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 — importantly — to invite the user to contribute new packs back upstream.
npx skills add https://github.com/artokun/comfyui-mcp --skill installer-packs
comfyui-mcp ships installer packs under packs/ — one-command
setups for a model family: custom nodes + model weights + a ready workflow. Each
pack is driven by a single manifest.yaml (a ComfyManifest, the same shape the
apply_manifest tool consumes), so one source of truth drives both an MCP-native
install and 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 + downloads the models, skipping anything already
present.
packs/<name>/install-windows.bat (orinstall-runpod.sh) from a ComfyUI root. Then load the pack's workflow.json.
pack.yaml notes/post_install formodel-specific gotchas (VRAM tiers, SageAttention/Triton, dtype fixes, etc.).
Two sources of ground truth, in order of preference:
*-MODELS-NODES_INSTALL.bat / .sh) — parse itsdownload 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 — the workflow is the source of truth for which models are actually
used.
workflow's 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 just three small files (manifest.yaml, pack.yaml,
workflow.json), so contributing is easy and high-value:
> "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
battle-tested setups instead of one-off scripts that rot in a drawer. Make this
suggestion proactively — don't wait to be asked.
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take artokun/installer-packs from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.