Details ComfyUI API workflow JSON format, node connections, class types, and tools like analyze_workflow for inspecting, loading, and saving workflows.
From comfynpx claudepluginhub artokun/comfyui-mcp --plugin comfyThis skill uses the workspace's default tool permissions.
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ComfyUI workflows are JSON objects mapping string node IDs to node definitions:
{
"1": {
"class_type": "CheckpointLoaderSimple",
"inputs": { "ckpt_name": "sd_xl_base_1.0.safetensors" },
"_meta": { "title": "Load Checkpoint" }
},
"2": {
"class_type": "CLIPTextEncode",
"inputs": { "text": "a cat", "clip": ["1", 1] },
"_meta": { "title": "Positive Prompt" }
}
}
"1", "2", etc.)class_type is the exact Python class name of the nodeinputs contains both widget values (scalars) and connections (arrays)["sourceNodeId", outputIndex] — a 2-element array where:
output list (0-based)_meta is optional, used for display titles only"model": ["1", 0] // Connect to node 1's first output (MODEL)
"clip": ["1", 1] // Connect to node 1's second output (CLIP)
"vae": ["1", 2] // Connect to node 1's third output (VAE)
"positive": ["2", 0] // Connect to node 2's first output (CONDITIONING)
"samples": ["5", 0] // Connect to node 5's first output (LATENT)
"images": ["6", 0] // Connect to node 6's first output (IMAGE)
{ "1": { class_type, inputs }, "2": { ... } }{ "nodes": [...], "links": [...] } — includes layout positions, visual metadataget_workflow defaults to format="api" which auto-converts saved UI-format workflows to compact API format_meta.mode: "muted" — these are inactive but visible for understanding the workflow_meta.title and Constant key for tracing data flowanalyze_workflow(filename) — use this first to understand any saved workflow. Returns a structured text summary with sections, node IDs, key settings, virtual wires, and connection graph. No raw JSON — just what you need to reason about the workflow. Supports views: summary (default), overview (mermaid), detail (section mermaid), list, flat.list_workflows — list all saved workflows in ComfyUI's user libraryget_workflow(filename) — load raw workflow JSON. Only use when you need the actual JSON for enqueue_workflow, modify_workflow, or save_workflow. Use analyze_workflow instead for understanding.save_workflow(filename, workflow) — save a workflow to the user libraryComfyUI nodes pass typed data through connections:
| Type | Description | Common Source |
|---|---|---|
MODEL | Diffusion model weights | CheckpointLoaderSimple (output 0) |
CLIP | Text encoder | CheckpointLoaderSimple (output 1) |
VAE | Variational autoencoder | CheckpointLoaderSimple (output 2) |
CONDITIONING | Encoded text prompt | CLIPTextEncode (output 0) |
LATENT | Latent space tensor | EmptyLatentImage, KSampler, VAEEncode |
IMAGE | Pixel image tensor (BHWC) | VAEDecode, LoadImage, SaveImage |
MASK | Single-channel mask | LoadImage (output 1) |
UPSCALE_MODEL | Upscaling model | UpscaleModelLoader |
CheckpointLoaderSimple → MODEL, CLIP, VAE
├─ CLIP → CLIPTextEncode (positive) → CONDITIONING
├─ CLIP → CLIPTextEncode (negative) → CONDITIONING
│
EmptyLatentImage → LATENT
│
KSampler (model, positive, negative, latent_image) → LATENT
│
VAEDecode (samples, vae) → IMAGE
│
SaveImage (images)
Node IDs typically: 1=Checkpoint, 2=Positive, 3=Negative, 4=EmptyLatent, 5=KSampler, 6=VAEDecode, 7=SaveImage
Same as txt2img but replace EmptyLatentImage with:
LoadImage → IMAGE
VAEEncode (pixels, vae) → LATENT → KSampler.latent_image
Set KSampler.denoise to 0.5–0.8 (lower = closer to input image).
