---

name: invokeai description: "Route InvokeAI server operations, node workflow authoring, workflow queue records, and model-management diagnostics for future coding agents." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

InvokeAI Repo Skill

Use this repo skill when a task names InvokeAI or asks about its Python server, CLI, node workflow system, workflow library/queue APIs, model manager, or model/backend diagnostics. InvokeAI is a Python image-generation application and local web server with workflow/node authoring, model records/install/load/cache services, and multiuser/API operations.

Minimal Setup

• Use Python >=3.11,<3.13 and install the public package or local checkout with the backend extras appropriate for the task.
• For metadata-only work, first verify the lightweight import surface with python -c "import invokeai, invokeai.version; print(invokeai.version.__version__)".
• Do not treat a metadata-only import as proof that server startup, Torch/diffusers backends, model loading, or generation workflows are ready.

Start Here

• Read references/environment-and-install.md for package metadata, Python/backend expectations, entry points, backend install choices, and safe import checks.
• Read references/troubleshooting.md for cross-cutting install/import/runtime/backend guidance before running heavyweight operations.
• Read references/repo-provenance.md before deciding whether this skill is stale for a checkout.
• Use scripts/check_invokeai_skill_assets.py to verify this skill tree’s bundled references, scripts, frontmatter, and structured metadata.

Route by Task

• Use sub-skills/operations-config/SKILL.md for invokeai-web, root/config resolution, env/YAML settings, FastAPI route discovery, OpenAPI/auth/user administration, and operational startup issues.
• Use sub-skills/workflow-nodes/SKILL.md for custom invocation/node authoring, invokeai.invocation_api, field metadata, InvocationContext, graph edges, iterator/collector/if behavior, and execution-state semantics.
• Use sub-skills/workflows-queues/SKILL.md for workflow library records, bundled default workflow JSON, session queue APIs, batch enqueue payloads, queue privacy redaction, workflow thumbnails, and public/private workflow behavior.
• Use sub-skills/model-management/SKILL.md for model taxonomy, records/install/register/import/delete/search, model load/cache diagnostics, LoRA/GGUF/quantization triage, external provider model records, and safe metadata inspection.

Common Decisions

• Prefer safe metadata, JSON, parser, and --help checks before starting the web server, importing all backends, loading model weights, downloading models, or mutating model/user/workflow databases.
• Treat full generation/regression scripts, GPU stress checks, downloads, model conversions, and destructive cleanup as opt-in operations that need explicit user approval and a disposable root or model store.
• Keep workflow JSON, invocation graph execution state, and model records separate: workflow files are frontend/library records, queued sessions hold serialized graph execution state, and model records point to model files/provider sources.
• For multiuser questions, combine operations-config for auth setup with workflows-queues for workflow/queue owner/public/admin behavior.
• For default workflow compatibility, combine workflows-queues for JSON/library structure, workflow-nodes for node/edge schemas, and model-management for model identifiers and loader compatibility.

Verification Notes

• The bundled scripts are intentionally lightweight and safe by default. They validate structure, metadata, settings catalogs, route families, and file headers; authoritative runtime checks may still require a complete InvokeAI environment.
• The generated skill does not bundle model weights, frontend assets, Docker images, native tests, or original repo scripts that perform network, GPU-heavy, release, or destructive operations.