Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
数据来源:ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一:命令行安装(推荐)
推荐(无需提前安装 clawhub)
npx clawhub@latest --dir ~/.claude/skills install ontology或使用 clawhub CLI(需提前安装)
clawhub --dir ~/.claude/skills install ontology⚠️ 需要 Node.js 18+,没有 Node?请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二:手动下载安装(无需 Node)
下载 ZIP,解压后将文件夹放到以下路径,重启 Agent 即可:
安装路径
~/.claude/skills/ontology/💡解压后将文件夹放到上方路径,重启 Agent 即可生效
--- 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. ---
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.
When working with existing ontology data or schema, append/merge changes instead of overwriting files. This preserves history and avoids clobbering prior definitions.
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)
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/schema.md — Full type definitions and constraint patternsreferences/queries.md — Query language and traversal examplesRuntime 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.
安装 ontology 后,可以对 AI 说这些话来触发它
Help me get started with ontology
Explains what ontology does, walks through the setup, and runs a quick demo based on your current project
Use ontology to typed knowledge graph for structured agent memory and composable sk...
Invokes ontology with the right parameters and returns the result directly in the conversation
What can I do with ontology in my ai agent & automation workflow?
Lists the top use cases for ontology, with example commands for each scenario
将技能文件夹放到 ~/.claude/skills/ontology/ 目录(个人级,所有项目可用),或 .claude/skills/ontology/(项目级)。重启 AI 客户端后,用 /ontology 主动调用,或让 AI 根据上下文自动发现并使用。
ontology 支持 Claude、Cursor、OpenClaw,可与这些 AI 平台无缝集成,扩展其能力。
ontology 可免费安装使用。请查阅仓库了解许可证信息。
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
ontology 属于「AI Agent & Automation」分类,该分类的技能帮助 AI 智能体在此领域执行专业任务。
Automate my ai agent & automation tasks using ontology
Identifies repetitive steps in your workflow and sets up ontology to handle them automatically