Token导航 LogoToken导航TokenDH.com
开发需要联网github未标认证来源可访问许可证需确认审计异常

comfyui-workflow-buildercomfyui 工作流程构建器

Agent Skill

comfyui-workflow-builder 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

13,638

周安装

574

GitHub Stars

50

下载量

4,776
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:comfyui-workflow-builder(comfyui 工作流程构建器)
来源仓库:https://github.com/mckruz/comfyui-expert
仓库路径:skills/comfyui-workflow-builder
安装命令:
npx skills add https://github.com/mckruz/comfyui-expert --skill comfyui-workflow-builder
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/mckruz/comfyui-expert --skill comfyui-workflow-builder

简介

comfyui-workflow-builder 将自然语言请求转换为可执行的 ComfyUI 工作流 JSON。

  • 适用于图像、视频或音频输出的工作流自动生成与验证。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用该技能。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

ComfyUI Workflow Builder

Translates natural language requests into executable ComfyUI workflow JSON. Always validates against inventory before generating.

Workflow Generation Process

Step 1: Understand the Request

Parse the user's intent into:

  • Output type: Image, video, or audio
  • Source material: Text-only, reference image(s), existing video
  • Identity method: None, zero-shot (InstantID/PuLID), LoRA, Kontext
  • Quality level: Draft (fast iteration) vs production (maximum quality)
  • Special requirements: ControlNet, inpainting, upscaling, lip-sync

Step 2: Check Inventory

Read state/inventory.json to determine:

  • Available checkpoints → select best match for task
  • Available identity models → determine which methods are possible
  • Available ControlNet models → enable pose/depth control if available
  • Custom nodes installed → verify all required nodes exist
  • VRAM available → optimize settings accordingly

Step 3: Select Pipeline Pattern

Based on request + inventory, choose from:

PatternWhenKey Nodes
Text-to-ImageSimple generationCheckpoint → CLIP → KSampler → VAE
Identity-Preserved ImageCharacter consistency+ InstantID/PuLID/IP-Adapter
LoRA CharacterTrained character+ LoRA Loader
Image-to-Video (Wan)High-quality videoDiffusion Model → Wan I2V → Video Combine
Image-to-Video (AnimateDiff)Fast video, motion control+ AnimateDiff Loader + Motion LoRAs
Talking HeadCharacter speaksImage → Video → Voice → Lip-Sync
UpscaleEnhance resolutionImage → UltimateSDUpscale → Save
InpaintingEdit regionsImage + Mask → Inpaint Model → KSampler

Step 4: Generate Workflow JSON

ComfyUI workflow format:

{
  "{node_id}": {
    "class_type": "{NodeClassName}",
    "inputs": {
      "{param_name}": "{value}",
      "{connected_param}": ["{source_node_id}", {output_index}]
    }
  }
}

Rules:

  • Node IDs are strings (typically "1", "2", "3"...)
  • Connected inputs use array format: ["source_node_id", output_index]
  • Output index is 0-based integer
  • Filenames must match exactly what's in inventory
  • Seed values: use random large integer or fixed for reproducibility

Step 5: Validate

Before presenting to user:

  1. Every class_type exists in inventory's node list
  2. Every model filename exists in inventory's model list
  3. All required connections are present (no dangling inputs)
  4. VRAM estimate doesn't exceed available VRAM
  5. Resolution is compatible with chosen model (512 for SD1.5, 1024 for SDXL/FLUX)

Step 6: Output

If online mode: Queue via comfyui-api skill If offline mode: Save JSON to projects/{project}/workflows/ with descriptive name

Workflow Templates

Basic Text-to-Image (FLUX)

{
  "1": {
    "class_type": "LoadCheckpoint",
    "inputs": {"ckpt_name": "flux1-dev.safetensors"}
  },
  "2": {
    "class_type": "CLIPTextEncode",
    "inputs": {"text": "{positive_prompt}", "clip": ["1", 1]}
  },
  "3": {
    "class_type": "CLIPTextEncode",
    "inputs": {"text": "{negative_prompt}", "clip": ["1", 1]}
  },
  "4": {
    "class_type": "EmptyLatentImage",
    "inputs": {"width": 1024, "height": 1024, "batch_size": 1}
  },
  "5": {
    "class_type": "KSampler",
    "inputs": {
      "seed": 42,
      "steps": 25,
      "cfg": 3.5,
      "sampler_name": "euler",
      "scheduler": "normal",
      "denoise": 1.0,
      "model": ["1", 0],
      "positive": ["2", 0],
      "negative": ["3", 0],
      "latent_image": ["4", 0]
    }
  },
  "6": {
    "class_type": "VAEDecode",
    "inputs": {"samples": ["5", 0], "vae": ["1", 2]}
  },
  "7": {
    "class_type": "SaveImage",
    "inputs": {"filename_prefix": "output", "images": ["6", 0]}
  }
}

With Identity Preservation (InstantID + IP-Adapter)

Extends basic template by adding:

  • Load reference image node
  • InstantID Model Loader + Apply InstantID
  • IPAdapter Unified Loader + Apply IPAdapter
  • FaceDetailer post-processing

See references/workflows.md for complete node settings.

Video Generation (Wan I2V)

Uses different loader chain:

  • Load Diffusion Model (not LoadCheckpoint)
  • Wan I2V Conditioning
  • EmptySD3LatentImage (with frame count)
  • Video Combine (VHS)

See references/workflows.md Workflow 4 for complete settings.

VRAM Estimation

ComponentApproximate VRAM
FLUX FP1616GB
FLUX FP88GB
SDXL6GB
SD1.54GB
InstantID+4GB
IP-Adapter+2GB
ControlNet (each)+1.5GB
Wan 14B20GB
Wan 1.3B5GB
AnimateDiff+3GB
FaceDetailer+2GB

Common Mistakes to Avoid

  1. Wrong output index: CheckpointLoader outputs [model, clip, vae] at indices [0, 1, 2]
  2. CFG too high for InstantID: Use 4-5, not default 7-8
  3. Wrong resolution for model: FLUX/SDXL=1024, SD1.5=512
  4. Missing VAE: FLUX needs explicit VAE (ae.safetensors)
  5. Wrong model in wrong loader: Diffusion models need LoadDiffusionModel, not LoadCheckpoint

Reference Files

  • references/workflows.md - Detailed node-by-node templates
  • references/models.md - Model files and paths
  • references/prompt-templates.md - Model-specific prompts
  • state/inventory.json - Current inventory cache

适合场景

01

文本生成图片

02

图片风格化

03

产品图和创意图

04

需要 FLUX 模型时

能力概览

能力 1

调用 FLUX 图像模型

能力 2

支持文本生图和图像改写

能力 3

覆盖 LoRA 或风格适配

能力 4

适合创意视觉生成

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

32.91%
按下载量换算1,572

Claude

30.63%
按下载量换算1,463

Cursor

17.74%
按下载量换算847

Gemini CLI

10.16%
按下载量换算485

安全审计

Gen Agent Trust Hub

通过

Socket

未通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills