@guoliang1114-boop

Creator · guoliang1114-boop

Last updated · Aug 24, 2026

archimate

REVIEW · 64Registry indexed

Create ArchiMate enterprise architecture diagrams using PlantUML stdlib macros. Best for TOGAF viewpoints, layered EA modeling (Business/Application/Technology), motivation analysis, and migration planning.

OpenAgentSkill Trust Score
64/100

Sandbox only

Quality62/100
Audit76/100
Stars37
Verified installs0

Install targets

Codex install prompt

Install the "archimate" agent skill from https://github.com/guoliang1114-boop/AriaAI/tree/main/skills/archimate. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Create ArchiMate enterprise architecture diagrams using PlantUML stdlib macros. Best for TOGAF viewpoints, layered EA modeling (Business/Application/Technology), motivation analysis, and migration planning. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"guoliang1114-boop-archimate","task":"Install archimate","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.

Supply asset profile

Coding and developer agents

Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.

Browse track

Scenario

Testing and QA

I need my agent to test a web app, reproduce bugs, and verify fixes.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add guoliang1114-boop/AriaAI --skill archimate

Maintenance

fresh

Pushed today

Risk

Needs review

Financial research output is not financial advice; require human review before any live investment decision

GitHub quality

37

62/100 Quality · 72/100 Trust

Coverage tags

CodingTesting and QAbusinessagent-skill

Review notes

Financial research output is not financial advice; require human review before any live investment decision · Metadata includes promotional content (author line referencing Markdown Viewer) that may be undesirable in a skill meant to be neutral; not a security risk but could be removed for clarity.

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
62

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
64

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
76

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

37 GitHub stars

Repo activity

37 stars, 2 forks

Maintenance

Pushed today

License

MIT

Install

npx skills add guoliang1114-boop/AriaAI --skill archimate

Install safety

standard package or runtime install path

Permission surface

network or browser access, database access

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • Metadata includes promotional content (author line referencing Markdown Viewer) that may be undesirable in a skill meant to be neutral; not a security risk but could be removed for clarity.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

View technical data+

Suited tasks

  • Browser automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Navigate pages

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add guoliang1114-boop/AriaAI --skill archimate
Policy
review
Human review
yes

Trust and risk

Trust
64/100
Audit
76/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add guoliang1114-boop/AriaAI --skill archimate

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Low GitHub adoption signal
  • Metadata includes promotional content (author line referencing Markdown Viewer) that may be undesirable in a skill meant to be neutral; not a security risk but could be removed for clarity.
  • No OpenAgentSkill engagement data yet

Agent safety v2

56/100 · Review before install

Experimentalreview

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Resolve via API

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

medium

Database access

Skill may inspect schemas, query databases, or work with persistent stores.

  • Financial research output is not financial advice; require human review before any live investment decision

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use archimate in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20archimate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/guoliang1114-boop-archimate/install
Install command: npx skills add guoliang1114-boop/AriaAI --skill archimate
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use archimate for this task. Review https://www.openagentskill.com/api/skills/guoliang1114-boop-archimate/install, then install with: npx skills add guoliang1114-boop/AriaAI --skill archimate

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

61/100

Browser automation

Platforms

Claude Code

Audit report

Needs review · 76/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Browser automation

Prototype with this skill first; keep a fallback candidate ready.

61
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

Browser automation

Trust label

Prototype first

Install path

Command ready

Use when

  • Browser automation workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 62/100 quality profile

review first

  • Low GitHub adoption signal
  • Metadata includes promotional content (author line referencing Markdown Viewer) that may be undesirable in a skill meant to be neutral; not a security risk but could be removed for clarity.
  • No OpenAgentSkill engagement data yet

Implementation path

  1. 1Install it in a sandbox agent and run one Browser automation task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

64
OpenAgentSkill Trust Score

GitHub adoption

CHECK

37 GitHub stars

Stars/forks activity

CHECK

37 stars, 2 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

MIT

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Metadata includes promotional content (author line referencing Markdown Viewer) that may be undesirable in a skill meant to be neutral; not a security risk but could be removed for clarity.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 37 GitHub stars
  • Stars/forks activity: 37 stars, 2 forks; issue activity unavailable in current metadata
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

62
GitHub stars
37
Freshness
Today
Install ready
Yes
License
MIT
Review before install: Low GitHub adoption signal · Metadata includes promotional content (author line referencing Markdown Viewer) that may be undesirable in a skill meant to be neutral; not a security risk but could be removed for clarity.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

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Prototype
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Prototype
Ready60
Quality61
Stars2

Overview

--- name: archimate description: Create ArchiMate enterprise architecture diagrams using PlantUML stdlib macros. Best for TOGAF viewpoints, layered EA modeling (Business/Application/Technology), motivation analysis, and migration planning. metadata: author: ArchiMate diagrams are powered by Markdown Viewer — the best multi-platform Markdown extension (Chrome/Edge/Firefox/VS Code) with diagrams, formulas, and one-click Word export. Learn more at https://docu.md ---

# Enterprise Architecture Diagram Generator (ArchiMate)

**Quick Start:** Add `!include <archimate/Archimate>` → Declare typed elements → Connect with `Rel_*` macros → Group into layers with `rectangle` → Wrap in ` ```plantuml ` fence.

> ⚠️ **IMPORTANT:** Always use ` ```plantuml ` or ` ```puml ` code fence. NEVER use ` ```text ` — it will NOT render as a diagram.

## Critical Rules

- Every diagram starts with `@startuml` and ends with `@enduml` - Must include `!include <archimate/Archimate>` before using any macros - Element syntax: `Layer_Type(alias, "Label")` - Relationship syntax: `Rel_Type(fromAlias, toAlias, "label")` - Use `rectangle "Layer" { ... }` to group elements into ArchiMate layers - Directional suffixes `_Up`, `_Down`, `_Left`, `_Right` control relationship direction

## Element Macros

### Business Layer

| Macro | ArchiMate Element | |-------|-------------------| | `Business_Actor(id, "Label")` | Business Actor | | `Business_Role(id, "Label")` | Business Role | | `Business_Process(id, "Label")` | Business Process | | `Business_Function(id, "Label")` | Business Function | | `Business_Service(id, "Label")` | Business Service | | `Business_Event(id, "Label")` | Business Event | | `Business_Interface(id, "Label")` | Business Interface | | `Business_Collaboration(id, "Label")` | Business Collaboration | | `Business_Object(id, "Label")` | Business Object | | `Business_Product(id, "Label")` | Business Product | | `Business_Contract(id, "Label")` | Business Contract | | `Business_Representation(id, "Label")` | Business Representation |

### Application Layer

| Macro | ArchiMate Element | |-------|-------------------| | `Application_Component(id, "Label")` | Application Component | | `Application_Service(id, "Label")` | Application Service | | `Application_Function(id, "Label")` | Application Function | | `Application_Interface(id, "Label")` | Application Interface | | `Application_Process(id, "Label")` | Application Process | | `Application_Interaction(id, "Label")` | Application Interaction | | `Application_Event(id, "Label")` | Application Event | | `Application_Collaboration(id, "Label")` | Application Collaboration | | `Application_DataObject(id, "Label")` | Application Data Object |

### Technology Layer

| Macro | ArchiMate Element | |-------|-------------------| | `Technology_Device(id, "Label")` | Technology Device | | `Technology_Node(id, "Label")` | Technology Node | | `Technology_SystemSoftware(id, "Label")` | System Software | | `Technology_Artifact(id, "Label")` | Technology Artifact | | `Technology_CommunicationNetwork(id, "Label")` | Communication Network | | `Technology_Path(id, "Label")` | Technology Path | | `Technology_Service(id, "Label")` | Technology Service | | `Technology_Process(id, "Label")` | Technology Process | | `Technology_Function(id, "Label")` | Technology Function | | `Technology_Interface(id, "Label")` | Technology Interface |

### Motivation Layer

| Macro | ArchiMate Element | |-------|-------------------| | `Motivation_Stakeholder(id, "Label")` | Stakeholder | | `Motivation_Driver(id, "Label")` | Driver | | `Motivation_Assessment(id, "Label")` | Assessment | | `Motivation_Goal(id, "Label")` | Goal | | `Motivation_Outcome(id, "Label")` | Outcome | | `Motivation_Principle(id, "Label")` | Principle | | `Motivation_Requirement(id, "Label")` | Requirement | | `Motivation_Constraint(id, "Label")` | Constraint | | `Motivation_Value(id, "Label")` | Value |

### Strategy Layer

| Macro | ArchiMate Element | |-------|-------------------| | `Strategy_Capability(id, "Label")` | Capability | | `Strategy_Resource(id, "Label")` | Resource | | `Strategy_CourseOfAction(id, "Label")` | Course of Action | | `Strategy_ValueStream(id, "Label")` | Value Stream |

