comfy-workflow-json

created	"2026-07-07T00:00:00.000Z"
modified	"2026-07-07T00:00:00.000Z"
reviewed	"2026-07-07T00:00:00.000Z"
name	comfy-workflow-json
description	Programmatically edit ComfyUI workflow JSON: edge-list link rebuilds, link-encoding schemas, preserving the extra block (App Mode config). Use when a workflow JSON is too large to hand-edit or a transform drops state.
allowed-tools	Bash, Read, Grep, Glob, Write, Edit

comfy-workflow-json

Workflow JSONs routinely exceed a Read tool's size limit (a moderately complex workflow can be 25k+ tokens). Don't chunk-read or hand-edit large workflow files — write a Python transform script that uses json.load / json.dump.

When to Use This Skill

Use this skill when...	Use instead when...
A workflow JSON is too large to hand-edit, or you're splicing/rewiring nodes programmatically	Auto-arranging node positions -> comfy-workflow-layout
A round-trip transform is silently dropping App Mode or canvas state	Deciding whether to use a subgraph/Blueprint/App Mode at all -> comfy-subgraphs-app-mode

Edge-list rebuild — mutate links without hand-surgery

In-place link surgery (patching the flat links array and every node's inputs[].link / outputs[].links by hand) is error-prone — the silent-failure mode is a link id present in one place but not the other. The robust alternative:

1. Extract edges to plain dicts: [{src, src_slot, dst, dst_slot, type}] from wf['links'].
2. Mutate that edge list (redirect an edge's dst to a new node, drop edges touching a deleted node, append new edges) and add/remove nodes from wf['nodes'].
3. Rebuild authoritatively: clear every node's inputs[].link → None and outputs[].links → [], then re-number all edges 1..N into wf['links'] and re-populate both endpoints' link refs from the edge list. Set last_link_id / last_node_id.

dst_slot indexes the node's inputs array (widget entries included); src_slot indexes outputs. New nodes get id = max(existing)+1. To insert node X between src and Target.model: find the edge with dst==Target, dst_slot==0, repoint its dst to X, then append X→Target.model. This keeps the socket-array / links-table invariant correct by construction.

Don't template off a live working JSON

A reference/comparison builder must clone a clean, stable template, not a workflow the user actively edits in the UI. A file you generated earlier can be silently reshaped between sessions (extra LoRAs, a swapped VAE-decode, a converted negative) — basing new variants on it inherits that drift and breaks structural assumptions a from-scratch builder made (e.g. a ConditioningZeroOut lookup can raise if the template gained unexpected nodes). Pick one canonical clean template, hardcode the per-variant deltas, and never read a user's live working copy as a template.

Running the workflow after editing it

To execute a workflow JSON headlessly (queue it, poll, collect outputs) without the browser, see the comfyui-pack-live-smoke skill's headless-API section. This is the fastest way to verify an edited or built workflow actually runs — the gold-standard check beyond JSON link-integrity validation.

Authoritative schema

The exact format ComfyUI validates against is the frontend's zod schema — there is no published JSON Schema. If uncertain about a field, extract the schema from the frontend's own sourcemap rather than guessing (see comfyui-node-authoring's sourcemap-verification recipe):

FE=<comfyui-root>/.venv/lib/python3.10/site-packages/comfyui_frontend_package/static/assets
python3 -c "import json,glob; m=json.load(open(glob.glob('$FE/api-*.js.map')[0])); i=m['sources'].index('../../src/platform/workflow/validation/schemas/workflowSchema.ts'); print(m['sourcesContent'][i])" > workflowSchema.ts

Re-extract after a frontend bump (the bundle hash changes) and keep the extracted file checked in with a provenance note, so uncertain transforms have a fast source of truth without re-extracting every time.

Schema facts that drive transforms:

• Two top-level versions. version: 0.4 → zComfyWorkflow; version: 1 → zComfyWorkflow1. They differ in link encoding and graph-state fields (see table below). A frontend can emit either — don't hardcode an assumption about which one you'll see.
• .passthrough() everywhere. Unknown keys are preserved, not rejected. json.load→mutate→json.dump is safe; the schema won't strip fields you don't touch (this is why preserving extra works for free).
• id/slot fields accept strings or ints (zNodeId, zSlotIndex). GroupNode hacks node ids to "<id>:<i>" strings, and subgraph slot ids are UUIDs. Don't assume integer node ids.
• widgets_values is array or object (zWidgetValues = array | record). Most core nodes use the positional array; some use a keyed record. Index-based widget edits break on the record form.

