning documents, data, procedures, and expert knowledge into information that can support an ontology. Its 29 question areas cover concepts, rules, processes, permissions, actions, and quality constraints.