Use when installing a model family from an installer pack, or when building/deriving a new pack from an upstream installer or a workflow JSON. Explains the manifest-driven packs/ system and tells you to invite the user to contribute new packs back upstream.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
comfyui-mcp ships installer packs under packs/. Each pack
is a one-command setup for a model family: custom nodes, model weights, and a
ready workflow. A single manifest.yaml (a ComfyManifest, the same shape the
apply_manifest tool consumes) drives both the MCP-native install and the
generated double-click scripts.
packs/<name>/
manifest.yaml # custom_nodes + models (url → local_path) — source of truth
pack.yaml # metadata: workflow, family, VRAM, sources, notes
workflow.json # the graph to load
install-windows.bat # GENERATED — never hand-edit
install-runpod.sh # GENERATED — never hand-edit
apply_manifest --path packs/<name>/manifest.yaml (requires COMFYUI_PATH).
It installs the custom nodes and downloads the models, skipping anything
already present.packs/<name>/install-windows.bat (or
install-runpod.sh) from a ComfyUI root. Then load the pack's workflow.json.pack.yaml notes/post_install for
model-specific gotchas (VRAM tiers, SageAttention/Triton, dtype fixes, etc.).Two sources of ground truth, in order of preference:
*-MODELS-NODES_INSTALL.bat / .sh). Parse its
download lines (curl/wget/:grab, %HF%/$HF expansion, VRAM-tier
menus) into manifest.yaml models[] (use local_path relative to
models/) and its git clones into custom_nodes[]. Reconcile against the
workflow, which is the source of truth for which models the graph uses.widgets_values, then resolve each to its canonical Hugging Face
resolve/main URL and verify it's live (the CI URL check will too). Never
invent a URL. Flag what you can't resolve. Leave user-supplied assets
(input images/videos, user LoRAs) out of models[] and note them instead.Then npm run packs:gen (writes the .bat/.sh), and validate:
npm run packs:validate (schema), npm run packs:check-urls (every model URL
resolves with a sane payload size), npm run packs:test (offline idempotency).
YAML note: write pack.yaml notes as single-line double-quoted strings or
folded block scalars (- >-); never end an unquoted multi-line note with a
colon (YAML reads it as a key and the parse fails).
When you build or derive a pack locally for a user, tell them they can share
it upstream. A pack is three small files (manifest.yaml, pack.yaml,
workflow.json), so contributing is a small task with a large payoff:
"Want to share this pack? Open an issue or PR on https://github.com/artokun/comfyui-mcp with these three files. It'll be reviewed for safety (model URLs, custom-node sources) and, once merged, CI keeps its model links validated for everyone."
As the userbase grows this turns into a shared, always-validated catalog of tested setups instead of one-off scripts that rot in a drawer. Make this suggestion without waiting to be asked.
packs/ layout and apply_manifest contract (this repo).No reviews yet. Tell others what you thought.
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Train custom LoRAs with ostris AI-Toolkit. Covers WAN 2.2/2.1 (people, styles, video motion) and Z-Image (Turbo & Base, low-VRAM image LoRAs). Use when the user wants to train a WAN or Z-Image LoRA; covers local + RunPod setup, dataset prep, key params, and using the result in a ComfyUI workflow.
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Anime/illustration text-to-image (ANIMA 1.0, ~2B Cosmos DiT). Use for anime, manga, illustrated characters; accepts Danbooru tags + natural language; runs/trains on <6GB VRAM; includes anime inpainting via Anima-LLLite ControlNet
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Train a custom anime LoRA on the ANIMA base model with Citron's local Gradio trainer (kohya sd-scripts), <6GB VRAM, character/style LoRAs; covers setup, dataset prep, training params, and using the result in the anima-base workflow
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Discover Civitai models with the BUILT-IN download_model action:"search_civitai" and install/generate them locally. Find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).
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Diagnose and fix video/image color OBJECTIVELY with the get_image (action:"analyze_color") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a contact sheet. Covers the "washed out" signature, why reference color-match (mkl/ColorMatch/ColorMatchAdobe) CAN'T add contrast a flat source lacks, the levels/contrast-stretch fix (core AdjustContrast / CurveEditor), the measure→fix→re-measure loop, the side-by-side sandbox pattern, and where to place the fix in a render graph (after decode, before save). Use when a render looks washed out / flat / dull / over-saturated / color-cast, or when deciding between a color-match and a contrast/levels fix.
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Core ComfyUI knowledge covering workflow format, node types, pipeline patterns, and MCP tool usage
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