### Implementation Layer

| Macro | ArchiMate Element | |-------|-------------------| | `Implementation_WorkPackage(id, "Label")` | Work Package | | `Implementation_Deliverable(id, "Label")` | Deliverable | | `Implementation_Plateau(id, "Label")` | Plateau | | `Implementation_Gap(id, "Label")` | Gap | | `Implementation_Event(id, "Label")` | Implementation Event |

## Relationship Macros

All relationships support directional suffixes: `_Up`, `_Down`, `_Left`, `_Right`.

| Macro | ArchiMate Relationship | Line Style | |-------|------------------------|------------| | `Rel_Composition(from, to, "label")` | Composition | Solid + filled diamond | | `Rel_Aggregation(from, to, "label")` | Aggregation | Solid + open diamond | | `Rel_Assignment(from, to, "label")` | Assignment | Solid + circle→triangle | | `Rel_Realization(from, to, "label")` | Realization | Dotted + hollow triangle | | `Rel_Serving(from, to, "label")` | Serving | Solid + arrow | | `Rel_Triggering(from, to, "label")` | Triggering | Solid + filled triangle | | `Rel_Flow(from, to, "label")` | Flow | Dashed + filled triangle | | `Rel_Access(from, to, "label")` | Access | Dotted line | | `Rel_Access_r(from, to, "label")` | Access (read) | Dotted + arrow | | `Rel_Access_w(from, to, "label")` | Access (write) | Dotted + reverse arrow | | `Rel_Influence(from, to, "label")` | Influence | Dashed + arrow | | `Rel_Association(from, to, "label")` | Association | Solid line | | `Rel_Specialization(from, to, "label")` | Specialization | Solid + hollow triangle |

