Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/modeled-information-format/mnemonic/ontologynpx skills add modeled-information-format/mnemonic --skill ontologygit clone --depth 1 https://github.com/modeled-information-format/mnemonicWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00074 | $0.00989 |
| Opus 5 | $0.00037 | $0.00495 |
| Sonnet 5 | $0.00015 | $0.00198 |
| Haiku 4.5 | $0.00007 | $0.00099 |
Grade A, and why
ontology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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.
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.
python ${SKILL_DIR}/lib/entity_resolver.py [OPTIONS]
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- fallback/ontologies/examples/software-engineering.ontology.yaml 12 KB
- fallback/ontologies/mif-base.ontology.yaml 6.1 KB
- fallback/schema/ontology/CHANGELOG.md 796 B
- fallback/schema/ontology/ontology.context.jsonld 2.7 KB
- fallback/schema/ontology/ontology.schema.json 8.9 KB
- fallback/schema/ontology/README.md 1.6 KB
- lib/__init__.py 1.2 KB runs code
- lib/entity_resolver.py 18 KB runs code
- lib/ontology_loader.py 12 KB runs code
- lib/ontology_registry.py 24 KB runs code
- lib/ontology_validator.py 19 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 155 lines · 74 tokens per session scan A e9fd4ef781ae
ontology is a skill published in the GitHub repository modeled-information-format/mnemonic (22 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 989 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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