What is the Categorization skill for?

To classify bank transactions into 15 Danish spending categories using merchant pattern matching.

How are patterns defined?

Patterns use wildcard * to map text to a merchant and a subcategory.

Can I customize categories?

Yes, you can update or extend the 15 categories and patterns to fit your own chart of accounts; ensure Andet as fallback.

categorization

npx machina-cli add skill peerjakobsen/smartspender/categorization --openclaw

Files (1)

SKILL.md

11.3 KB

Categorization

Purpose

Provides Claude with Danish merchant knowledge and categorization rules for classifying bank transactions into spending categories.

Category	Subcategories	Description
Bolig	Husleje, El, Vand, Varme, Forsikring	Housing costs
Dagligvarer	Supermarked, Specialbutik	Groceries
Transport	Offentlig, Bil, Taxi, Cykel	Transportation
Abonnementer	Streaming, Fitness, Software, Telefon	Subscriptions
Restauranter	Restaurant, Café, Takeaway	Dining out
Shopping	Tøj, Elektronik, Bolig, Andet	Shopping
Sundhed	Apotek, Læge, Tandlæge	Health
Underholdning	Biograf, Koncert, Spil	Entertainment
Rejser	Fly, Hotel, Ferie	Travel
Børn	Daginstitution, Tøj, Legetøj	Children
Personlig pleje	Frisør, Kosmetik	Personal care
Uddannelse	Kurser, Bøger, Materialer	Education
Opsparing	Overførsler til opsparing	Savings transfers
Indkomst	Løn, Refusion	Income
Andet	Ukategoriseret	Other / fallback

Merchant Pattern Database

Map raw transaction text patterns to normalized merchant names and categories. Patterns use * as wildcard.

Dagligvarer (Groceries)

Pattern	Merchant	Subcategory
`NETTO`	Netto	Supermarked
`FØTEX` or `FOETEX`	Føtex	Supermarked
`REMA1000` or `REMA1000*`	Rema 1000	Supermarked
`IRMA`	Irma	Supermarked
`LIDL`	Lidl	Supermarked
`ALDI`	Aldi	Supermarked
`BILKA`	Bilka	Supermarked
`MENY`	Meny	Supermarked
`SPAR`	Spar	Supermarked
`FAKTA`	Fakta	Supermarked
`COOP`	Coop	Supermarked
`DAGLIGBRUGSEN` or `DAGLIBRUGSEN`	Dagli'Brugsen	Supermarked
`SUPERBRUGSEN*`	SuperBrugsen	Supermarked
`KVICKLY`	Kvickly	Supermarked

Transport

Pattern	Merchant	Subcategory
`DSB`	DSB	Offentlig
`REJSEKORT`	Rejsekort	Offentlig
`MOVIA`	Movia	Offentlig
`Q8`	Q8	Bil
`CIRCLEK*`	Circle K	Bil
`SHELL`	Shell	Bil
`OK BENZIN` or `OK PLUS`	OK	Bil
`UBER`	Uber	Taxi
`TAXA` or `DANTAXI`	Taxa	Taxi
`DONKEYREPUBLIC*`	Donkey Republic	Cykel

Abonnementer (Subscriptions)

Pattern	Merchant	Subcategory
`NETFLIX`	Netflix	Streaming
`SPOTIFY`	Spotify	Streaming
`DISNEYPLUS` or `DISNEYPLUS*`	Disney+	Streaming
`HBO` or `MAXSTREAMING*`	HBO Max	Streaming
`VIAPLAY`	Viaplay	Streaming
`TV2PLAY` or `TV 2 PLAY*`	TV2 Play	Streaming
`YOUTUBEPREMIUM` or `GOOGLEYOUTUBE`	YouTube Premium	Streaming
`APPLEMUSIC*`	Apple Music	Streaming
`FITNESSWORLD*`	Fitness World	Fitness
`FITNESSDK*`	Fitness DK	Fitness
`SATS`	SATS	Fitness
`TDC` or `YOUSEE`	TDC	Telefon
`TELENOR`	Telenor	Telefon
`TELIA`	Telia	Telefon
`ADOBE`	Adobe CC	Software
`MICROSOFT365` or `MICROSOFTOFFICE`	Microsoft 365	Software
`ICLOUD` or `APPLE.COM/BILL`	iCloud	Software
`DROPBOX`	Dropbox	Software

Restauranter (Dining)

Pattern	Merchant	Subcategory
`WOLT`	Wolt	Takeaway
`JUSTEAT*`	Just Eat	Takeaway
`TOO GOOD TO GO` or `TOOGOODTOGO`	Too Good To Go	Takeaway
`STARBUCKS`	Starbucks	Café
`JOETHEJUICE` or `JOE & THE JUICE`	Joe & The Juice	Café
`LAGKAGEHUSET`	Lagkagehuset	Café
`MCDONALDS` or `MC DONALDS`	McDonald's	Restaurant
`BURGERKING*`	Burger King	Restaurant

