Agent skill

Comfyui Topology Viz

by automateyournetwork in automateyournetwork/netclaw

Turn a network topology into one stylized, AI-generated still image via a self-hosted ComfyUI instance — reuses the same topology model as threejs-network-viz (any of 8 topology-source integrations…

Apache-2.0Auto-check: notesAI & LLM Engineering

Install Comfyui Topology Viz

skills CLI
$ npx skills add automateyournetwork/netclaw --skill comfyui-topology-viz -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install automateyournetwork/netclaw comfyui-topology-viz --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/automateyournetwork/netclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/workspace/skills/comfyui-topology-viz .claude/skills/comfyui-topology-viz && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
comfyui-topology-viz
GitHub stars
676
Token cost
~3.9k tokens
SKILL.md length
1,872 words
Files
13
Skills in repo
120
Repo updated
First seen
Licence
Apache-2.0

At a glance

Turn a network topology into one stylized, AI-generated still image via a self-hosted ComfyUI instance — reuses the same topology model as threejs-network-viz (any of 8 topology-source integrations…

  • The operator asks for a stylized
  • SKILL.md covers Overview, Prerequisites, Natural Language Commands and If you get "no usable model…, plus 5 more sections
  • Runs Python scripts from its folder; calls npm
  • AI-generated image/picture/illustration of a network topology

What it does

Comfyui Topology Viz is an agent skill from automateyournetwork/netclaw. Turn a network topology into one stylized, AI-generated still image via a self-hosted ComfyUI instance — reuses the same topology model as threejs-network-viz (any of 8 topology-source integrations, or a freeform description). Use when the operator asks for a stylized, flashy, or AI-generated image/picture/illustration of a network topology. Stills only — no video/animation.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files (for example `__init__.py`, `comfyui_client.py` and `federated_generation.py`).

It sits in AI & LLM Engineering, covering Diffusion and image models and 3D graphics and WebGL. It works with ComfyUI, Three.js and Model Context Protocol. The repository describes itself as: An AI agent that claws through your network. The licence is Apache-2.0.

When your agent uses it

  • The operator asks for a stylized
  • AI-generated image/picture/illustration of a network topology

Example prompts

  • “/comfyui-topology-viz”

Requirements

  • Python 3
  • Node.js

What it can do on your machine

Read from SKILL.md and the folder at commit aa90e7d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Comfyui Topology Viz loads about 3.9k tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 1,872 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~100
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:39
    - **`COMFYUI_URL`** in `.env` (see `.env.example`) — your ComfyUI instance's endpoint, e.g.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from automateyournetwork/netclaw at commit aa90e7d, republished under its Apache-2.0 licence (© automateyournetwork). 1,872 words, ~3,903 tokens.

Download SKILL.mdSave it as .claude/skills/comfyui-topology-viz/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
comfyui-topology-viz
description
Turn a network topology into one stylized, AI-generated still image via a self-hosted ComfyUI instance — reuses the same topology model as threejs-network-viz (any of 8 topology-source integrations, or a freeform description). Use when the operator asks for a stylized, flashy, or AI-generated image/picture/illustration of a network topology. Stills only — no video/animation.
license
Apache-2.0
user-invocable
true

ComfyUI Network Topology Visualization Skill

Version: 1.0.0 Feature: 120-comfyui-topology-viz Status: Active

Overview

Turns a network topology into one stylized, AI-generated still image via a self-hosted ComfyUI instance — a different rendering path from NetClaw's existing three.js (threejs-network-viz), Blender (blender-3d-viz), and UE5 (ue5-network-viz) skills, which all produce navigable 3D scenes. This skill produces one flat, "flashy" illustration per request instead, reusing the same canonical topology model spec 046's three.js skill already assembles from any of NetClaw's existing topology-source integrations, or a freeform description.

v1 scope is stills only. Video (traffic flybys, packet-tracing animations) and stylized test-result cards are explicitly out of scope for this version — likely follow-on specs, not built here (spec.md FR-016).

