Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Ontology
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.
skill openclawclawhub Free
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.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- references/queries.md, references/schema.md, scripts/ontology.py, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
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- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.4
Provenance
- Publisher
- oswalpalash
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
TriggerAction"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 queryPlanning multi-step workModel as graph transformationsSkill needs shared stateRead/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.
Create Entity
python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"alice@example.com"}'
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.
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 β Full type definitions and constraint patterns references/queries.md β 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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs1 Scripts
- SKILL.md
- references/queries.md
- references/schema.md
- scripts/ontology.py