/install wangjipeng-diagram-to-code
Diagram To Code
Use when (1) user provides architecture diagram and needs infrastructure as code or component code.
Core Position
This skill solves the specific engineering problem of: user provides architecture diagram and needs infrastructure as code or component code
This skill is NOT:
- A general-purpose capability that activates on anything
- A replacement for manual human judgment
- A tool that stores state or remembers across sessions
This skill IS activated ONLY when the trigger conditions are explicitly met.
Modes
/diagram-to-code
Default mode. Performs the core task end-to-end.
When to use: User provides input matching the trigger conditions above.
Execution Steps
-
Receive diagram — User pastes an architecture diagram, flowchart, or infrastructure plan
- Accepted formats: Mermaid, PlantUML, draw.io XML, ASCII art, or a text description
- If the input is not recognizable as a diagram, state: "This skill converts architecture diagrams to infrastructure as code or component code. Please provide a diagram in Mermaid, PlantUML, or draw.io format."
-
Parse diagram structure — Identify the diagram elements:
- Detect nodes (services, components, databases, queues)
- Identify relationships (arrows, dependencies, data flows)
- Note labels, annotations, and any numeric values (latency, throughput)
- Determine the diagram type (architecture, flowchart, sequence, ER)
-
Map to target code form — Identify the appropriate output:
- Infrastructure diagrams → Terraform (.tf), Kubernetes YAML, or CloudFormation
- Flowcharts → state machine code, workflow scripts, or sequence diagrams
- Component diagrams → code structure (React components, Python classes)
- Network diagrams → config files or deployment manifests
-
Generate code — Produce the target format with the diagram's structure:
- Use correct syntax for the target platform (HCL for Terraform, YAML for K8s)
- Name resources based on node labels in the diagram
- Add comments linking each code block back to the diagram element
- Include realistic defaults for unstated properties
-
Deliver with validation — Return the code with a diagram-to-code mapping:
- List each diagram node and the corresponding code resource
- Note any assumptions made for unlabeled properties
- If multiple output formats are possible, state which was chosen and why
- Offer to refine specific components or add missing connections
Mandatory Rules
Do not
- Do not make up facts or claim actions were taken that were not
- Do not hardcode API keys — use
os.getenv("API_KEY")instead - Do not store sensitive user data beyond the current session
- Do not exceed token budget without warning the user first
- Do not activate for off-topic requests — return a brief decline message
Do
- Validate all inputs before acting
- Handle errors gracefully with actionable error messages
- Log actions taken for auditability
- State explicitly when you are uncertain or data is insufficient
Quality Bar
A good output:
- Solves exactly the problem described in the trigger conditions
- Provides actionable result in the expected format within 3 turns
- Handles error cases with specific guidance, not generic "try again"
- States assumptions explicitly when input is ambiguous
A bad output:
- Solves a different problem than the one triggered
- Provides a generic "I can't help with that" without explaining why
- Crashes, hangs, or returns malformed output on valid input
- Activates for off-topic requests (false positive)
Good vs. Bad Examples
| Scenario | Bad Output | Good Output |
|---|---|---|
| Trigger matched | "I can help with that." + no action | Correct transformation delivered in structured format |
| Invalid input | Crash or wrong result | "Missing required field: [X]. Please provide [Y]." |
| Ambiguous input | Guesses and might be wrong | States assumption and asks for confirmation |
| Off-topic request | Attempts to help anyway | "This skill activates when [trigger]. Please restate your request." |
References
references/— Detailed templates, schemas, and edge-case rules for this skill
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install wangjipeng-diagram-to-code - 安装完成后,直接呼叫该 Skill 的名称或使用
/wangjipeng-diagram-to-code触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
diagram-to-code 是什么?
Use when (1) user provides architecture diagram and needs infrastructure as code or component code. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 36 次。
如何安装 diagram-to-code?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install wangjipeng-diagram-to-code」即可一键安装,无需额外配置。
diagram-to-code 是免费的吗?
是的,diagram-to-code 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
diagram-to-code 支持哪些平台?
diagram-to-code 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 diagram-to-code?
由 王继鹏(@wangjipeng977)开发并维护,当前版本 v1.0.0。