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Agent Skill · 图像处理

emergence-render-image(出现渲染图像)

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

emergence-render-image 是图像处理类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在图像处理类的 1,562 个 Skill 里排第 869。

总安装 2,421周安装 97GitHub Stars 公开资料未说明下载 784核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.1.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:emergence-render-image(出现渲染图像)
来源仓库:https://github.com/emergencescience/emergence-render-image
安装命令:
openclaw skills install emergence-render-image
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install emergence-render-image

这个 Skill 是做什么的

图像处理类 Skill 处理生成、裁剪、压缩和批量转换图片的任务。

官方 Emergence Science 技能,用于通过 Emergence Science Render API 渲染专业图表(TikZ、Mermaid、Graphviz、D2)。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install emergence-render-image;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

emergence-render-image 怎么安装?

在终端执行 openclaw skills install emergence-render-image。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

emergence-render-image 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

emergence-render-image 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

emergence-render-image 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

emergence-render-image 用的人多吗?

公开数据是累计安装 2,421 次,近一周安装 97 次,下载 784 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

emergence-render-image 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理图像处理类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

图像处理类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
ai-image-generation

AI 图像生成 · Codex、Claude

4,365,552382调用外部服务未明确有提醒
openai-image-gen

OpenAI 图像生成 · OpenClaw

1,906,88432需要联网MIT-0有提醒
image

图像 · OpenClaw

525,99926需要联网MIT-0通过
baoyu-cover-image

宝玉封面图 · Codex、Claude

474,24016,779只读MIT有提醒
baoyu-image-gen

宝玉图像生成器 · Codex、Claude

468,00716,778接触敏感数据MIT通过
emergence-render-image

出现渲染图像 · 本页

2,421—接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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 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:

{
  "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.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.

平台分布

OpenClaw

94.7%
按下载量换算742

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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