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.
npx agentmods add commands/sandyup/comfyui-mcp/vizgit clone --depth 1 https://github.com/sandyup/comfyui-mcpWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00012 | $0.00445 |
| Opus 5 | $0.00006 | $0.00222 |
| Sonnet 5 | $0.00002 | $0.00089 |
| Haiku 4.5 | $0.00001 | $0.00044 |
Grade A, and why
viz scanned grade A with 0 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to viz — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/comfy-viz — Visualize a Workflow
The user wants to visualize a ComfyUI workflow as a mermaid flowchart diagram.
Instructions
-
Get the workflow JSON. The argument may be: $ARGUMENTS
- A file path to a workflow JSON file — read it with the Read tool
- Inline JSON pasted directly as the argument
- Nothing — ask the user to provide a workflow file path or paste the JSON
-
Validate the input. The workflow must be in ComfyUI's API format: an object where keys are node IDs and values have
class_typeandinputs. If it looks like the web UI format (hasnodesandlinksarrays), tell the user it needs to be in API format and suggest they export it via "Save (API Format)" in ComfyUI. -
Visualize. Use the
visualize_workflowtool with:workflow: the parsed workflow JSONshow_values:true(to include parameter values in node labels)direction:"LR"(left-to-right, easiest to read)
-
Present the diagram. Show the mermaid output to the user. The mermaid code block will render as a flowchart showing nodes grouped by category with labeled connections.
Example
User: /comfy-viz ~/workflows/my-workflow.json
Steps:
- Read the file at
~/workflows/my-workflow.json - Pass contents to
visualize_workflow - Display the mermaid diagram
Notes
- If the workflow is very large (50+ nodes), suggest using
direction: "TB"(top-to-bottom) for better readability - The diagram groups nodes into subgraphs by category: loading, conditioning, sampling, image, output
- Connection edges are labeled with data types (MODEL, CLIP, CONDITIONING, LATENT, IMAGE, VAE, etc.)
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.
- 2d ago First seen · 40 lines · 12 tokens per session scan A c88dfc4917c0
viz is a command published in the GitHub repository sandyup/comfyui-mcp (1 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 445 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to viz, differing in 6 lines, and is treated as a copy.
Other commands, from other repositories
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.