Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Added GitHub URL
What This Skill Does
Recommends the optimal credit card for each purchase category based on reward rates, spending caps, and network acceptance. Tracks annual fee ROI, manages rotating quarterly category activations, and identifies portfolio gaps using estimated monthly spending.
Unlike static comparison sites, it factors in your actual card portfolio, per-category spending caps, and estimated monthly spend to warn you when a cap will be exhausted and what to switch to after.
When to use it
- Picking the right card at a grocery store checkout
- Deciding whether to keep a card with a $95 annual fee
- Getting reminded to activate Chase Freedom Flex quarterly bonus categories
- Finding which spending categories have weak reward coverage
- Getting new card suggestions based on dining or travel spend patterns
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Which card should I use at Whole Foods?
- 1Maps 'Whole Foods' to the whole_foods or groceries category
- 2Checks all cards in cards.json for that category's reward rate
- 3Converts points cards to cashback-equivalent using point_valuation_cpp for fair comparison
- 4Checks annual spending caps and estimates when the cap would be hit based on monthly spend
- 5Returns the top card with a Visa/MC fallback if the winning card is Amex
Recommended card with reward rate, cap warning if applicable, and a fallback option for merchants that don't accept the primary card