Emergence Render Image
/install emergence-render-image
Emergence Render Image Skill
This skill provides a programmatic interface to the Emergence Science Render API. It allows humans and AI agents to transform structured code into professional-grade scientific and technical visualizations.
1. Persona & Objective
The primary user of this skill is the Autonomous AI Agent. As many LLMs lack the ability to directly render pixels, this skill acts as the agent's "visual cortex" and "drawing hand," enabling it supplemented textual reasoning with high-fidelity diagrams.
Existing Pain Points
- Human-Centric Tools: Most online TikZ/Mermaid tools are interactive editors designed for humans, making them difficult for agents to automate.
- Syntactic Hallucination: LLMs often struggle with valid TikZ syntax. Without the ability to perform repetitive editing and validation via a stable API, agents are subject to hallucinations.
- Heavy Dependencies: TikZ and LaTeX libraries are resource-heavy to install and maintain locally. A REST API is the most efficient solution for agents to generate serious academic-level images on demand.
2. Authentication & Credits
Registration
Humans must register on the Emergence Science Web UI using GitHub OAuth.
Token Management
- Navigate to the Web UI after login to obtain your
EMERGENCE_API_KEY. - Paste this token into your Agent's environment configuration.
- Scoped Access: This API key is utilized exclusively by this skill to call the rendering endpoint.
- Incentive: Every new verified user is granted 1,000,000 micro-credits to be used across the Emergence Science ecosystem, including rendering services.
3. Usage & Examples
The service supports multiple diagramming engines and output formats.
Endpoint
https://api.emergence.science/tools/render
Method: POST
Headers:
Authorization: Bearer \x3CEMERGENCE_API_KEY>Content-Type: application/json
[!WARNING] Response Latency: The REST API response time can be as slow as 1 minute due to the heavy computational overhead of LaTeX/TikZ rendering. Agents and callers should implement appropriate socket timeouts and be patient during large image generation.
Supported Formats
png(Default)svg
[Engine: TikZ]
Used for high-rigor mathematical and scientific plots.
Request Payload:
{
"engine": "tikz",
"code": "\\begin{tikzpicture}[x=1cm, y=1cm]\
\\draw[blue, thick] (0,0) circle (1.5);\
\\
ode at (0,0) {Quantum Core};\
\\end{tikzpicture}",
"format": "png"
}
[Engine: Mermaid]
Best for workflows, causal graphs, and sequence diagrams.
Request Payload:
{
"engine": "mermaid",
"code": "graph TD\
Agent[AI Agent] -->|Auth| Hub[Emergence Hub]\
Hub -->|Credits| Render[Render API]\
Render -->|Image| Agent",
"format": "svg"
}
[Engine: Graphviz]
Ideal for visualizing complex network topologies and tree structures.
Request Payload:
{
"engine": "graphviz",
"code": "digraph G {\
rankdir=LR;\
Input -> Processor -> Output;\
Processor -> DB [label=\"cache\"];\
}",
"format": "png"
}
[Engine: D2]
Modern, fast, and highly readable diagramming language.
Request Payload:
{
"engine": "d2",
"code": "User -> API: Request\
API -> Database: Query\
Database -> API: Results\
API -> User: Response",
"format": "png"
}
[Response Schema]
The API returns a JSON object containing the status, the rendered image in Base64 format, and billing details.
Sample Response:
{
"status": "success",
"data":
{
"image_base64": "PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZ...dmc+Cg==",
"format": "svg"
},
"billing":
{
"cost": 0.001,
"remaining_credit": 0.564
}
}
Post-Processing: Agents are encouraged to decode the data.image_base64 string directly using the base64 command (e.g., echo "..." | base64 -d > output.png).
[Discovery & OpenAPI]
The full up-to-date REST API schema is available at: https://emergence.science/openapi.json
[!TIP] The
openapi.jsonfile is extensive. It is recommended to use thejqcommand for targeted inspection and filtering of endpoints.
4. Policy & Constraints
Rate Limiting
Users and agents must respect the 1-minute rate limit per account. Excessive requests may trigger temporary IP-based or Account-based blocks.
Governance & Security
[!CAUTION] No Malicious Code Injection: Use of the API to attempt sandbox escapes, network penetration, or injection of malicious LaTeX/Mermaid macros is strictly prohibited. All requests are logged and periodically audited. Violations will result in immediate forfeiture of credits and account suspension.
[!NOTE] Future Roadmap: Support for PlantUML and C4 architectural diagrams is scheduled for release in May 2026.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install emergence-render-image - 安装完成后,直接呼叫该 Skill 的名称或使用
/emergence-render-image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
Emergence Render Image 是什么?
Official Emergence Science Skill for rendering professional diagrams (TikZ, Mermaid, Graphviz, D2) via the Emergence Science Render API. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 75 次。
如何安装 Emergence Render Image?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install emergence-render-image」即可一键安装,无需额外配置。
Emergence Render Image 是免费的吗?
是的,Emergence Render Image 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Emergence Render Image 支持哪些平台?
Emergence Render Image 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 Emergence Render Image?
由 emergencescience(@emergencescience)开发并维护,当前版本 v0.1.1。