According to AlphaMoat, as of 2026-09, Ontology 1.0.4 has 2,488 downloads and 0 stars — ranked #4,109 of 5,078 AI skills overall, and #837 of 969 in AI Agent.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 64 | 2.2K | - | - |
| SkillHub ↗ | 122 | 2.5K | - | — |
Platforms count differently and update at different times.
The guide includes entity types such as people, projects, tasks, events, and documents, plus examples for creating, querying, relating, and validating records. Existing graph and schema data should be appended to or merged. Shared memory may be read or changed by later agents, and validation before writes may be limited; do not store secrets or sensitive personal data directly, and use validation as a review gate for important workflows.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 2.5K | — | - |
| 2026-08 | 2.1K | +6.9% | - |
| 2026-07 | 2.0K | +15.9% | - |
| 2026-06 | 1.7K | +10.6% | - |
| 2026-05 | 1.5K | — | - |
| 2026-03 | 737 | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: ontology description: 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. --- # Ontology A typed vocabulary + constraint system for representing knowledge as a verifiable graph. ## Core Concept
Everything is an **entity** with a **type**, **properties**, and **relations** to other entities. Every mutation is validated against type constraints before committing.
```
Entity: { id, type, properties, relations, created, updated }
Relation: { from_id, relation_type, to_id, properties }
```
## When to Use
| Trigger | Action |
|---------|--------|
| "Remember that..." | Create/update entity |
| "What do I know about X?" | Query graph |
| "Link X to Y" | Create relation |
| "Show all tasks for project Z" | Graph traversal |
| "What depends on X?" | Dependency query |
| Planning multi-step work | Model as graph transformations |
| Skill needs shared state | Read/write ontology objects |
## Core Types
```yaml
# Agents & People
Person: { name, email?, phone?, notes? }
Organization: { name, type?, members[] }
# Work
Project: { name, status, goals[], owner? }
Task: { title, status, due?, priority?, assignee?, blockers[] }
Goal: { description, target_date?, metrics[] }
# Time & Place
Event: { title, start, end?, location?, attendees[], recurrence? }
Location: { name, address?, coordinates? }
# Information
Document: { title, path?, url?, summary? }
Message: { content, sender, recipients[], thread? }
Thread: { subject, participants[], messages[] }
Note: { content, tags[], refs[] }
# Resources
Account: { service, username, credential_ref? }
Device: { name, type, identifiers[] }
Credential: { service, secret_ref } # Never store secrets directly
# Meta
Action: { type, target, timestamp, outcome? }
Policy: { scope, rule, enforcement }
```
## Storage
Default: `memory/ontology/graph.jsonl`
```jsonl
{"op":"create","entity":{"id":"p_001","type":"Person","properties":{"name":"Alice"}}}
{"op":"create","entity":{"id":"proj_001","type":"Project","properties":{"name":"Website Redesign","status":"active"}}}
{"op":"relate","from":"proj_001","rel":"has_owner","to":"p_001"}
```
Query via scripts or direct file ops. For complex graphs, migrate to SQLite.
### Append-Only Rule
When working with existing ontology data or schema, **append/merge** changes instead of overwriting files. This preserves history and avoids clobbering prior definitions.
## Workflows
### Create Entity
```bash
python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"[email protected]"}'
```
### Query
```bash
python3 scripts/ontology.py query --type Task --where '{"status":"open"}'
python3 scripts/ontology.py get --id task_001
python3 scripts/ontology.py related --id proj_001 --rel has_task
```
### Link Entities
```bash
python3 scripts/ontology.py relate --from proj_001 --rel has_task --to task_001
```
### Validate
```bash
python3 scripts/ontology.py validate # Check all constraints
```
## Constraints
Define in `memory/ontology/schema.yaml`:
```yaml
types:
Task:
required: [title, status]
status_enum: [open, in_progress, blocked, done]
Event:
required: [title, start]
validate: "end >= start if end exists"
Credential:
required: [service, secret_ref]
forbidden_properties: [password, secret, token] # Force indirection
relations:
has_owner:
from_types: [Project, Task]
to_types: [Person]
cardinality: many_to_one
blocks:
from_types: [Task]
to_types: [Task]
acyclic: true # No circular dependencies
```
## Skill Contract
Skills that use ontology should declare:
```yaml
# In SKILL.md frontmatter or header
ontology:
reads: [Task, Project, Person]
writes: [Task, Action]
preconditions:
- "Task.assignee must exist"
postconditions:
- "Created Task has status=open"
```
## Planning as Graph Transformation
Model multi-step plans as a sequence of graph operations:
```
Plan: "Schedule team meeting and create follow-up tasks"
1. CREATE Event { title: "Team Sync", attendees: [p_001, p_002] }
2. RELATE Event -> has_project -> proj_001
3. CREATE Task { title: "Prepare agenda", assignee: p_001 }
4. RELATE Task -> for_event -> event_001
5. CREATE Task { title: "Send summary", assignee: p_001, blockers: [task_001] }
```
Each step is validated before execution. Rollback on constraint violation.
## Integration Patterns
### With Causal Inference
Log ontology mutations as causal actions:
```python
# When creating/updating entities, also log to causal action log
action = {
"action": "create_entity",
"domain": "ontology",
"context": {"type": "Task", "project": "proj_001"},
"outcome": "created"
}
```
### Cross-Skill Communication
```python
# Email skill creates commitment
commitment = ontology.create("Commitment", {
"source_message": msg_id,
"description": "Send report by Friday",
"due": "2026-01-31"
})
# Task skill picks it up
tasks = ontology.query("Commitment", {"status": "pending"})
for c in tasks:
ontology.create("Task", {
"title": c.description,
"due": c.due,
"source": c.id
})
```
## Quick Start
```bash
# Initialize ontology storage
mkdir -p memory/ontology
touch memory/ontology/graph.jsonl
# Create schema (optional but recommended)
python3 scripts/ontology.py schema-append --data '{
"types": {
"Task": { "required": ["title", "status"] },
"Project": { "required": ["name"] },
"Person": { "required": ["name"] }
}
}'
# Start using
python3 scripts/ontology.py create --type Person --props '{"name":"Alice"}'
python3 scripts/ontology.py list --type Person
```
## References
- `references/schema.md` — Full type definitions and constraint patterns
- `references/queries.md` — Query language and traversal examples
## Instruction Scope
Runtime instructions operate on local files (`memory/ontology/graph.jsonl` and `memory/ontology/schema.yaml`) and provide CLI usage for create/query/relate/validate; this is within scope. The skill reads/writes workspace files and will create the `memory/ontology` directory when used. Validation includes property/enum/forbidden checks, relation type/cardinality validation, acyclicity for relations marked `acyclic: true`, and Event `end >= start` checks; other higher-level constraints may still be documentation-only unless implemented in code.
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
Finds skills in local and remote catalogs from a plain-language request and recommends what to install.
A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
Stores agent memory and cross-skill state as a typed, constraint-checked knowledge graph.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
A guide to creating and updating effective Claude skills.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.