---

name: core-graphs-schedulers description: "Use this sub-skill for Dask delayed, task graph inspection, HighLevelGraph concepts, scheduler selection, compute/persist/optimize/annotate behavior, tokenization, graph manipulation, and custom collection basics." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Core Graphs and Schedulers

Use this sub-skill when the task is about Dask's core lazy execution model rather than a collection-specific API.

Route Here For

• Building lazy graphs with dask.delayed, dask.compute, dask.persist, dask.optimize, and dask.annotate.
• Inspecting low-level task dicts, HighLevelGraph layers/dependencies, task keys, tokenization, and scheduler inputs.
• Choosing local schedulers: synchronous, threads, processes, or explicit scheduler get functions.
• Debugging graph construction, optimization/fusion, multiprocessing serialization, graph visualization, or non-deterministic tokens.
• Implementing or reviewing custom Dask collections that expose the __dask_*__ protocol.

Route Elsewhere

• NumPy-like arrays, chunks, blockwise array operations, reductions, gufuncs, or rechunking: ../array-workflows/SKILL.md.
• Pandas-like dataframes, partitions, IO, groupby, joins, shuffle, or query planning: ../dataframe-workflows/SKILL.md.
• Bag, text, JSON, bytes, compression, and filesystem ingestion workflows: ../bag-bytes-workflows/SKILL.md.
• Config files, CLI commands, diagnostics, progress bars, profilers, and install/runtime environment checks: ../configuration-diagnostics-cli/SKILL.md.

Start Here

1. Decide whether the user needs lazy graph construction, graph inspection, scheduler selection, or custom collection internals.
2. Keep graph definition lazy: build Dask collections first, then call compute() or persist() once at the boundary.
3. Prefer collection APIs when the work naturally fits arrays, dataframes, or bags; use delayed as a release valve for custom Python functions.
4. Use scheduler="synchronous" and optimize_graph=False for debugging; switch to threads or processes only after graph structure is correct.
5. For multiprocessing, use importable top-level functions and a if __name__ == "__main__": guard in scripts.

Bundled References

• references/api-reference.md: API signatures, graph objects, scheduler get functions, tokenization, graph manipulation, and custom collection hooks.
• references/workflows.md: common lazy graph workflows, scheduler-choice recipes, graph inspection patterns, and custom collection checklist.
• references/troubleshooting.md: fixes for eager execution, scheduler mismatches, pickling failures, non-deterministic tokens, Graphviz, and optimization surprises.

Bundled Script

Run scripts/core_smoke.py --help for options. The script builds delayed tasks, inspects graph metadata, computes the result with a chosen scheduler, and prints JSON suitable for quick environment checks.