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name: recipes-and-ops description: "Local Data-Juicer recipe processing, analysis, export, and utility workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

recipes-and-ops

Use this sub-skill for local Data-Juicer work that stays on one machine and does not depend on Ray recovery or the service/MCP surface.

Start here

Read these references when the task is not trivial:

• references/workflows.md
• references/configuration.md
• references/cli-reference.md
• references/troubleshooting.md

Owns

• dj-process local recipe execution
• dj-analyze dataset inspection
• dataset config, export config, validators, text_keys, and utility helpers
• operator discovery and safe dependency guidance for local recipes
• preprocess / postprocess / format conversion helpers

Excludes

• Ray partitioning, resume, checkpointing, job monitoring, tracing -> ray-and-recovery
• FastAPI service, MCP server, operator search service -> service-mcp
• Spark launchers, maintainer-only pipelines, and heavy multimodal workflows unless they are only needed as troubleshooting notes

Common flow

1. Inspect the recipe or dataset shape.
2. Choose local config, process list, and export target.
3. Run dj-process or dj-analyze.
4. If a dependency is missing, consult the troubleshooting reference before changing scope.

Validation targets

• Can the user run a local recipe end to end?
• Are the dataset and export settings valid?
• Are failures explained with a concrete fix, not just a stack trace?

When to route away

• Any mention of executor_type: ray or resume/checkpoint tokens
• Any mention of dj-mcp, FastAPI, or tool registration
• Any mention of cluster state, job lifecycle, or partition recovery