Prerequisites

Required
  • A separately running ComfyUI instance — this skill does not install or manage ComfyUI itself, only connects to one you already have running. Get ComfyUI from https://github.com/comfyanonymous/ComfyUI or the ComfyUI Desktop app.
  • COMFYUI_URL in .env (see .env.example) — your ComfyUI instance's endpoint, e.g. http://127.0.0.1:8000. Required; there is no assumed default (FR-005), because comfyui-mcp's own built-in defaults (8000 for ComfyUI Desktop, 8188 for a manual install) are easy to mix up with your actual instance's port.
  • At least one image-generation checkpoint installed in ComfyUI (Stable Diffusion 1.5, SDXL, or Flux) — see "If you get 'no usable model found'" below if you haven't installed one yet.
  • The vendored comfyui-mcp server (mcp-servers/comfyui-mcp/), cloned and built via npm install && npm run build, registered as comfyui-mcp in config/openclaw.json.
Topology sources

Any of NetClaw's existing topology-of-record or lab-emulation integrations — Cisco Modeling Labs, GNS3, containerlab, EVE-NG, Nautobot, NetBox/Infrahub, IP Fabric, or Forward Networks — or a freeform plain-language description requiring no live source at all.

Natural Language Commands

Render a live topology as a stylized still image (User Story 1)
"Give me a stylized AI image of the CML lab topology"
"Render my GNS3 project as a ComfyUI image"
"Make a flashy image of this network"

NetClaw retrieves the topology from the named (or clarified) source, checks what image-generation checkpoints ComfyUI has installed, generates one image, and tells you where it was saved: workspace/output/comfyui-topology-viz/comfyui-<timestamp>-<request-id>.png (plus a sidecar .json recording the prompt and checkpoint used) — and which checkpoint was used (FR-006a).

Sketch a topology without a live source (User Story 3)
"Make a flashy image of this topology: a router r1 connected to a switch sw1"

Same generation pipeline as a live-sourced request, just parsed from your plain-language description instead.

If something isn't available (User Story 2)

Every failure NetClaw can detect gets a specific, distinct message — never a hang or a generic error:

ConditionWhat NetClaw tells you
ComfyUI unreachable at the configured COMFYUI_URL (or comfyui-mcp silently connected to a different instance instead — see Known Limitations)The configured endpoint could not be reached
ComfyUI reachable, but no installed checkpoint is suitable for image generationWhat kind of model to install (SD1.5/SDXL/Flux) and where
ComfyUI itself reports the generation job failedThe generation failed — as distinct from a reachability or missing-model problem
A generation is already runningWait for it to finish (or fail) and ask again — this skill runs one job at a time, never two concurrently
A named topology source is unreachableThe sourcing failure, distinct from anything ComfyUI-side
The topology has zero devicesNothing to visualize — no generation is attempted

There is no NetClaw-imposed timeout on generation itself — real GPU image-generation time varies with model, workflow, and hardware, so a submitted job is tracked to ComfyUI's own completed/failed status rather than being given up on early.

If you get "no usable model found"

Expected on a fresh ComfyUI install with no checkpoints downloaded yet — not a NetClaw bug. Install at least one Stable Diffusion 1.5, SDXL, or Flux checkpoint into ComfyUI's models/checkpoints directory (ComfyUI Manager, or a manual download into that folder), then ask again.

Architecture

ModuleResponsibility
topology_model.pyCanonical Device/Interface/Link/TopologySnapshot types (ported from threejs-network-viz, trimmed of 3D-only concepts)
materials.pyHostname-based device-role inference (ported, trimmed of color tables)
sources.pyOne adapter per topology source (8 live integrations + freeform), plus source-selection disambiguation (ported)
generation_model.pyNEW entities: GenerationRequest, ModelAvailabilityCheck, GeneratedImage, the six-kind GenerationFailure taxonomy
comfyui_client.pyMCP stdio client wrapper around the vendored comfyui-mcp server — discovery, template selection, async submission, no-timeout polling, plus the ControlNet workflow builder and image upload
prompt_builder.pyTopologySnapshot → a bounded-length, role/count-summarized generation prompt
topology_renderer.pyDeterministic (NOT AI) box/line structure diagram, fed to ComfyUI's Canny node as ControlNet conditioning — the structural-accuracy pipeline (research.md §10)
label_overlay.pyBurns real, correct hostname labels onto the completed generation deterministically — Canny-conditioned text is too lossy for Flux to reproduce reliably (research.md §10)
generation.pyOrchestrates the full call sequence and every failure classification; picks the structural ControlNet path when available, falls back to plain txt2img otherwise; enforces the single-in-flight-job guard — unmodified since spec 120 (spec 121 FR-012)
output.pyOverlays labels (if structural path used) and copies the completed image into workspace/output/comfyui-topology-viz/ with a timestamped name + sidecar JSON, never overwriting — unmodified since spec 120
federated_generation.pyNEW (spec 121) — the actual entry point __init__.py now calls. Routes each request between the federated path (below) and generation.run_generation()'s existing pipeline (called as-is, never modified)

See contracts/comfyui-generation-contract.md for spec 120's exact fallback call sequence, and specs/121-federated-topology-viz/contracts/ for the two new federated-stage tool contracts.

The federated path (spec 121)

Same entry point, no new command — every "give me a stylized image" request goes through this skill exactly as before; which path actually produced the delivered image is now visible in the response as generation_path:

generation_pathMeaning
federatedBoth stages ran on an operator-configured federation member (e.g. <your-risk>/viz): a deterministic, correct-by-construction diagram (real role icons, real labels, real connections — no diffusion model involved), then a diffusion image-edit pass that restyled it without altering structure. This is the strongest correctness guarantee this skill can offer (spec 121 SC-001).
federated_partialThe structural diagram (correct, unstyled) was produced on the configured federation member, but the styling stage failed or that half of the member was unreachable — you still get the correct diagram, not nothing, with reason telling you styling didn't complete.
fallbackSpec 120's original Flux+ControlNet+Canny pipeline (unchanged) — used for a freeform request (no real device data for the structural stage to work from) or when the configured federation member itself is unreachable. reason says which.

Both new stages run as separate MCP servers (mcp-servers/topology-diagram-mcp/, mcp-servers/image-style-mcp/) invoked from Border via n2n/tools/call on the live your configured federation member — Border never renders or diffuses anything itself for this path (FR-005). See specs/121-federated-topology-viz/research.md for the full design, including four real gaps found and fixed in the shared federation infrastructure itself (R10) to make this actually work — this was the first working internal n2n/tools/call in the codebase.

If your viz federation member is down: systemctl --user start netclaw-member-<your-risk>-viz.service (service name derived from your own risk/member name, per your N2N federation setup) (it's enabled, so a host reboot brings it back automatically).

Show full SKILL.md (796 more words)Show less

Two generation paths (spec 120's fallback pipeline, used when the federated path isn't)

  • Structural (preferred, when Flux + a ControlNet are installed): the topology is rendered as a plain geometric box/line diagram, ComfyUI's Canny node extracts edges from it, and Flux paints over those edges — the generated image's structure (which device connects to which) is guaranteed accurate because it comes from deterministic code, not the diffusion model. Real hostname labels are overlaid afterward, also deterministically. Verified end-to-end (2026-08-28): exactly the right devices, exactly the right connections, correct legible labels.
  • Plain txt2img (fallback): used only when the ControlNet pipeline's models aren't all installed. Generates from a text-only prompt with no structural guarantee — the diffusion model is free-associating from a description, not reproducing an accurate diagram. Verified working (produces a real image) but visually confirmed by the user to not resemble the actual topology.