Link encoding by version

Where	Format
Top-level links, 0.4 (zComfyLink)	tuple [id, origin_id, origin_slot, target_id, target_slot, type]
Top-level links, v1 (zComfyLinkObject)	dict {id, origin_id, origin_slot, target_id, target_slot, type, parentId?}
Subgraph-internal links (any host version)	dict zComfyLinkObject — subgraph definitions always follow the v1 schema
floatingLinks	dict zComfyLinkObject

This is why a 0.4 workflow with subgraphs has tuple links at the top level but dict links inside definitions.subgraphs[]: the subgraph definition (zSubgraphDefinition) extends zComfyWorkflow1, so it carries v1 object-links regardless of the host workflow's version. v1 also replaces last_node_id/last_link_id with a state object ({lastNodeId, lastLinkId, lastGroupId, lastRerouteId}).

Subgraph format

Newer workflows wrap part of the graph in subgraphs (wf['definitions']['subgraphs'][i]). Each subgraph has its own nodes, inputs, outputs, and an internal links table whose format differs from the top-level:

Where	Link format
Top-level wf['links']	flat array [id, src, src_slot, dst, dst_slot, type]
Subgraph sg['links']	array of dicts {id, origin_id, origin_slot, target_id, target_slot, type}

When splicing a node into a subgraph, both the new node's input/output links arrays and the subgraph's internal links table must reference the new link ids. A link id present in a socket array but missing from the links table is the silent-failure mode — the workflow loads but the splice is invisible.

A subgraph published as a Blueprint (node-library reuse) is a separate concern from this on-disk shape: the blueprint itself lives server-side via the userdata API, while each placed instance is a plain embedded definitions.subgraphs[] copy like any other. Editing the JSON never touches the blueprint, and there is no live link back to it (instances are snapshots). See comfy-subgraphs-app-mode.

Preserve the extra block (App Mode config lives there)

Round-trip transforms must carry wf['extra'] through unchanged — it holds editor and feature state, not just cosmetics:

Key	What it is
extra.ds	canvas pan/zoom
extra.frontendVersion	authoring frontend version
extra.favoritedWidgets	pinned widgets
extra.linearMode / extra.linearData	App Mode input/output bindings + layout

json.load → mutate nodes → json.dump preserves extra for free. The failure mode is rebuilding a workflow from scratch in a builder script: the new dict has no extra, so any App Mode configuration the user set up in the UI is silently dropped and must be reconfigured. If a workflow has been App-Mode-configured, prefer an in-place transform over a from-scratch rebuild, or copy the old extra.linearMode/extra.linearData across. See comfy-subgraphs-app-mode for the App Mode feature itself.

Diagnosing a swapped-conditioning wiring bug

Symptom: a workflow generates outputs that look like the input image with only minor changes, even at denoise=1.0. With CFG-distilled/Lightning- style LoRAs (CFG=1.0) the symptom is especially common because the negative path is ignored, so an empty positive conditioning falls back to "reconstruct the reference image."

A real root cause found this way: the node titled positive-prompt and the node titled negative-prompt were wired into the sampler's negative and positive inputs respectively. Titles say one thing, links say the other.

Diagnostic recipe — JSON-only, no UI required:

import json
wf = json.load(open(path))
by_id = {n["id"]: n for n in wf["nodes"]}
ks = next(n for n in wf["nodes"] if n["type"] == "KSampler")  # or KSamplerAdvanced
pos_link = next(i for i in ks["inputs"] if i["name"] == "positive")["link"]
neg_link = next(i for i in ks["inputs"] if i["name"] == "negative")["link"]
src_of = lambda lid: next(L[1] for L in wf["links"] if L[0] == lid)
print("positive source:", by_id[src_of(pos_link)].get("title"))
print("negative source:", by_id[src_of(neg_link)].get("title"))

Fix: swap link source nodes in both the links table and the source nodes' outputs[0].links arrays — a 10-line transform once you know the link IDs. Same pattern applies to SamplerCustomAdvanced (via BasicGuider/CFGGuider) and any subgraph-internal sampler; the link-table layout is what matters, not which sampler family.

Naming conventions are per-install

Many installs adopt their own save-node filename_prefix convention (dated buckets, sampler/scheduler/seed tokens, per-workflow kebab-case node-title slugs) to keep output sortable and runs distinguishable. That convention — and any audit script enforcing it — is specific to how a given install organizes its output/ directory, not a portable ComfyUI fact; design one for your own install rather than importing someone else's verbatim.