card-optimizer_skill

This skill helps you maximize credit card rewards by recommending the best card for each purchase category and calculating ROI.
  • Python

2.5k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill card-optimizer

  • _meta.json281 B
  • SKILL.md12.6 KB

Overview

This skill helps you maximize credit card rewards by recommending the best card for every purchase category. It tracks category-specific rates, annual caps, rotating quarterly categories, and computes annual-fee ROI. It also suggests new cards based on your spending estimates and gap analysis.

How this skill works

The optimizer reads your card definitions, reward rates, caps, and estimated monthly spending to compute effective cashback equivalents (converting points/miles using stored valuations). For a given merchant or category it checks all cards, factors in caps and network acceptance, and returns a cap-aware recommendation with a fallback. It can run ROI, gap analysis, quarterly activation reminders, and propose new cards that would add value.

When to use it

  • When deciding which card to use for a merchant or purchase category (e.g., groceries, gas, Amazon).
  • To see whether an annual-fee card is worth keeping via ROI analysis.
  • At the start of each quarter to activate rotating categories and avoid losing bonus rewards.
  • To run a spending gap analysis and identify weak coverage or cap exhaustion.
  • When considering adding or removing a card and recalculating the optimal category map.

Best practices

  • Provide estimated monthly spending by category to enable accurate ROI and gap analysis.
  • Keep point_valuation_cpp updated for points/miles cards to compare effective cashback rates.
  • Record quarterly activation status for rotating cards so reminders are accurate.
  • Always include a Visa/MC fallback when recommending Amex due to limited acceptance.
  • Recalculate the category_map whenever you add, remove, or materially change a card entry.

Example use cases

  • Ask 'Which card for groceries?' to get a primary recommendation, cap timing, and a fallback.
  • Run 'Annual fee worth it?' to get a per-card ROI report comparing against a 2% flat baseline.
  • Request 'Card optimization report' to see well-covered categories, gaps, and suggested new cards.
  • Add a new card by name to generate an updated category_map and projected first-year value with signup bonus.
  • Get quarterly reminders like 'Activate Q2 categories' for cards that require activation.

FAQ

No. The skill uses estimated monthly spending per category to run ROI and gap analysis. For transaction-level tracking use a budgeting tool and import summaries.

How are points and miles compared to cashback?

Points/miles are converted to a cashback-equivalent using point_valuation_cpp (cents per point). Multiply reward rate by valuation to get an effective cashback rate for direct comparison.

Built by
VeilStrat
AI signals for GTM teams
© 2026 VeilStrat. All rights reserved.All systems operational