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 skills/thangchung/agent-engineering-experiment/comfyuinpx skills add thangchung/agent-engineering-experiment --skill comfyuigit clone --depth 1 https://github.com/thangchung/agent-engineering-experimentWrote 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/skills/thangchung/agent-engineering-experiment/comfyui)<a href="https://agentmods.dev/skills/thangchung/agent-engineering-experiment/comfyui"><img src="https://agentmods.dev/badge/skills/thangchung/agent-engineering-experiment/comfyui.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 | $0.00025 | $0.01186 |
| Opus 5 | $0.00013 | $0.00593 |
| Sonnet 5 | $0.00005 | $0.00237 |
| Haiku 4.5 | $0.00003 | $0.00119 |
Grade A, and why
comfyui-converter 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
comfyui-converter
Your job is to convert a json workflow graph for ai image generation into a typescript function.
- You should define a type for the input, using Zod for validation.
- You should use
.describeto describe each parameter to the best of your ability. - Filename prefix is always set by the system in a different location.
- Do not extrapolate enum values. Always use the checkpoint value from config and use imported types as demonstrated.
- Use snake_case for multi-word parameters.
- LoadImage inputs will always be accepted as either a url or base64 encoded string
- Only output the typescript, with no additional commentary.
Example Output
import { z } from "zod"; import config from "../config";
let checkpoint: any = config.models.checkpoints.enum.optional(); if (config.warmupCkpt) { checkpoint = checkpoint.default(config.warmupCkpt); }
const ComfyNodeSchema = z.object({ inputs: z.any(), class_type: z.string(), _meta: z.any().optional(), });
type ComfyNode = z.infer;
interface Workflow { RequestSchema: z.ZodObject<any, any>; generateWorkflow: (input: any) => ComfyPrompt; description?: string; summary?: string; }
const RequestSchema = z.object({ prompt: z.string().describe("The positive prompt for image generation"), width: z .number() .int() .min(256) .max(2048) .optional() .default(1024) .describe("Width of the generated image"), height: z .number() .int() .min(256) .max(2048) .optional() .default(1024) .describe("Height of the generated image"), seed: z .number() .int() .optional()`` .default(() => Math.floor(Math.random() * 1000000000000000)) .describe("Seed for random number generation"), steps: z .number() .int() .min(1) .max(100) .optional() .default(4) .describe("Number of sampling steps"), cfg_scale: z .number() .min(0) .max(20) .optional() .default(1) .describe("Classifier-free guidance scale"), sampler_name: config.samplers .optional() .default("euler") .describe("Name of the sampler to use"), scheduler: config.schedulers .optional() .default("simple") .describe("Type of scheduler to use"), denoise: z .number() .min(0) .max(1) .optional() .default(1) .describe("Denoising strength"), checkpoint, });
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.
- 4d ago First seen · 194 lines · 25 tokens per session scan A e00408298c45
comfyui-converter is a skill published in the GitHub repository thangchung/agent-engineering-experiment (24 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 1,186 once invoked, about $0.0001 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.
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