A curated collection of Agent Skills for working with PYXLL, to help AI agents write and understand code using the PyXLL Excel add-in.
99
90%
Does it follow best practices?
Impact
100%
1.56xAverage score across 17 eval scenarios
Advisory
Suggest reviewing before use
A trading operations team uses a PyXLL Excel add-in. Quants regularly deploy new pricing model Python modules to the server, so the list of available models changes over time. Hard-coding a fixed ribbon menu would go stale within days.
The team lead wants a ribbon button that opens a menu populated at runtime: each
time a user clicks the arrow, Python is called to determine which models are currently
available and the menu is built on the fly. The ribbon module is pricing_ribbon.
Produce two files:
pricing_ribbon.xml — ribbon XML with a "Pricing" tab containing a menu button
that is populated at runtime. The menu should refresh its contents each time it is
opened (in case new models have been deployed since the last open).
pricing_ribbon.py — Python module containing all callback functions referenced
from the XML. For the demo implementation, return a menu with at least two example
pricing model buttons (e.g. Black-Scholes and Monte Carlo). Each button's
onAction should also be implemented as a stub function.