Memory

Search first: /mnemonic:search {relevant_keywords} Capture after: /mnemonic:capture {namespace} "{title}"

Run /mnemonic:list --namespaces to see available namespaces from loaded ontologies.

Ontology Skill

Provides custom ontology support for extending mnemonic with domain-specific knowledge structures.

Requirements

•PyYAML - Optional but recommended for YAML parsing. The library works without it but with limited functionality.

Capabilities

•Load and validate ontology definitions (YAML)
•Define custom namespaces beyond the 9 base namespaces
•Create typed entities (semantic, episodic, procedural)
•Establish relationships between entities
•Use traits/mixins for reusable field sets
•Entity discovery patterns

Cognitive Memory Type Triad

All entity types inherit from one of three base memory types:

Type	Purpose	Examples
Semantic	Facts, concepts	Components, technologies, decisions
Episodic	Events, experiences	Incidents, debug sessions
Procedural	Step-by-step processes	Runbooks, deployments

Usage

ontology_validator.py

Validate ontology YAML files against the schema.

bash

python ${SKILL_DIR}/lib/ontology_validator.py <file> [--json]

Option	Description
`<file>`	Ontology YAML file to validate (required)
`--json`	Output validation results as JSON

ontology_registry.py

Load and query ontologies.

bash

python ${SKILL_DIR}/lib/ontology_registry.py [OPTIONS]

Option	Description
`--list`	List all loaded ontologies
`--namespaces`	List all available namespaces
`--types`	List all entity types
`--validate <NS>`	Validate a specific namespace
`--json`	Output as JSON

entity_resolver.py

Resolve and search entity references across memories.

bash

python ${SKILL_DIR}/lib/entity_resolver.py [OPTIONS]

Option	Description
`--build-index`	Build complete entity index from memory files
`--search <query>`	Search for entities by name or content
`--resolve <ref>`	Resolve entity reference (e.g., `@[[Name]]`)
`--json`	Output results as JSON

Entity Reference Syntax

•Simple: @[[Entity Name]] - Resolves by name
•Typed: [[technology:postgres-id]] - Resolves by type and ID

Discovery Patterns

Ontologies can define patterns for automatic namespace suggestion during memory capture.

Content Patterns

Match text content to suggest appropriate namespaces:

yaml

discovery:
  content_patterns:
    - pattern: "\\bdecided to\\b|\\bwe will use\\b"
      namespace: _semantic/decisions
    - pattern: "\\blearned that\\b|\\bthe fix was\\b"
      namespace: _semantic/knowledge

File Patterns

Match file paths for contextual namespace hints:

yaml

discovery:
  file_patterns:
    - pattern: "auth|login|session"
      namespaces:
        - _semantic/knowledge
        - _semantic/decisions
      context: authentication

Configuration

yaml

discovery:
  enabled: true
  confidence_threshold: 0.8  # Minimum confidence to suggest

See fallback/ontologies/mif-base.ontology.yaml for complete examples.

Files

•lib/ontology_registry.py - Load and manage ontologies
•lib/ontology_validator.py - Validate ontology YAML
•lib/ontology_loader.py - Centralized loading with MIF submodule support
•lib/entity_resolver.py - Resolve entity references
•fallback/ontologies/mif-base.ontology.yaml - Standard mnemonic namespaces
•fallback/schema/ontology/ontology.schema.json - JSON Schema