name: recipes-and-integrations
description: "Use when adapting DataDesigner tutorial notebooks, documented
recipes, workflow chaining, trace ingestion, multimodal/image/VLM patterns, or
Hugging Face export plans."
disable-model-invocation: true
metadata:
disco-role: operating
license: NOASSERTION
Recipes and Integrations
Use this sub-skill when the user wants to adapt a DataDesigner tutorial, recipe, or integration pattern rather than design a schema from scratch.
It is a route and safety guide, not a catalog of every config object.
Typical triggers
- "Turn the text-to-SQL recipe into a safer dry-run plan"
- "Adapt the image generation notebooks for my product images"
- "Use MCP/tool calls in a dataset recipe"
- "Ingest agent traces into an SFT dataset"
- "Chain a review step between two generation stages"
- "Export a generated dataset to Hugging Face"
- "Which tutorial or advanced recipe should I start from?"
What this sub-skill owns
- Selecting between tutorial notebooks, code/SQL recipes, MCP/tool-use recipes, image recipes, VLM long-document recipes, trace-ingestion recipes, workflow chaining, and Hugging Face export.
- Converting large source recipes into small, task-specific DataDesigner plans without depending on the original checkout.
- Classifying a recipe as safe local, credentialed, network-bound, GPU/Docker-bound, or reference-only.
- Planning external-data and seed handling at the recipe level.
- Planning human review gates and workflow chaining dry-runs.
- Calling out optional dependency groups, remote endpoints, API keys, downloads, and hardware before execution.
Boundaries and required cross-links
- For exact column/config classes, seed source fields, person sampling, Pydantic schemas, Jinja expressions, validators, processors, or custom-column syntax, read
../config-authoring/SKILL.md.
- For
DataDesigner.validate, preview, create, check_models, artifact loading, resume, local export, and base workflow APIs, read ../generation-runtime/SKILL.md.
- For MCP provider setup,
ToolConfig, plugin entry points, plugin install/discovery, or installed-plugin inspection, read ../plugins-and-extensions/SKILL.md.
- For CLI model/provider/persona state, especially
data-designer agent context, read ../cli-and-agent-tools/SKILL.md.
Do not duplicate detailed API tables from those sub-skills. This sub-skill explains how documented recipes are chosen, adapted, constrained, and exported.
Read these first
references/recipe-index.md — choose the right recipe family and see which source scripts are reference-only, excluded, or locally dry-runnable.references/multimodal-image-and-vlm.md — adapt image context, image generation, image editing, rich document images, and long-document VLM recipes safely.references/workflow-chaining-and-trace-ingestion.md — plan composite workflows, review gates, selected stage outputs, and agent-rollout trace ingestion.references/huggingface-export.md — export local artifacts or push generated datasets to Hugging Face Hub without leaking unsafe metadata.references/troubleshooting.md — diagnose API keys, notebook cache/profile behavior, remote endpoints, image extra_body, base64 media, MCP servers, dependency groups, GPU/Docker VLM services, Hub tokens, and large downloads.
Safe adaptation workflow
- Identify the recipe class. Decide whether the user needs a beginner tutorial pattern, an advanced recipe pattern, an integration export, or only a reference-only plan.
- Classify execution risk before running anything. Mark each dependency as local-only, remote model/API, MCP/server, network download, credentialed upload, GPU/Docker service, or large/private data.
- Reduce the recipe to the smallest plan. Extract the relevant column graph, seed assumptions, model alias needs, validation points, and output artifacts. Do not copy large recipe scripts into the answer or generated project unless a small self-contained helper is truly needed.
- Route API details to the owning sub-skill. Use config-authoring for object fields and generation-runtime for validate/preview/create/export calls.
- Use local dry-run checks whenever possible. Validate readable local seed files, image/base64 shapes, expected output columns, workflow artifact names, and Hugging Face folder structure before any network or model call.
- Ask before credentialed or GPU-heavy execution. API keys, Tavily web search, OpenRouter/OpenAI/NVIDIA endpoints, Hugging Face uploads, FinePDF/PDF downloads, local vLLM Docker services, and multi-H100 VLM recipes are not safe implicit actions.
- Preserve reviewability. For adapted recipes, leave a concise plan that states what was executed, what was only validated locally, what remains unverified, and which credentials/hardware are required.
Good finish line
A future agent should be able to choose the right recipe family, produce a self-contained adaptation plan or config script, avoid unsafe executions, know which sibling sub-skill owns exact API details, and explain how to verify or export artifacts without reopening the original repository.