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Productivity · SKILL.md

pipelines

pipelines

Routes UltraRAG pipeline build, run, and recipe workflows, including YAML orchestration and the Python API wrappers.

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name: pipelines description: "Routes UltraRAG pipeline build, run, and recipe workflows, including YAML orchestration and the Python API wrappers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Pipelines

Use this sub-skill when the task is about running, building, or authoring an UltraRAG pipeline.

Typical triggers

  • ultrarag build, ultrarag run, or ultrarag show
  • ToolCall / PipelineCall usage from Python
  • pipeline.yaml, servers:, pipeline:, loop:, branch:
  • Demo or experiment files under examples/demos/ or examples/experiments/
  • Questions about how a pipeline step consumes or produces variables
  • Requests to reproduce or adapt a named example such as RAG, LLM, LightResearch, AgentCPM-Report, webnote, search_o1, search_r1, rankcot, ircot, visrag, or sayhello

What this sub-skill covers

  • CLI build/run/show entry points.
  • ToolCall and PipelineCall from the Python API.
  • Pipeline structure, variable resolution, step remapping, branch/loop logic, and generated parameter/ and server/ artifacts.
  • Example workflows that combine benchmark data, retriever, generation, prompt, custom, memory, router, evaluation, and corpus steps.
  • Safe smoke checks that prove the orchestration layer still works.

What stays elsewhere

  • Individual MCP server signatures and backend-specific configuration belong in sub-skills/servers/.
  • Flask UI, chat sessions, knowledge-base storage, and case study belong in sub-skills/ui-and-storage/.

Start here

  • Read references/pipeline-dsl.md for the verified YAML syntax, data-flow rules, and build/run behavior.
  • Read references/workflows.md for a map of example families and the best starting point for each one.
  • Read references/troubleshooting.md when a pipeline build, parameter merge, or execution step fails.
  • Run scripts/smoke_sayhello_pipeline.py for a minimal end-to-end smoke check.

Common user questions this sub-skill should answer

  • How do I build a pipeline from YAML?
  • How do I run a pipeline with a custom parameter file?
  • How do I express a loop or branch?
  • Why did a step remap or variable lookup fail?
  • Which example pipeline is closest to my task?
  • How do I get a small smoke test working before a larger RAG workflow?

Practical workflow

  1. Identify the example family that matches the request.
  2. Check whether the task is only orchestration, or whether it also needs a specific server backend or UI route.
  3. Use the DSL reference to confirm the step shape and variable names.
  4. Use the smoke script or a minimal example to validate the plan.

Helpful commands

Use the bundled smoke helper instead of relying on a source example file:

python sub-skills/pipelines/scripts/smoke_sayhello_pipeline.py --repo-root <checkout>
ultrarag show ui
ultrarag show case --config_path <memory-case.json>

If the task is about a named experiment or demo, use the distilled family map in references/workflows.md before guessing the pipeline structure.

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