Shopping

Pattern	Merchant	Subcategory
`H&M` or `H M ` or `HM `	H&M	Tøj
`ZALANDO`	Zalando	Tøj
`IKEA`	IKEA	Bolig
`ELGIGANTEN`	Elgiganten	Elektronik
`POWER`	Power	Elektronik
`NORMAL`	Normal	Andet
`FLYINGTIGER*`	Flying Tiger	Andet
`SØSTRENEGRENE` or `SOSTRENEGRENE`	Søstrene Grene	Andet
`AMAZON`	Amazon	Andet
`JYSK`	Jysk	Bolig

Bolig (Housing)

Pattern	Merchant	Subcategory
`ØRSTED` or `OERSTED`	Ørsted	El
`HOFOR`	HOFOR	Vand
`TRYG`	Tryg	Forsikring
`TOPDANMARK`	Topdanmark	Forsikring
`ALKA` or `CODAN`	Codan	Forsikring
`HUSLEJE` or `FAST OVERFØRSELHUSLEJE*`	Husleje	Husleje
`NORLYS`	Norlys	El
`EWII`	Ewii	El

Sundhed (Health)

Pattern	Merchant	Subcategory
`APOTEK`	Apoteket	Apotek
`MATAS`	Matas	Apotek
`TANDLÆGE` or `TANDLAEGE`	Tandlæge	Tandlæge
`LÆGE` or `LAEGE`	Læge	Læge

Underholdning (Entertainment)

Pattern	Merchant	Subcategory
`NORDISKFILM*`	Nordisk Film	Biograf
`CINEMAXX`	CinemaxX	Biograf
`TICKETMASTER`	Ticketmaster	Koncert
`BILLETLUGEN`	Billetlugen	Koncert

Rejser (Travel)

Pattern	Merchant	Subcategory
`SAS` or `SCANDINAVIANAIRLINES*`	SAS	Fly
`NORWEGIAN`	Norwegian	Fly
`RYANAIR`	Ryanair	Fly
`AIRBNB`	Airbnb	Hotel
`HOTELS.COM` or `BOOKING.COM`	Booking.com	Hotel

Personlig pleje

Pattern	Merchant	Subcategory
`SEPHORA`	Sephora	Kosmetik
`FRISØR` or `FRISOER` or `CUTTERS`	Frisør	Frisør

Uddannelse (Education)

Pattern	Merchant	Subcategory
`SAXO`	Saxo	Bøger
`AMAZONKINDLE*`	Amazon Kindle	Bøger

Normalization Rules

Before matching patterns, normalize the raw transaction text:

Convert to uppercase for matching
Replace Danish characters: Ø → OE, Æ → AE, Å → AA (for pattern matching only — preserve originals in raw_text)
Collapse multiple spaces into one
Trim leading/trailing whitespace

Transaction Type Detection

Use the transaction description prefix to identify the payment method:

Prefix Pattern	Type	Notes
`Dankort-køb`	Card payment (physical)	Danish debit card
`Visa-køb`	Card payment (physical)	Visa card
`Overførsel`	Bank transfer	Internal or external
`Fast overførsel`	Standing order	Recurring transfer
`PBS` or `Betalingsservice`	Direct debit	Subscriptions and bills
`MobilePay`	Mobile payment	Person-to-person or merchant
`Løn fra`	Salary	Income — categorize as Indkomst
`Hævning`	ATM withdrawal	Categorize as Andet

Matching Order

When categorizing a transaction, check these sources in order — stop at the first match:

learnings/categorization.md — Check for user corrections learned from previous sessions (confidence 1.0). These take precedence over all other rules.
learnings/merchants.md — Check for merchant name aliases to normalize the raw text before pattern matching.
merchant-overrides.csv — Check for a matching raw_pattern (confidence 1.0). These are learned from previous user corrections within the same session.
Static pattern database — Check the merchant pattern tables above (confidence 1.0 exact / 0.8 partial).
Intelligent classification — No pattern match, but Claude can infer category from transaction context (confidence 0.5–0.7).
Fallback — Cannot determine category — assign to "Andet" (confidence 0.0).

Confidence Scoring

Assign a confidence score to each categorization:

Match Type	Confidence	Description
Merchant override match	1.0	Transaction text matches a learned override in merchant-overrides.csv
Exact pattern match	1.0	Transaction text matches a known merchant pattern
Partial pattern match	0.8	Part of the text matches a known pattern
Intelligent classification	0.5–0.7	No pattern match, but Claude can infer from context
Unknown	0.0	Cannot determine category — assign to Andet

When manual_override is TRUE for a transaction, always use the user's category regardless of what patterns suggest.

