What is the Wheelhouse Plugin Library?
The Wheelhouse Plugin Library is a public, growing collection of Revenue Management skills you can connect directly to your AI client.
If you're already using Wheelhouse MCP, your assistant has direct access to your portfolio — listings, pricing, reservations, preferences, and more. The Plugin Library goes a step further: it packages that MCP connection together with the actual workflows the Wheelhouse Revenue Management team builds, so your assistant can do real analysis, faster and with less back-and-forth.
📽️ Loom walkthrough: https://www.loom.com/share/5fe58a753b91483d814ee378c997cbc1
Skills included today
STLY pacing — on-the-books pacing for any listing or segment, over any date range, compared to the same time last year
Price-change attribution — checks whether a recent rate or preference change actually correlated with bookings
Future rate overpricing — flags months where your posted rates look out of line with what actually transacted last year
Custom rate attribution — a fast check on whether a custom rate you set actually got booked
Portfolio leaderboards — ranks your whole portfolio by pacing, market position, expiring inventory, and more
You don't need to name a skill or remember which one to use — ask in plain English and your assistant picks the right one:
"How is this listing pacing versus last year?" → STLY pacing
"Did that rate change actually drive bookings?" → Price-change attribution
"Are next season's rates too high?" → Future rate overpricing
"Who needs attention across my portfolio?" → Portfolio leaderboard
A faster, more efficient MCP
Data-heavy requests are also faster now, independent of which skill you use.
Previously, when your assistant needed to work through a lot of data, it worked one step at a time — call a tool, wait for the full result to come back through the model, decide what to do next, call the next tool. Every one of those round trips costs tokens and time.
Now, for multi-step, data-heavy requests, your assistant can write a short piece of code that calls the tools it needs, loops through the results, and filters everything down — all before anything comes back into its context. Instead of pulling every reservation or every day's pricing data through the model just to produce a summary, only the answer you actually asked for makes the trip. In testing, this reduced the resources needed to pull all reservations from Wheelhouse by 96%.
Try asking your assistant to pull reservations across your entire market or portfolio in one go — a request that used to hit a wall now runs end-to-end.
How to connect
Connecting is a one-step install, supported in Claude Code, Cursor, Codex, Grok Build, and ChatGPT (Enterprise/Work, via admin-managed import). The library also updates automatically as Wheelhouse ships changes — there's nothing to reinstall or update yourself.
Claude Code:
/plugin marketplace add pricemethod/wheelhouse-plugin
/plugin install wheelhouse-plugin@wheelhouse-plugin
Cursor:
cursor-agent plugin marketplace add https://github.com/pricemethod/wheelhouse-plugin
Then install wheelhouse-plugin from Customize. Teams and Enterprise users can also import the repository through the Dashboard.
Codex:
codex plugin marketplace add pricemethod/wheelhouse-plugin
Then install wheelhouse-plugin from the Codex plugins list.
Grok Build:
grok plugin marketplace add pricemethod/wheelhouse-plugin
grok plugin install pricemethod/wheelhouse-plugin --trust
ChatGPT: ask your admin to go to Admin → Plugins → Add → Import marketplace and enter https://github.com/pricemethod/wheelhouse-plugin as the Source.
Note
The Plugin Library requires the same Wheelhouse MCP connection covered in "How do I connect an AI assistant to Wheelhouse via MCP?" — set that up first if you haven't already.
Related links
Plugin Library repository —
github.com/pricemethod/wheelhouse-pluginAPI Reference —
api.usewheelhouse.com/wheelhouse_rm_apiRelated article: How do I connect an AI assistant to Wheelhouse via MCP?