Q (sonnet-5.5): Can you help me write a production-ready Python script using Bokeh to build an interactive clinicogenomic dashboard to analyzing the MSK-CHORD dataset? I have some specific requirements I can give you

═ answer (success)
Yes, I can help. Before I write it, I need your requirements. These questions would shape the design most:

**Data access**
- Should the script pull data live from the public cBioPortal REST API (`https://www.cbioportal.org/api`, study `msk_chord_2024`), or read a local download of the study files?
- Do you need to run it offline or behind a firewall?

**Views and analyses**
- Which views do you want? Some common ones for MSK-CHORD:
  - Cohort overview: cancer type, sex, age, sample type, and primary vs. metastatic.
  - Gene-level mutation frequency, with a gene picker and filters by cancer type.
  - An OncoPrint-style heatmap or co-mutation view.
  - Kaplan-Meier survival curves split by mutation status or clinical group. These need `lifelines` or similar, since Bokeh doesn't compute statistics.
  - TMB and MSI distributions.
  - Treatment and timeline views, which use the CHORD clinical event data.
- Should the KM and group-comparison views show statistics such as log-rank p-values, hazard ratios, or Fisher's exact test? I'd compute those in the script with `scipy` or `lifelines`.

**Interactivity**
- What filters do you need (cancer type, gene, age range, sex, sample type), and should they be linked across all plots?
- Do you want a standalone HTML file (`bokeh.io.save`) or a Bokeh server app (`bokeh serve`) with callbacks? The server option handles large data better.

**Production requirements**
- Do you need any of these: CLI arguments, config file, logging, caching of API responses, type hints, unit tests, or a `requirements.txt`?
- Which Python and Bokeh versions are you on (Bokeh 3.x assumed)?
- Do you need export options, such as CSV download of the filtered cohort?

**Data handling**
- Should the dashboard count patients or samples? I'd suggest showing both explicitly.
- Should it use only profiled samples as the denominator for mutation frequencies? I'd suggest yes.
- Are there any restrictions on displaying small cell counts?

Send whatever requirements you have. If you'd rather I start with a sensible default, I can build a Bokeh server app that:
- pulls MSK-CHORD data from the REST API and caches it;
- has global filters for cancer type, sex, and age;
- shows a cohort overview, a top-mutated-genes chart, and a KM survival panel with log-rank stats.

You could then extend it from there.