Cc Optimize

CC Optimize

Analyze credit card portfolio, identify savings opportunities, and generate an interactive dashboard.

Arguments

Output Structure

Each run creates a timestamped folder — never overwrites previous runs:

cc-optimize-YYYY-MM-DD-HHMM/
├── cc-optimize-YYYY-MM-DD-HHMM-analysis.md      # Full analysis document
├── cc-optimize-YYYY-MM-DD-HHMM-dashboard.html   # Interactive dashboard
└── cc-optimize-YYYY-MM-DD-HHMM-data.json        # Raw data snapshot

Prerequisites

• Hiro MCP server connected to Claude with linked financial accounts
• gog CLI (optional) — only needed for Gmail-based fee/credit verification. Skip with --skip-gmail if not installed.

Workflow

Execute these 6 phases sequentially. Each phase feeds the next. Do NOT ask the user for guidance between phases — proceed autonomously using the instructions below.

Phase 1: Research — Current Card Data

Goal: Get current points valuations and card reward structures from the web.

1. Points valuations by redemption method: Web search for "credit card points valuations [current year]" from sources like The Points Guy (TPG), NerdWallet, Bankrate. For each program, research 3-4 redemption methods with their cpp values (e.g., transfer to airline/hotel partners, travel portal, statement credit, Pay with Points). Extract per-method cpp for:

   • Amex Membership Rewards (MR)
   • Chase Ultimate Rewards (UR)
   • Capital One Miles
   • Hilton Honors
   • United MileagePlus
   • Any other programs discovered in Phase 2

2. Card reward structures: After discovering cards in Phase 2, search "[card name] rewards rates [current year]" for each card to get:

   • Category multipliers (dining, travel, groceries, gas, streaming, etc.)
   • Annual fee
   • Credits and perks (with dollar values)
   • Foreign transaction fee status
   • Sign-up bonus status (if relevant)

Store all research results for use in later phases. Note sources for citation in the final report.

Phase 2: Inventory — Discover All Credit Cards

1. Pull all accounts from Hiro using mcp__hiro__list_accounts, filter to credit cards
2. For each card, extract: name, institution, credit limit, current balance
3. If --skip-gmail is NOT set, cross-reference with Gmail for annual fee charges:

   gog gmail search 'subject:"annual fee" OR subject:"annual membership" OR subject:"yearly fee"' --max 100 --account YOUR_GMAIL_ACCOUNT
   

   Also search for:

   gog gmail search 'subject:"statement credit" OR subject:"benefit" OR subject:"perk"' --max 100 --account YOUR_GMAIL_ACCOUNT
   

4. Match each discovered card to its reward structure from Phase 1 web research
5. For any card that can't be identified, ask the user via AskUserQuestion

Phase 3: Spending Analysis — Where Money Goes on Which Card

1. Calculate the date range based on --months argument (default 6 months back from today)
2. Pull ALL transactions for the period using mcp__hiro__list_transactions with date range
   • Paginate through ALL results using cursor pagination — do not stop at first page
   • Filter to credit card accounts only
3. Group transactions by:
   • Card (account_id) — total spend per card
   • Category — spend by category per card
   • Month — monthly breakdown per card
4. Identify top spending categories across all cards:
   • Travel (flights, hotels, car rental)
   • Dining (restaurants, food delivery)
   • Groceries (supermarkets)
   • Gas/Transit
   • Online shopping / Amazon
   • Streaming/subscriptions
   • General/other
5. Flag foreign transactions (for FX fee analysis) — look for transactions with non-USD indicators or known foreign merchants
6. Calculate annualized spending rates from the period analyzed

3a. Detect Redemption Methods

Scan transactions and Gmail for signals of how the user redeems points in each rewards program. This determines which cpp value from Phase 1 to use.

Transactions (Hiro) — scan for credits/negative amounts indicating redemptions:

• Statement credits from issuer: merchant names like "AMEX REWARD", "CHASE CASHBACK REDEMPTION", "REWARDS REDEMPTION"
• Travel portal charges: "AMEX TRAVEL", "CHASE TRAVEL", "CAPITAL ONE TRAVEL"
• Pay with Points partial credits on retail purchases

Gmail (if not skipped) — search for redemption-related emails:

gog gmail search 'subject:"points transfer" OR subject:"miles transfer" OR subject:"transfer confirmation" OR from:airline' --max 50
gog gmail search 'subject:"travel booking" OR from:"amextravel" OR from:"chase travel"' --max 50
gog gmail search 'subject:"rewards redemption" OR subject:"cashback" OR subject:"statement credit" from:amex OR from:chase' --max 50

Detection logic per program:

Record: program, detected method (or "unknown"), evidence summary.