## Quick Example

```plantuml @startuml !include <archimate/Archimate>

rectangle "Business" { Business_Actor(customer, "Customer") Business_Process(order, "Order Process") Business_Service(orderSvc, "Order Service") }

rectangle "Application" { Application_Component(orderApp, "Order System") Application_Service(orderAPI, "Order API") }

rectangle "Technology" { Technology_Node(server, "App Server") Technology_Device(db, "Database Server") }

Rel_Triggering(customer, order, "places order") Rel_Realization(order, orderSvc, "realizes") Rel_Serving(orderAPI, orderSvc, "serves") Rel_Realization(orderApp, orderAPI, "realizes") Rel_Assignment(server, orderApp, "runs on") Rel_Serving(db, server, "stores data") @enduml ```

## Diagram Types

| Type | Purpose | Key Macros | Example | |------|---------|------------|---------| | Enterprise Landscape | Full B/A/T layered view | All layers | [enterprise-landscape.md](examples/enterprise-landscape.md) | | Application Integration | App-to-app data flows | `Application_*` | [application-integration.md](examples/application-integration.md) | | Technology Infrastructure | Infrastructure stack | `Technology_*` | [technology-infrastructure.md](examples/technology-infrastructure.md) | | Business Capability | Capability map | `Strategy_*`, `Business_*` | [business-capability.md](examples/business-capability.md) | | Migration Planning | Plateau-based roadmap | `Implementation_*` | [migration-planning.md](examples/migration-planning.md) | | Security Architecture | Security controls | `Technology_*`, `Motivation_*` | [security-architecture.md](examples/security-architecture.md) | | Data Architecture | Data flow & ownership | `Application_DataObject`, `Rel_Access_*` | [data-architecture.md](examples/data-architecture.md) | | DevOps Pipeline | CI/CD delivery chain | `Technology_*`, `Application_*` | [devops-pipeline.md](examples/devops-pipeline.md) |

## Capability Upgrade

### Mode Selection

- **Quick**: 根据用户描述生成单视角 ArchiMate 图。 - **Standard**: 选择业务、应用、技术、动机或迁移视角,并输出可渲染 PlantUML。 - **Deep**: 结合项目架构、业务能力、系统清单、数据流和迁移计划,形成多视角架构图包。

### Viewpoint Decision Logic

先判断用户要解释什么:业务能力、应用集成、技术部署、动机目标、安全控制还是迁移路线。不同问题不能混在一张图里;复杂架构应拆成多个 viewpoint。

### Quality Gates

- [ ] 元素层级符合 ArchiMate 语义,不混用业务/应用/技术层。 - [ ] 关系类型准确,避免全部使用普通箭头。 - [ ] 图中节点数量适中,必要时拆图。 - [ ] 命名能被业务和技术读者理解。 - [ ] 输出包含可复制 PlantUML 和简短解读。

### Deliverable Catalog

| Deliverable | When to use | Minimum content | Format | |-------------|-------------|-----------------|--------| | ArchiMate viewpoint diagram | 单一架构视角 | 视角目标、元素、关系、PlantUML 和说明 | PlantUML / Markdown | | Enterprise architecture pack | 多层架构说明 | 业务、应用、技术、动机、迁移视角和关系 | Markdown / PPT | | Capability map | 业务能力建模 | 能力、层级、owner、支撑应用和目标关联 | PlantUML / PPT | | Application integration view | 系统集成说明 | 应用、接口、数据对象、协议和依赖关系 | PlantUML | | Migration roadmap view | 架构演进 | 当前/目标 plateau、work package、里程碑和依赖 | PlantUML / PPT | | Diagram QA checklist | 发布前检查 | 语义、关系、层级、命名、密度和可读性 | Checklist |

Technical details

Version
1.0.0
License
MIT
Last updated
Aug 24, 2026
Published
Aug 24, 2026

Decision snapshot

Fallback candidate

61
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

76
Needs review
Security
77/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for archimate, ready for a manual X post.

Curator note
archimate: Create ArchiMate enterprise architecture diagrams using PlantUML stdlib macros. Best for TOGA...

37 stars

https://www.openagentskill.com/skills/guoliang1114-boop-archimate?ref=x
Open X draft
Optional reply with install command
Listing + install path for archimate:
https://www.openagentskill.com/skills/guoliang1114-boop-archimate?ref=x

Install: npx skills add guoliang1114-boop/AriaAI --skill archimate

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Author

G

guoliang1114-boop

@guoliang1114-boop

Platform fit

Health signals

GitHub stars
37
Quality score
34/100
Last GitHub push
Aug 24, 2026
Framework hints
Unknown
OpenAgentSkill views
0
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

64
  • GitHub adoption37 GitHub starsCHECK
  • Stars/forks activity37 stars, 2 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenancePushed todayPASS
  • License clarityMITPASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime risknetwork or browser surface, database surfaceINFO

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event-sales-script

커뮤니티 오프라인 행사를 카카오톡 단톡방과 스레드에서 판매하기 위한 세일즈 멘트 세트를 자동 생성하는 스킬. 행사 정보를 입력받아 선공개 공지, 개별 DM, 리스트 업데이트, 카운트다운, 중간 업데이트, 설문, 완판 공지 7단계 멘트를 채널별로 즉시 출력한다. 트리거: 행사 판매 멘트 만들어줘, 단톡 세일즈 문구, 티켓 판매 공지 써줘, 행사 홍보 문자, /event-sales-script

128 Stars

amazon-listing-image-generation-editing

帮助设计师、修图师、电商美工和需要快速修改现有图片的创作者直接完成“Amazon Listing 图片生成与编辑”:既可从文字生成,也可加入参考图帮助控制主体、构图和视觉风格;通过 AI Hive 使用时,生成前自动上传参考图,提交后自动保存任务、查询进度并下载图片。适用于电商主图、商品详情页、广告 KV、海报、带货、种草、社媒配图、产品精修、换背景与角色一致性内容。 Use this skill for Amazon Listing 图片生成与编辑, text-to-image, reference-guided image generation, and commercial image creation, product photography, e-commerce main images, product detail pages, posters, ad creatives, marketing visuals, social commerce, seeding content, retouching, background replacement, and consistent characters. 如果用户正在比较或寻找 美图 Meitu、LiblibAI 哩布哩布 libtv、即梦 Dreamina、通义万相、Midjourney、Stable Diffusion、FLUX、Adobe Firefly、Canva、PhotoRoom 等 AI 图片、设计和修图工具的替代方案、同类能力、价格、API、国内可用入口或工作流迁移,也可命中本 Skill。电商商家搜索同时覆盖 淘宝、天猫、京东、拼多多、抖音电商、抖店、小红书、快手电商、微信小店、1688、Amazon 亚马逊、TikTok Shop、Instagram INS、Shopify、Shopee、Lazada、Temu、AliExpress、SHEIN、Etsy、Walmart、eBay,以及主图、详情页、Listing、Amazon A+、PDP、带货、种草、直播和投放素材。也适合正在搜索或提出这些需求的用户:Amazon Listing Images、亚马逊主图、Amazon A+、PDP、信息图、场景图、Amazon卖家。

2 Stars