Agent-Skills.md

Agent Skills: Explain Comfy Workflow

Explain an existing VibeComfy or ComfyUI workflow, ready template, recipe, scratchpad, target graph, node chain, model path, prompt path, or runtime result. Use when the user asks what a workflow does, how it works, what a node or setting means, why a graph is wired a certain way, what will happen if it runs, or wants answers about a workflow without necessarily editing or executing it.

UncategorizedID: peteromallet/vibecomfy/explain-comfy-workflow

Repository

peteromallet/vibecomfy
peteromalletLicense: MIT
15119

Install this agent skill to your local

pnpm dlx add-skill https://github.com/peteromallet/VibeComfy/tree/HEAD/docs/agent-skill/skills/explain-comfy-workflow

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docs/agent-skill/skills/explain-comfy-workflow/SKILL.md

Skill Metadata

Name
explain-comfy-workflow
Description
Explain an existing VibeComfy or ComfyUI workflow, ready template, recipe, scratchpad, target graph, node chain, model path, prompt path, or runtime result. Use when the user asks what a workflow does, how it works, what a node or setting means, why a graph is wired a certain way, what will happen if it runs, or wants answers about a workflow without necessarily editing or executing it.

Explain Comfy Workflow

Use this for understanding, not mutation. Push toward the same VibeComfy evidence path as edit/run/debug, but stop at a clear answer unless the user asks to change or execute the graph.

Fast Path

vibecomfy inspect <workflow>
vibecomfy analyze info <workflow>

If the target is raw JSON, inspect it as import evidence and use the positional-source conversion path when a Python explanation is needed:

vibecomfy import <workflow.json>
vibecomfy validate workflows/<source-stem> --json

If the question depends on class behavior, sockets, or widgets:

vibecomfy nodes spec <ClassType>

Use search-comfy-workflows only when local evidence is not enough to explain a custom node, model family, or community workflow pattern.

What To Answer

Shape the explanation around the user's question:

  • what the workflow makes
  • the main data path: loaders -> conditioning -> sampler/generator -> decode/output
  • important public inputs, prompts, seeds, steps, dimensions, frame/audio controls, or model choices
  • custom nodes, models, and missing dependencies
  • likely runtime constraints or reasons it may not run
  • where the answer came from: command output, node ids, file path, metadata, or run artifact

Keep it concrete. Name exact node classes and ids when they matter. Do not invent field meanings; inspect the graph or node spec.

Boundaries

  • Do not edit the workflow; hand off to edit-comfy-workflow.
  • Do not run GPU work just to explain a static graph; hand off to run-comfy-workflow only when the user asks for execution or output proof.
  • Do not diagnose a failure beyond the available evidence; hand off to debug-comfy-workflow when logs, validation errors, or missing assets are central.
  • Do not search Hivemind for every question. Start with the local graph and escalate only when semantics or precedent are missing.

Return Shape

Answer in plain language with a compact evidence trail:

  • target workflow or file
  • short purpose summary
  • main node/data path
  • relevant knobs or dependencies
  • uncertainties or next proof step, if any

For a vibecomfy.exec node, identify the source mode (inline function, snapshot capsule, or installed entrypoint), the declared semantic IO, the physical in_N/out_N mapping, bindings, result adapter, source digest, and declared worker dependencies. Inspecting a capsule is inert; only queue-time execution imports it. Link users to the custom Python workflow guide when they want to edit or export the source.

If the answer is based on a run, cite the RunResult fields or out/runs/<run_id>/metadata.json. If it is based on an edit candidate, say it is a candidate graph, not an executed result.