Environment Variables

VariableRequiredPurpose
COMFYUI_URLYesEndpoint of your own already-running ComfyUI instance

Known Limitations

  • Stills only (v1). No video/animation output, no stylized test-result cards — see Overview.
  • One job at a time. A second request while one is generating is rejected outright, not queued.
  • comfyui-mcp can silently substitute a different ComfyUI instance than the one configured. Found live during implementation: if the configured COMFYUI_URL is unreachable, comfyui-mcp falls back to port-scanning common local ports and connects to whatever ComfyUI it finds there instead of failing — even for a completely non-routable configured host. comfyui_client.py guards against this by verifying the response's comfyuiUrl/discoverySource actually match what was configured, treating a mismatch as backend_unreachable rather than silently generating against the wrong instance. See research.md §8. If you run more than one ComfyUI instance on the same network, double-check COMFYUI_URL is exactly right.
  • comfyui-mcp's own npm audit reports 10 vulnerabilities (1 low, 2 moderate, 7 high) in transitive dependencies it bundles for its own internal HTTP/WebSocket handling (hono, path-to-regexp, qs, sharp, ws) — all in libraries used for comfyui-mcp's own internal serving/media-processing, not exposed to this skill's stdio-only, sandboxed usage. Tracked as a non-blocking follow-up, matching the same treatment sketchfab-mcp-server's own audit findings received in spec 046 — do not run npm audit fix --force (it force-upgrades sharp with a breaking change) without testing the server still builds and runs afterward.
  • comfyui-mcp's own task tracker (get_task_result/get_task/list_tasks) is unreliable — do not poll it. Live-verified: it got permanently stuck reporting {"status": "working"} for a job ComfyUI itself had already completed successfully ~19 seconds earlier; its WebSocket completion listener silently failed to update. Completion is instead tracked by polling ComfyUI's own /history/{promptId} endpoint directly (comfyui_client.get_prompt_history()), and the finished image is downloaded straight from ComfyUI's own /view endpoint — neither depends on anything comfyui-mcp reports about task status or file location. See research.md §9. get_task_result is kept in comfyui_client.py for diagnostics only.
  • A stdio teardown race in our own client, not comfyui-mcp, was also found and fixed: calling run_workflow intermittently raised anyio.BrokenResourceError even though the job had actually submitted and completed successfully every time (confirmed against ComfyUI's own history) — a race between trailing stdio traffic and our client's async with teardown. Fixed by capturing the result before the context managers close (research.md §9).
  • Verified end-to-end, plain path (2026-08-27): a real freeform topology produced a genuine 512×512 PNG in ~21.5 seconds using sd_xl_base_1.0.safetensors, correctly attributed in both the returned path and the sidecar JSON — but visually confirmed not to resemble the actual topology (abstract line-art, no real structure).
  • sources.from_freeform() mis-parsed connector clauses with inline role declarations. "core1 connects to a switch called sw1" took the article "a" as the device name instead of "sw1" — creating a phantom device and leaving the real one disconnected. Invisible in the plain path (which never exposes exact link structure in its prompt) but exposed immediately once the structural renderer made the parsed graph directly visible. Fixed in sources.py; the identical bug still exists in threejs-network-viz/sources.py (this was ported from there) but was left untouched per FR-014. See research.md §10.
  • Canny-edge text reconstruction is unreliable. Baking hostnames into the structure image and relying on Flux to reproduce them through Canny conditioning produced garbled nonsense, not real text. Fixed by never asking the diffusion model to render text at all — see the two generation paths section above and label_overlay.py. See research.md §10.
  • Verified end-to-end, structural path (2026-08-28): the same freeform topology produced a genuinely correct diagram in 41.9s — exactly 3 devices, the real core1↔sw1↔fw1 chain, correct legible labels. Remaining imperfections are cosmetic (generic device icons rather than role-specific ones, decorative hallucinated background clutter, thin/dashed rather than "glowing" connection lines) — prompt-tuning opportunities, not correctness bugs.
  • ~25GB of Flux/ControlNet models installed on the ComfyUI host for the structural path — see specs/120-comfyui-topology-viz/model-inventory.md for the full list and cleanup guidance if disk space is needed back.

See specs/120-comfyui-topology-viz/tasks.md for the full implementation history and research.md for the technical decisions and live findings behind this skill's design.

