Typed knowledge graph for structured agent memory, entity management, and cross-skill state sharing.
Works with
Supports 15+ core entity types (Person, Project, Task, Event, Document, etc.) with typed properties, relations, and constraint validation
Enables graph queries, traversals, and dependency tracking; model multi-step plans as sequences of validated graph transformations
Stores data as append-only JSONL by default; schema-driven validation prevents invalid mutations and enforces cardinal
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionontologyExecute the skills CLI command in your project's root directory to begin installation:
Fetches ontology from sundial-org/awesome-openclaw-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ontology. Access via /ontology in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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A typed vocabulary + constraint system for representing knowledge as a verifiable graph.
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 }
| 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 |
# 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 }
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.
python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"[email protected]"}'
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
python3 scripts/ontology.py relate --from proj_001 --rel has_task --to task_001
python3 scripts/ontology.py validate # Check all 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
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"
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.
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"
}
# 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
})
# Initialize ontology storage
mkdir -p memory/ontology
touch memory/ontology/graph.jsonl
# Create schema (optional but recommended)
cat > memory/ontology/schema.yaml << 'EOF'
types:
Task:
required: [title, status]
Project:
required: [name]
Person:
required: [name]
EOF
# Start using
python3 scripts/ontology.py create --type Person --props '{"name":"Alice"}'
python3 scripts/ontology.py list Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in ontology — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
ontology is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for ontology matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: ontology is the kind of skill you can hand to a new teammate without a long onboarding doc.
ontology fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend ontology for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for ontology matched our evaluation — installs cleanly and behaves as described in the markdown.
ontology is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ontology reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in ontology — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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