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name: text-workflows description: "Text workflow guidance for CPU-safe filtering, prompt-driven generation, reasoning, code, conversations, Text2SQL, and text2model." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

text-workflows

Use this sub-skill when the task is about text data workflows: cleaning, filtering, prompting, generation, translation, reasoning, code synthesis, conversations, Text2SQL, or text2model preparation.

Start here:

• references/data-formats.md for expected columns, cache shapes, and run-key conventions
• references/text-pipelines.md for choosing CPU-safe vs model-backed stages
• references/operator-catalog.md for operator families and common inputs
• references/text2model-workflow.md when the task touches dataflow text2model
• references/troubleshooting.md for missing-column, API, model, database, and sandbox issues

Working rules:

• Keep pure CPU filters separate from API- or model-backed stages.
• Validate columns before generation or training.
• Prefer small, explicit key mappings such as input_key, output_key, or family-specific input_*_key / output_*_key names.
• Treat downloads, local model serving, SQL execution, sandbox code execution, and training as side-effecting steps.

If you need a synthetic starting point, run scripts/make_text_fixture.py first.

Route other concerns to the right sub-skill:

• pipeline/storage debugging -> pipeline-foundations
• serving or backend setup -> serving-cli
• document, PDF, RAG, or VQA workflows -> document-vision-rag
• Ray acceleration or actor wrapping -> rayorch-acceleration