LoadImage → IMAGE
UpscaleModelLoader → UPSCALE_MODEL
ImageUpscaleWithModel (upscale_model, image) → IMAGE
SaveImage (images)
LoadImage (image) → IMAGE → VAEEncode → LATENT
LoadImage (mask) → MASK
SetLatentNoiseMask (samples, mask) → LATENT → KSampler.latent_image
create_workflow with template "txt2img" and your paramsenqueue_workflow with the returned JSON — returns prompt_id immediatelyget_job_status with the prompt_id until done is truelist_output_images (limit 1) to find the generated image, then Read to display itget_node_info — query what nodes are available and their schemasmodify_workflow — patch an existing workflow (set_input, add_node, remove_node, connect, insert_between)visualize_workflow — see a workflow as a mermaid diagramvisualize_workflow — workflow JSON → mermaid diagrammermaid_to_workflow — mermaid diagram → workflow JSON (uses /object_info for schema resolution)list_local_models — see what's installedsearch_models — find models on HuggingFacedownload_model — download to ComfyUI's models directoryImportant: Never ask the user to manually download models. If a required model is missing, proactively search for it and download it yourself:
list_local_models firstsearch_models or CivitAI via their REST APIdownload_model to install it directly to the correct subfolderCivitAI API (when CIVITAI_API_TOKEN env var is available):
GET https://civitai.com/api/v1/models?query={query}&types=Checkpoint&sort=Most+Downloaded&limit=5GET https://civitai.com/api/v1/models/{modelId}GET https://civitai.com/api/download/models/{modelVersionId}?token={token}CivitAI is preferred for fine-tuned models, community-rated checkpoints, and specialized LoRAs. HuggingFace is preferred for official/base models (SDXL, Flux, SD 1.5).
search_custom_nodes — search the ComfyUI Registryget_node_pack_details — get details about a specific packgenerate_node_skill — auto-generate a skill file for a node packenqueue_workflow submits to ComfyUI's queue and returns prompt_id + queue position immediately. It does NOT block.
After enqueuing one or more workflows, use a background Bash task to monitor progress silently:
# Single job
Bash(run_in_background: true):
node "${CLAUDE_PLUGIN_ROOT}/scripts/monitor-progress.mjs" <prompt_id>
# Multiple jobs (batch)
Bash(run_in_background: true):
node "${CLAUDE_PLUGIN_ROOT}/scripts/monitor-progress.mjs" <id1> <id2> <id3>
The script connects to ComfyUI's WebSocket and reports:
KSampler step 12/20 (60%))Standard generation pattern:
create_workflow or build workflow JSON + enqueue_workflow (repeat for batch)list_output_images or Read to display the generated imagesDo NOT poll get_job_status in a loop. The background monitor replaces polling entirely.
Fallback: If the monitor script is unavailable, use get_job_status to poll until done is true.
get_queue — shows running/pending job counts and prompt_idsget_job_status — check if a specific prompt_id is running, pending, or donecancel_job — interrupt a running job (pass optional prompt_id to target a specific one)cancel_queued_job — remove a specific pending job from the queue by prompt_idclear_queue — remove all pending jobs (does NOT stop the currently running job)When to use queue tools:
get_job_status for a quick boolean check (prefer background monitor for ongoing tracking)cancel_job stops what's running now; cancel_queued_job removes a pending oneclear_queue then optionally cancel_jobget_system_stats — GPU, VRAM, Python version, OS detailsget_queue — see running/pending jobs (also listed above under Queue Management)When ComfyUI is unresponsive or crashed:
get_system_stats — if it fails, ComfyUI is downrestart_comfyui to restart it (preserves launch args from prior stop_comfyui)start_comfyui or ask the user to start it manuallyWhen a job appears hung (monitor shows [STALL]):
get_system_stats — look at VRAM usage (OOM causes hangs)cancel_job to interrupt the stuck jobrestart_comfyui to force-restartclear_vram after restart to free GPU memory before retrying| Parameter | Type | Common Values |
|---|---|---|
seed | int | Random (0 to 2^48). Omit to auto-randomize. |
steps | int | 20 (standard), 4-8 (turbo/lightning models) |
cfg | float | 7-8 (SD 1.5/SDXL), 1.0 (Flux), 3.5 (turbo) |
sampler_name | string | "euler", "euler_ancestral", "dpmpp_2m", "dpmpp_sde" |
scheduler | string | "normal", "karras", "sgm_uniform" |
denoise | float | 1.0 (txt2img), 0.5-0.8 (img2img), 0.75-0.9 (inpaint) |
The visualize_workflow tool produces mermaid flowcharts with:
loading, conditioning, sampling, image, output-->|MODEL|, -->|CLIP|, -->|LATENT|, etc.LR (left-to-right) by default, TB (top-to-bottom) for large workflowsThe mermaid_to_workflow tool parses mermaid back into workflow JSON, using connection type labels to resolve the correct input/output slots via /object_info schemas.
["1", 0] not [1, 0] — node IDs are strings{ nodes: [], links: [] } — use API formatget_node_infoenqueue_workflow randomizes seeds by default unless disable_random_seed: true