---

name: data-juicer description: "Data-Juicer repo router for local recipes, Ray recovery, and service/MCP workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Data-Juicer Repo Skill

Use this skill as the top-level router for the Data-Juicer repository. Keep workflow depth, long examples, and troubleshooting details in the sub-skills and shared references.

Start with the route

User need	Read
Local recipe processing, dataset config, export, operator choice, or simple analysis	sub-skills/recipes-and-ops/SKILL.md
Ray execution, partitioning, checkpointing, job monitoring, or recovery	sub-skills/ray-and-recovery/SKILL.md
FastAPI service, MCP server, operator search, or request encoding	sub-skills/service-mcp/SKILL.md

Quick environment check

Install guidance lives in references/install-and-scope.md; start with py-data-juicer and add tools or distributed only when the selected route needs them.

Use the bundled smoke check when you only need a safe install/import sanity check:

python scripts/check_environment.py

Useful CLI help checks:

dj-process --help
dj-analyze --help
dj-install --help
dj-mcp --help

Shared references

• references/repo-provenance.md
• references/repo-routing-metadata.json
• references/install-and-scope.md
• references/package-overview.md
• references/cli-reference.md
• references/api-reference.md
• references/optional-extras.md
• references/troubleshooting.md
• references/workflow-map.md

Scope boundaries

• Do not mix local recipe guidance with Ray recovery details.
• Do not mix HTTP/MCP request transport with local dataset semantics.
• Do not assume optional extras are installed unless the workflow needs them.
• Do not depend on the source checkout remaining present once the skill is created.

When in doubt

Start from references/workflow-map.md, then open the matching sub-skill.