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Execute OpenQuant notebook code cells non-interactively, persist outputs to artifact paths, and fail fast on runtime errors.
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Based on SOC occupation classification
Audit experiment artifact folders for completeness, schema consistency, and promotion traceability across single and grid runs.
Build candidate features, run feature screening and diagnostics, and prepare accepted feature sets for downstream flywheel experiments.
Run OpenQuant notebook-to-experiment flywheel iterations (single run and grid run), capture artifacts, and summarize ranked outcomes for promotion decisions.
Verify parity between notebook-like pipeline execution and scripted experiment workflows so research findings are reproducible.
Evaluate promotion readiness from flywheel outputs using statistical and economic gates, then produce a concise go/no-go decision note.
Build and run an AFML-grounded, chapter-by-chapter documentation loop for OpenQuant docs. Use this when you need consistent module docs, hierarchical chapter coverage, and iterative progress tracking with MCP-backed prompts.
| name | notebook-executor |
| description | Execute OpenQuant notebook code cells non-interactively, persist outputs to artifact paths, and fail fast on runtime errors. |
Use this skill when you need deterministic notebook execution in CI or local verification loops.
.ipynb code cells with plain Python.--out) to avoid mutating source notebooks.uv run --python .venv/bin/python notebooks/python/scripts/execute_notebook_cells.py \
notebooks/python/08_algo_wheel_experiments.ipynb \
--out /tmp/openquant-artifacts/08_algo_wheel_experiments.executed.ipynb
notebooks/python/scripts/execute_notebook_cells.pynotebooks/python/*.ipynbuv run --python .venv/bin/python pytest python/tests/test_notebook_execute_script.py -q