Ontology 1

Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...

Security Scan

The skill implements a local typed knowledge-graph (files + Python helper) consistent with its description; it reads/writes workspace files and does not request credentials or network access.

Purpose & Capability

Name/description match the included assets: SKILL.md describes a typed ontology and the included scripts/ontology.py implements create/query/relate/validate operations over a JSONL graph file. Nothing in the code or docs demands unrelated capabilities (no cloud creds, no unrelated binaries).

Instruction Scope

Runtime instructions focus on creating/querying/linking entities via memory/ontology/graph.jsonl and the scripts. They direct file writes/reads within the workspace and recommend schema files. They do not instruct reading arbitrary system files or transmitting data externally. Note: schema and document types reference local file paths and URLs — the skill does not itself fetch remote URLs, but users should avoid putting secrets or sensitive file contents into the graph.

Install Mechanism

No install spec; this is instruction-only with a bundled Python script. No external downloads or package installs are requested. Risk is limited to the included script being run locally.

Credentials

The skill declares no required environment variables, credentials, or config paths. The code does not read secrets or environment vars. Schema enforces using secret_ref for credentials (advisory).

Persistence & Privilege

always is false and the skill can be user-invoked or autonomously invoked per platform defaults. The skill writes an append-only graph file under memory/ontology/graph.jsonl within the agent workspace — this is expected for its purpose but does persist user data locally.

Assessment

This skill appears coherent and implements a local ontology stored under memory/ontology/graph.jsonl using the bundled Python script. It does not request secrets or network access. Before installing: (1) be aware data is persisted locally in the workspace (append-only), so do not store raw secrets or sensitive file contents in entity properties; use secret_ref patterns as recommended; (2) confirm the agent's workspace root is appropriate (the script constrains paths to the workspace); (3) if you intend to expose this graph to other skills, review those skills' permissions because they may read/write the same files; and (4) you may review the full scripts/ontology.py to confirm its behavior matches your expectations (no network or unexpected system access was observed).

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

# 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

{"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

python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"[e-mail]"}'

Query

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

python3 scripts/ontology.py relate --from proj_001 --rel has_task --to task_001

Validate

python3 scripts/ontology.py validate  # Check all constraints

Constraints

Define in memory/ontology/schema.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:

# 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:

# 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

# 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

# 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.