Menu
Evaluate promotion readiness from flywheel outputs using statistical and economic gates, then produce a concise go/no-go decision note.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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.
Execute OpenQuant notebook code cells non-interactively, persist outputs to artifact paths, and fail fast on runtime errors.
Verify parity between notebook-like pipeline execution and scripted experiment workflows so research findings are reproducible.
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 | promotion-gate |
| description | Evaluate promotion readiness from flywheel outputs using statistical and economic gates, then produce a concise go/no-go decision note. |
Use this skill when deciding whether a strategy candidate moves from research iteration to deeper validation.
summary, promotion, costs).openquant.research.run_flywheel_iteration(...)openquant.pipeline.summarize_pipeline(...)Required outputs:
promote_candidate (bool)realized_sharpe, net_sharpe, turnover, estimated_cost)decision.md and metrics.parquet in each experiment run folder.uv run --python .venv/bin/python pytest python/tests/test_experiment_scaffold.py -q