© automateyournetwork, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files in workspace/skills/comfyui-topology-viz of automateyournetwork/netclaw.

  • SKILL.md
  • __init__.py
  • comfyui_client.py
  • federated_generation.py
  • generation.py
  • generation_model.py
  • label_overlay.py
  • materials.py
  • output.py
  • prompt_builder.py
  • sources.py
  • topology_model.py
  • topology_renderer.py

Open the folder on GitHubat commit aa90e7d

Compare with similar skills

Comfyui Topology Viz next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Comfyui Topology Viz compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Comfyui Topology Viz this skillautomateyournetwork/netclaw676—~3.9kAutomated safety check: NotesApache-2.0
Comfyui Skill OpenclawHuangYuChuh/ComfyUI_Skills_OpenClaw413—~2.7kAutomated safety check: PassApache-2.0
ComfyUI Custom Node BuilderConstantineB6/comfy-pilot230—~897Automated safety check: PassMIT
Civitaiartokun/comfyui-mcp803—~1.1kAutomated safety check: PassMIT
Setupguaardvark/guaardvark258—~1.2kAutomated safety check: PassMIT
Installer Packsartokun/comfyui-mcp803—~952Automated safety check: PassMIT

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Questions about Comfyui Topology Viz

What does Comfyui Topology Viz do?

Turn a network topology into one stylized, AI-generated still image via a self-hosted ComfyUI instance — reuses the same topology model as threejs-network-viz (any of 8 topology-source integrations…. Comfyui Topology Viz is an agent skill from automateyournetwork/netclaw. Turn a network topology into one stylized, AI-generated still image via a self-hosted ComfyUI instance — reuses the same topology model as threejs-network-viz (any of 8 topology-source integrations, or a freeform description).

When should I use Comfyui Topology Viz?

Comfyui Topology Viz fits situations like: the operator asks for a stylized; AI-generated image/picture/illustration of a network topology.

How do I install Comfyui Topology Viz in Claude Code?

Run `npx skills add automateyournetwork/netclaw --skill comfyui-topology-viz -a claude-code`. Or copy the skill folder (workspace/skills/comfyui-topology-viz in automateyournetwork/netclaw) into .claude/skills/comfyui-topology-viz in your project. Claude Code loads it when a task matches its description.

How do I install Comfyui Topology Viz in Codex?

Run `npx skills add automateyournetwork/netclaw --skill comfyui-topology-viz -a codex`. Or copy the skill folder (workspace/skills/comfyui-topology-viz in automateyournetwork/netclaw) into .agents/skills/comfyui-topology-viz in your project. Codex loads it when a task matches its description.

Can I use Comfyui Topology Viz in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add automateyournetwork/netclaw --skill comfyui-topology-viz -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/comfyui-topology-viz, .gemini/skills/comfyui-topology-viz, .github/skills/comfyui-topology-viz and .opencode/skills/comfyui-topology-viz in your project.

What does Comfyui Topology Viz need to run?

Going by SKILL.md and its folder, Comfyui Topology Viz needs Python for the scripts in its folder and the command-line tools its instructions call (npm). Our summary lists: Python 3; Node.js.

Does Comfyui Topology Viz access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Comfyui Topology Viz safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Comfyui Topology Viz use?

Comfyui Topology Viz is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Comfyui Topology Viz use?

About 3.9k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Comfyui Topology Viz?

Skills that share tags, products or a category with Comfyui Topology Viz: Comfyui Skill Openclaw (HuangYuChuh/ComfyUI_Skills_OpenClaw, 413 stars), ComfyUI Custom Node Builder (ConstantineB6/comfy-pilot, 230 stars), Civitai (artokun/comfyui-mcp, 803 stars) and Setup (guaardvark/guaardvark, 258 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Comfyui Topology Viz?

automateyournetwork (a GitHub user) maintains it in automateyournetwork/netclaw, which has 676 GitHub stars. The repository holds 120 skills in this directory. The repository was last updated on October 9, 2026.

Source: automateyournetwork/netclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.