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 skills add modeled-information-format/mnemonic --skill ontologygit clone --depth 1 https://github.com/modeled-information-format/mnemonicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/modeled-information-format/mnemonic/ontology)<a href="https://agentmods.dev/skills/modeled-information-format/mnemonic/ontology"><img src="https://agentmods.dev/badge/skills/modeled-information-format/mnemonic/ontology/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/modeled-information-format/mnemonic/ontology"><img src="https://agentmods.dev/badge/skills/modeled-information-format/mnemonic/ontology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00074 | $0.00989 |
| Opus 5.5 | $0.00030 | $0.00396 |
| Sonnet 5.5 | $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 1mo ago.
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 1.6 KB
- fallback/schema/ontology/ontology.context.jsonld 3.1 KB
- fallback/schema/ontology/ontology.schema.json 12 KB
- fallback/schema/ontology/README.md 2.5 KB
- lib/__init__.py 1.2 KB runs code
- lib/entity_resolver.py 18 KB runs code
- lib/ontology_loader.py 14 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.
- 1mo ago First seen · 155 lines · 74 tokens per session scan A e9fd4ef781ae
ontology is a skill published in the GitHub repository modeled-information-format/mnemonic (23 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 989 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
reflect
Deep reflection skill - read memory and context, decide whether there is something worth sending.
digest-auto
A skill for analysing the current state of an EpisodicRAG system, which retrieves information from records of past events or work sessions.
digest-config
An interactive configuration tool for EpisodicRAG, a system that retrieves relevant information from past interactions or events.
digest-setup
An interactive setup process for the early stage of EpisodicRAG, a system that retrieves useful information from past conversations.
okf
Author, maintain, and consume Open Knowledge Format (OKF) knowledge bundles — portable markdown + YAML frontmatter that both humans and agents read. Use when capturing project knowledge (services, APIs, schemas, metrics, runbooks, decisions) into an OKF bundle, when updating one after code or docs change, or when a…
forgetful-remember
Remember knowledge worth keeping — a decision made, a solution found, a preference stated, a pattern confirmed. Use when work surfaces something future sessions will need, or the user asks to remember something. Routes content to the right store (memory, document, code artifact, entity, procedure, file) and enforces…