3b. Confirm Redemption Methods with User

After detection, present findings and ask the user to confirm or correct — one AskUserQuestion for all programs. Pre-fill detected methods and only require input for unknowns. Skip programs with only one meaningful redemption option (pure cash-back cards).

Example prompt:

Based on your transactions and emails, here's how you appear to redeem points.
Please confirm or correct — this affects how we value your rewards.

**Amex MR** — used by: Amex Gold, Amex Platinum
  Detected: Transfer to airline partners (~2.0 cpp)
  Evidence: Found 3 transfer confirmation emails to Delta SkyMiles
  → Is this right? Other options: Travel portal (~1.0 cpp), Statement credit (~0.6 cpp)

**Chase UR** — used by: Chase Sapphire Reserve
  Detected: Could not determine
  Options:
  1. Transfer to airline/hotel partners (~1.8 cpp)
  2. Chase Travel portal (~1.5 cpp via CSR)
  3. Statement credit (~1.0 cpp)

Reply with corrections or confirmations, e.g. "MR looks right, UR: 2"

Default to detected method (or highest-value if unknown) if user doesn't respond clearly.

Phase 4: Optimization Analysis

Using data from Phases 1-3, perform the following analyses:

4a. Earning Rate Calculation

For each card, calculate the effective earning rate using actual spending patterns:

• Apply the card's category multipliers to actual category spending
• Account for monthly/quarterly caps on bonus categories
• Account for tiered rates where applicable
• Convert points earned to dollar value using the redemption-specific cpp determined in Phase 3
• Calculate: effective earning rate = (total points value earned) / (total spend)

4b. Spending Misallocations

For each major spending category:

• Identify which card it's currently being charged to
• Identify which card in the portfolio would be optimal for that category
• Calculate the annual dollar difference if spending were routed optimally
• Rank misallocations by annual value lost

4c. Fee Analysis

For each fee-bearing card:

• Calculate total value received (rewards earned + credits used + perks valued)
• Subtract annual fee
• Determine net value (positive = keep, negative = evaluate)
• Calculate break-even spending level

4d. Credit/Perk Utilization (if Gmail not skipped)

For premium cards, check Gmail for evidence of credit claims:

• Entertainment credits (Disney+, Hulu, etc.)
• Uber/Lyft credits
• CLEAR membership
• Hotel credits (Hilton, Marriott)
• Airline incidentals
• Walmart+ / Instacart+
• Dining credits
• Saks / other retail credits

Track: credit name, amount available, amount claimed (from Gmail evidence), amount unclaimed

4e. Recommendations

Assign each card a status with rationale:

• Keep — Positive net value, well-utilized
• Downgrade — Negative net value but has a no-fee version
• Cancel — Negative net value, no downgrade path, not worth the fee
• Evaluate — Need more info or close call

4f. Optimal Routing (Wallet Guide)

Build the definitive category → card → rate mapping:

Category          | Best Card              | Rate
Dining            | Amex Gold              | 4x MR (4.8 cpp)
Groceries         | Amex Gold              | 4x MR (up to $25k/yr)
Travel (flights)  | Chase Sapphire Reserve | 3x UR (4.5 cpp)
...

4g. Action Items

Generate a ranked list of changes by annual value:

• Each item: description, annual value, difficulty (Easy/Medium/Hard)
• Easy = just change which card you use
• Medium = requires a phone call or app change
• Hard = requires opening/closing accounts

Phase 5: Write Analysis Document

Create the timestamped output folder and write $STAMP-analysis.md:

STAMP="cc-optimize-$(date +%Y-%m-%d-%H%M)"
OUTPUT_DIR="./$STAMP"
mkdir -p "$OUTPUT_DIR"

$STAMP-analysis.md sections:

1. Overview — Date, analysis period, total spend across all cards, total annual fees, estimated annual savings opportunity
2. Points Valuations Used — Table of program → redemption method → cpp value with source. Include how each method was determined (auto-detected from transactions/email vs. user-specified) and list alternative redemption options not chosen with their cpp values.
3. Card-by-Card Breakdown — Table: Card Name | Annual Fee | Period Spend | Best Earning Rate | Effective Earning Rate | Status (Keep/Downgrade/Cancel/Evaluate)
4. Cards to Downgrade/Cancel — For each: card name, current fee, savings from action, rationale, downgrade target (if applicable)
5. Cards to Evaluate — For each: what needs checking and why it's a close call
6. Top 5 Spending Misallocations — Category | Current Card | Current Rate | Optimal Card | Optimal Rate | Annual Value Difference
7. Premium Card Credit Utilization — Card | Credit | Available | Claimed | Unclaimed | Annual Value at Risk
8. Optimal Wallet Routing Guide — The definitive category → card table for daily use
9. Action Items — Ranked by annual value with difficulty ratings

Phase 6: Generate Dashboard (unless --skip-dashboard)

1. Read the dashboard template from this skill's directory:

   dashboard-template.html
   

2. Build the DATA JSON object containing all analysis results structured for the dashboard
3. Replace /* __DATA_PLACEHOLDER__ */ in the template with the actual JSON data
4. Save as $STAMP-dashboard.html in the output folder
5. Open in browser:

   open "$OUTPUT_DIR/$STAMP-dashboard.html"
   

Also save $STAMP-data.json in the output folder for future diffing between runs.

DATA JSON Structure

The dashboard template expects this structure:

{
  "generated": "2026-03-07T14:30:00",
  "period": { "start": "2025-09-07", "end": "2026-03-07", "months": 6 },
  "summary": {
    "totalSpend": 45000,
    "totalFees": 1250,
    "totalSavings": 890,
    "cardCount": 5
  },
  "pointsValuations": [
    {
      "program": "Amex MR",
      "cpp": 2.0,
      "redemptionMethod": "Transfer to airline/hotel partners",
      "detectedFrom": "Gmail: 3 transfer confirmations to Delta SkyMiles",
      "source": "TPG Mar 2026",
      "allOptions": [
        { "method": "Transfer to airline/hotel partners", "cpp": 2.0 },
        { "method": "Amex Travel portal", "cpp": 1.0 },
        { "method": "Statement credit", "cpp": 0.6 }
      ]
    }
  ],
  "cards": [
    {
      "name": "Amex Gold",
      "institution": "American Express",
      "annualFee": 250,
      "spend": 18000,
      "effectiveRate": 0.038,
      "bestRate": 0.048,
      "status": "keep",
      "statusReason": "Strong dining/grocery rewards offset fee",
      "creditLimit": 25000,
      "categories": [
        { "name": "Dining", "spend": 8000, "rate": 0.04, "pointsEarned": 32000 }
      ]
    }
  ],
  "misallocations": [
    {
      "category": "Dining",
      "currentCard": "Chase Freedom",
      "currentRate": 0.01,
      "optimalCard": "Amex Gold",
      "optimalRate": 0.04,
      "annualSpend": 6000,
      "annualLoss": 180
    }
  ],
  "credits": [
    {
      "card": "Amex Platinum",
      "credit": "Uber Credit",
      "available": 200,
      "claimed": 150,
      "unclaimed": 50
    }
  ],
  "walletGuide": [
    {
      "category": "Dining",
      "card": "Amex Gold",
      "rate": "4x MR",
      "effectiveCpp": 0.048,
      "notes": "Up to $25k/yr"
    }
  ],
  "actionItems": [
    {
      "action": "Move dining spend to Amex Gold",
      "annualValue": 180,
      "difficulty": "Easy",
      "details": "Currently split across Chase Freedom and Citi Double Cash"
    }
  ],
  "monthlySpend": [
    { "month": "2025-10", "total": 7500, "byCard": { "Amex Gold": 3000, "Chase Sapphire": 2500, "Other": 2000 } }
  ]
}

Important Notes

• Don't ask for workflow guidance — proceed through all 6 phases autonomously without asking "what should I do next?" or "how should I analyze this?"
• DO ask about unidentifiable cards — if you can't determine a card's reward structure from web search (e.g., obscure card, ambiguous name from Hiro), ask the user via AskUserQuestion what card it is, its reward rates, and annual fee. Getting this right matters more than speed.
• Paginate all Hiro API calls — always check for cursor/next and fetch all pages
• Use current web data — do not rely on embedded knowledge for points valuations or card benefits
• Be precise with numbers — never round unless displaying summaries. Keep full precision in the data JSON
• Detect redemption methods before asking — scan transactions for reward credits and Gmail for transfer/booking confirmations. Only ask the user to confirm or fill gaps. Critical because cpp varies 2-3x by redemption method.
• Cite sources — include URLs or source names for all points valuations