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/artokun/comfyui-mcp/batchgit clone --depth 1 https://github.com/artokun/comfyui-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/artokun/comfyui-mcp/batch)<a href="https://agentmods.dev/commands/artokun/comfyui-mcp/batch"><img src="https://agentmods.dev/badge/commands/artokun/comfyui-mcp/batch.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00006 | $0.01045 |
| Opus 5 | $0.00003 | $0.00522 |
| Sonnet 5 | $0.00001 | $0.00209 |
| Haiku 4.5 | $0.00001 | $0.00104 |
Grade A, and why
batch 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- batch — 89% identical, 36 lines differ
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/comfy-batch — Parameter Sweep Generation
The user wants to generate several images while sweeping parameter values, so they can compare the results.
Instructions
-
Parse the arguments. The argument is: $ARGUMENTS
If no argument was provided, ask the user for a prompt and which parameters to sweep.
Extract:
- Prompt text: everything that isn't a parameter range specifier
- Parameter ranges: identified by
param_name:valuessyntax
-
Parse parameter range syntax. Supported formats:
param:min-max: integer range with step 1 (e.g.,cfg:5-10produces 5, 6, 7, 8, 9, 10)param:min-max:step: range with explicit step (e.g.,cfg:4-12:2produces 4, 6, 8, 10, 12)param:val1,val2,val3: explicit list (e.g.,sampler:euler,dpmpp_2m,dpmpp_sde)seed:N: generate N different random seeds (e.g.,seed:4produces 4 random seeds)
-
Supported sweep parameters:
cfg: CFG scale (float)steps: sampling steps (integer)samplerorsampler_name: sampler algorithm namescheduler: scheduler nameseed: random seed count or explicit seedsdenoise: denoising strength (float, 0.0-1.0)width: image width in pixelsheight: image height in pixels
-
Calculate total combinations. Multiply the count of values for each swept parameter. If the total exceeds 20, warn the user:
- Show the total count and estimated time
- Ask for confirmation before proceeding
- Suggest reducing ranges if the count is very high
-
Check available models. Call
list_local_modelswithmodel_type: "checkpoints"to find a checkpoint. If none are available, follow the model acquisition steps from the gen command. -
Enqueue all combinations. For each parameter combination:
- Call
create_workflowwith template"txt2img"and the current parameter set includingpositive_prompt - Call
enqueue_workflow(action="enqueue")with the created workflow - Collect the returned
prompt_id
- Call
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.
- 3d ago First seen · 91 lines · 6 tokens per session scan A 4253dd31f495
batch is a command published in the GitHub repository artokun/comfyui-mcp (715 stars, last pushed today), licensed MIT. It adds 6 tokens to every session and 1,045 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
setup
You are setting up the Video Recreation Agent Team inside this comfyui-agent repo. Run every step below automatically. Don't ask permission — just do it, report what happened.
PRODUCTION_HARDEN
Each agent is a Claude Code sub-agent invoked via claude --model claude-sonnet-4-6-20250929 with a role-specific prompt file. Agents operate on branches, submit PRs via conventional commits.
PRODUCER
You are the production manager for this open-source VFX tool. You handle everything that's NOT the code itself: CI/CD, packaging, release management, documentation infrastructure, metrics, and cross-agent coordination. You're the one who makes sure the train runs on time and nothing ships broken.
qa-compare
You are the QA expert in the Video Recreation Agent Team.
COMFY_LEAD
You are the ComfyUI technical lead. You know ComfyUI's API, node system, workflow JSON format, and the MCP protocol deeply. You're hardening an AI co-pilot that helps VFX artists work with ComfyUI through natural language.
NUKE_COMP
You are a senior VFX compositor with deep Nuke pipeline experience who has transitioned into pipeline TD work. You think about tool integration the way a compositor thinks about a comp tree — everything must connect cleanly, data must flow predictably, and the artist should never have to think about the plumbing.