據 AlphaMoat 統計,截至 2026-09,Ontology 1.0.4 累計下載 2,488 次、獲 0 個星標,在全部 5,078 個 AI 技能中排名第 4,109 位,在「AI 智慧體」分類(969 個)中排名第 837 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 64 | 2191 | - | - |
| SkillHub ↗ | 122 | 2488 | - | — |
各平台的計數口徑不同,統計時間不一致。
主文列出人物、專案、任務、事件、文件等實體型別,提供建立、查詢、關係連線與校驗示例;已有圖譜或 schema 應採用追加或合併方式更新。共享記憶會被後續智慧體讀取或修改,且寫入前的校驗保證可能有限;不要直接存放秘密或敏感個人資訊,並在重要流程中將校驗作為審查關卡。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 2488 | — | - |
| 2026-08 | 2114 | +6.9% | - |
| 2026-07 | 1978 | +15.9% | - |
| 2026-06 | 1707 | +10.6% | - |
| 2026-05 | 1544 | — | - |
| 2026-03 | 737 | — | - |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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.
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。