Learning from Corrections

When a user manually corrects a transaction's category, the correction should be saved to learnings/categorization.md so future sessions automatically apply the learned rule.

How to record a categorization learning:

Open learnings/categorization.md
Append a new row to the Learnings table with:
- Date: Today's date (YYYY-MM-DD)
- Pattern: The normalized raw text pattern to match (uppercase, wildcards allowed)
- Merchant: The normalized merchant name
- Category: The user-corrected category
- Subcategory: The user-corrected subcategory
- Context: Brief note about why this correction was made

Example learning entry:

| 2026-01-15 | *NETFLIX* | Netflix | Underholdning | Streaming | User: "Netflix er underholdning, ikke abonnement" |

Also update merchant-overrides.csv for the current session:

Take the corrected transaction's raw_text from transactions.csv
Normalize it: uppercase, trim whitespace, collapse multiple spaces
Use this as the raw_pattern
Use the user-corrected merchant, category, and subcategory from categorized.csv
Set created_at to the current timestamp
Only append if no row with the same raw_pattern already exists

Ambiguous Merchants

Netto: Always Dagligvarer, even though they sell non-grocery items
7-Eleven: Dagligvarer (most purchases are food/drink)
Amazon: Default to Shopping unless description contains "Prime" or "Kindle" (then Abonnementer or Uddannelse)
Normal: Shopping → Andet (sells mixed categories)
Matas: Sundhed → Apotek (primarily health/beauty)
Wolt: Restauranter → Takeaway unless the Wolt+ subscription charge (then Abonnementer)

Examples

Example 1: Clear Match

raw_text: "NETTO FO 1234 KØBENHAVN"
normalized: "NETTO FO 1234 KOEBENHAVN"
pattern match: *NETTO* → Netto
result: category=Dagligvarer, subcategory=Supermarked, merchant=Netto, confidence=1.0

Example 2: PBS Subscription

raw_text: "PBS FITNESS WORLD"
normalized: "PBS FITNESS WORLD"
pattern match: *FITNESS*WORLD* → Fitness World
result: category=Abonnementer, subcategory=Fitness, merchant=Fitness World, confidence=1.0, is_recurring=TRUE

Example 3: Intelligent Classification

raw_text: "RESTAURANT COFOCO KBH"
normalized: "RESTAURANT COFOCO KBH"
no pattern match — but "RESTAURANT" prefix suggests dining
result: category=Restauranter, subcategory=Restaurant, merchant=Cofoco, confidence=0.6

Example 4: Income

raw_text: "Løn fra Arbejdsgiver ApS"
prefix match: "Løn fra" → salary
result: category=Indkomst, subcategory=Løn, merchant=Arbejdsgiver ApS, confidence=1.0

Source

git clone https://github.com/peerjakobsen/smartspender/blob/main/skills/categorization/SKILL.mdView on GitHub

Overview

Provides Claude with Danish merchant knowledge and categorization rules to classify bank transactions into spending categories. It maps raw transaction text to normalized merchant names and 15 main categories with subcategories, enabling consistent budgeting and reporting in Danish contexts.

How This Skill Works

The skill uses a Merchant Pattern Database to map raw transaction text to normalized merchant names and a Danish subcategory. Patterns use wildcards (*) to match merchant names (e.g., *NETTO*, *DSB*, *LAGKAGEHUSET*) and assign the appropriate 15-category + subcategory pairing such as Dagligvarer: Supermarked or Transport: Offentlig.

When to Use It

When organizing Danish bank transactions into spending categories for budgeting
When you need consistent merchant name normalization from raw text
When analyzing recurring payments in Abonnementer or attempting to classify subscriptions
When handling new or ambiguous merchants that should map to Andet
When auditing or exporting categorized data for Danish financial reports

Quick Start

Step 1: Input the raw bank transaction text
Step 2: Run it through the Merchant Pattern Database to find a match
Step 3: Assign the corresponding normalized merchant and the Danish category/subcategory

Best Practices

Keep the 15 category definitions aligned with your chart of accounts
Regularly update merchant patterns to reflect new names and brands
Test wildcard patterns against common feeds to minimize misclassification
Prioritize exact matches before wildcard fallbacks
Maintain a fallback to Andet for unrecognized patterns

Example Use Cases

Pattern *NETTO* → Dagligvarer > Supermarked (Netto)
Pattern *DSB* → Transport > Offentlig
Pattern *NETFLIX* → Abonnementer > Streaming
Pattern *LAGKAGEHUSET* → Restauranter > Café
Pattern *WOLT* → Restauranter > Takeaway

Frequently Asked Questions

Add this skill to your agents