image-gen

An image-generation workflow for creating pictures, posters, and illustrations from text prompts, including requests using Qwen-Image or compatible image APIs.

In plain words
What is it for?
Use it to generate images from a description and save the finished files in the workspace’s outputs folder.
Why use it?
It handles the steps of calling the image service, downloading the result, and saving it where the user can access it.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/xerrors/yuxi/image-gen
Any agent
npx skills add xerrors/Yuxi --skill image-gen
Clone the repo
git clone --depth 1 https://github.com/xerrors/Yuxi

Made for: Claude Code, Codex.

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 855 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.00855
Opus 5 $0.00024 $0.00428
Sonnet 5 $0.00010 $0.00171
Haiku 4.5 $0.00005 $0.00085

Measured 2d ago against content hash 667165f2a46d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

image-gen scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Sends data to an external URLmediumData exfiltration

A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.

response = requests.post( "https://api.siliconflow.cn/v1/images/generations",

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

response = requests.post(
backend/package/yuxi/agents/skills/buildin/image-gen/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

图片生成技能

当用户要求生成图片、海报、插画、文生图,或明确提到 Qwen-Image 时,使用此技能组织图片生成流程。

默认生成接口

默认使用 SiliconFlow 的 Qwen-Image 接口:

  • Endpoint: POST https://api.siliconflow.cn/v1/images/generations
  • Model: Qwen/Qwen-Image
  • 默认参数:
    • negative_prompt: ""
    • num_inference_steps: 20
    • guidance_scale: 7.5

调用外部接口时,必须在 Agent 沙盒执行环境中读取 SILICONFLOW_API_KEY。不要依赖后端进程环境变量。

操作流程

  1. 明确用户要生成的图片内容、风格、尺寸或约束;信息不足但不影响生成时,使用合理默认值,不要反复追问。
  2. 使用可用的执行工具在沙盒中运行脚本,调用图片生成接口,传入用户需求整理后的 prompt,并按需传入 negative_promptnum_inference_stepsguidance_scale
  3. 从生成接口响应中读取图片地址,默认路径为 images[0].url
  4. 在同一个沙盒脚本中用 Authorization: Bearer $SILICONFLOW_API_KEY 下载该图片地址;如果接口直接返回 base64,则直接解码保存。
  5. 将最终图片保存到当前 Workdir 的 outputs/ 下,例如 outputs/generated-image.png
  6. 调用 present_artifacts,传入保存后的 outputs 虚拟路径,让前端展示图片产物。
  7. 最终回复简要说明图片已生成,不要把外部临时 URL 当作最终结果展示。

脚本示例

可根据用户需求调整 prompt 和输出文件名:

import os
import requests
from pathlib import Path

api_key = os.environ["SILICONFLOW_API_KEY"]
prompt = "根据用户需求整理后的图片提示词"

response = requests.post(
    "https://api.siliconflow.cn/v1/images/generations",
    headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
    json={
        "model": "Qwen/Qwen-Image",
        "prompt": prompt,
        "negative_prompt": "",
        "num_inference_steps": 20,
        "guidance_scale": 7.5,
    },
    timeout=120,
)
response.raise_for_status()
image_url = response.json()["images"][0]["url"]

image_response = requests.get(
    image_url,
    headers={"Authorization": f"Bearer {api_key}"},
    timeout=120,
)
image_response.raise_for_status()

output_path = Path("outputs/generated-image.png")
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_bytes(image_response.content)
print(output_path.as_posix())

多模型扩展

如果用户指定其它图片生成模型或兼容接口,可以按该接口的协议先生成图片。只要最终拿到图片 bytes 或 base64,就保存到当前 Workdir 的 outputs/,再调用 present_artifacts 展示。

关键约束

  • 不要把外部生成接口返回的临时 URL 当作最终结果直接展示给用户。
  • 不要调用后端 MinIO 上传工具;图片生成和下载都应在沙盒内完成。
  • 如果 SILICONFLOW_API_KEY 缺失,应明确提示用户需要在 Agent 沙盒环境变量中配置。
  • 保存到 outputs 后必须调用 present_artifacts,否则前端不会自动展示生成图片。

Read the full file on GitHub · 83 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 83 lines · 48 tokens per session scan B 667165f2a46d

Subscribe to this mod's changes

image-gen is a skill published in the GitHub repository xerrors/Yuxi (6,606 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 855 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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