Skill: emergencescience/emergence-render-image (version 0.1.1)

--- name: emergence-render-image title: Emergence Render Image description: Official Emergence Science Skill for rendering professional diagrams (TikZ, Mermaid, Graphviz, D2) via the Emergence Science Render API. version: 0.1.1 homepage: https://github.com/emergencescience/emergence-render-image repository: https://github.com/emergencescience/emergence-render-image tags: [visualization, tikz, mermaid, graphviz, d2, emergence-science, agent-tools] metadata: clawdbot: requires: env: ["EMERGENCE_API_KEY"] primaryEnv: "EMERGENCE_API_KEY" --- # 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](https://emergence.science/) using **GitHub OAuth**. ### Token Management 1. Navigate to the Web UI after login to obtain your `EMERGENCE_API_KEY`. 2. Paste this token into your Agent's environment configuration. 3. **Scoped Access**: This API key is utilized exclusively by this skill to call the rendering endpoint. 4. **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 <EMERGENCE_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:** ```json { "engine": "tikz", "code": "\\begin{tikzpicture}[x=1cm, y=1cm]\n\\draw[blue, thick] (0,0) circle (1.5);\n\\node at (0,0) {Quantum Core};\n\\end{tikzpicture}", "format": "png" } ``` --- ### [Engine: Mermaid] Best for workflows, causal graphs, and sequence diagrams. **Request Payload:** ```json { "engine": "mermaid", "code": "graph TD\n Agent[AI Agent] -->|Auth| Hub[Emergence Hub]\n Hub -->|Credits| Render[Render API]\n Render -->|Image| Agent", "format": "svg" } ``` --- ### [Engine: Graphviz] Ideal for visualizing complex network topologies and tree structures. **Request Payload:** ```json { "engine": "graphviz", "code": "digraph G {\n rankdir=LR;\n Input -> Processor -> Output;\n Processor -> DB [label=\"cache\"];\n}", "format": "png" } ``` --- ### [Engine: D2] Modern, fast, and highly readable diagramming language. **Request Payload:** ```json { "engine": "d2", "code": "User -> API: Request\nAPI -> Database: Query\nDatabase -> API: Results\nAPI -> 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:** ```json { "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.json` file is extensive. It is recommended to use the `jq` command 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**.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)