Overcollateralized borrowers deposit funds into Sky and receive DAI stablecoin.

Sky is a non-custodial stablecoin protocol built on Ethereum. Sky (previously MakerDAO) was founded by Rune Christensen and launched in 2015.

SKY tokenholders own and govern the protocol. They currently take all of the total interest and PSM fees paid by borrowers (revenue). Sky’s investors include a16z, Polychain, IOSG Ventures, etc.

governance

github

twitter

discord

etherscan

website

Stablecoin issuers

Sky | Fees and revenues breakdown

# Sky Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interact with the Sky protocol (formerly MakerDAO) within any 7-day period. This metric captures user engagement across borrowing, lending, collateral management, and PSM (Peg Stability Module) trading activities. The calculation uses a rolling 7-day window approach with HyperLogLog probabilistic data structures to efficiently count distinct users without double-counting those active on multiple days within the same week.

## 2. Data sources

- **Ethereum Vat contract function calls**: Parsed function calls to the Sky protocol's core Vat contract, specifically `frob`, `grab`, and `fold` functions that handle vault modifications, liquidations, and debt adjustments
- **Ethereum transaction records**: Complete transaction data including sender addresses (`from_address`) for all Sky protocol interactions
- **Sky protocol collateral metadata**: Collateral type identifiers and names extracted from contract calls, with special identification of PSM (Peg Stability Module) collateral types
- **Blockchain event logs**: Ethereum event logs capturing successful vault operations and state changes

## 3. Step-by-step calculation

### Component A: User identification and categorization

- **Purpose**: Identify all Sky protocol users and categorize them by interaction type
- **Logic**: Extract all successful Vat contract function calls (`frob`, `grab`, `fold`) from Ethereum. For `frob` function calls, examine the debt change parameter (`dart`) and collateral change parameter (`dink`) to classify user behavior. Users with positive `dart` values are classified as borrowers, negative `dart` as repayers. Users with positive `dink` values are depositors, negative `dink` as withdrawers. Special handling for PSM collateral types - interactions with collateral names containing 'PSM-' pattern are classified as traders regardless of debt/collateral changes. Regular vault operations exclude PSM collateral types to separate trading from borrowing/lending activities.
- **Key fields**: `block_timestamp`, `transaction_hash`, `collateral_id`, `dart` (debt change), `dink` (collateral change), `user_type` (borrower/repayer/depositor/withdrawer/trader)

### Component B: Address resolution and business categorization

- **Purpose**: Link contract interactions to user addresses and apply business segment classification
- **Logic**: Join vault interaction data with Ethereum transaction records using `transaction_hash` to retrieve the transaction sender (`from_address`) as the user address. Apply standardized business categorization with `market_sector_id` as 'stablecoin_issuers', `business_segment_id` as 'stablecoin_issuer', and `product_id` as 'usds' for all interactions.
- **Key fields**: `user_address`, `market_sector_id`, `business_segment_id`, `product_id`

### Component C: Daily active user aggregation

- **Purpose**: Calculate daily distinct active users as the foundation for weekly aggregation
- **Logic**: Group all user interactions by day using `timestamp_trunc` to the day level. Count distinct `user_address` values for each combination of date, chain (ethereum), `user_type`, and `product_id`. This creates a daily active user (DAU) dataset that serves as input for weekly calculations.
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` (day), `chain`, `user_type`, `product_id`, `user_dau`

### Component D: HyperLogLog sketch creation

- **Purpose**: Convert daily user counts to probabilistic data structures for efficient weekly aggregation
- **Logic**: Transform daily active user records into HyperLogLog sketches using `hll_count.init` with precision parameter 15. This allows efficient merging of user sets across multiple days without maintaining individual user identifiers or risking double-counting.
- **Key fields**: `hll` (HyperLogLog sketch of daily users)

### Component E: Seven-day rolling window construction

- **Purpose**: Create overlapping 7-day periods for each date to calculate weekly active users
- **Logic**: Generate a complete date spine from the earliest available data to the current date. For each date in the spine, create a 7-day window by generating timestamps for the target date and the 6 preceding days. Join this window with the daily HLL sketches to collect all relevant daily user data for each weekly calculation.
- **Key fields**: `timestamp` (target date), `joinday` (each day in the 7-day window)

### Component F: Weekly user aggregation by dimension

- **Purpose**: Calculate weekly active users across different analytical dimensions
- **Logic**: For each 7-day window, merge all HLL sketches using `hll_count.merge_partial` and extract the final count with `hll_count.extract`. Create separate aggregations for: project-level (all dimensions set to null), chain-level (grouped by `chain_id`), user type level (grouped by `user_type`), and product level (grouped by `product_id`). Each aggregation includes a `dimensions` field indicating the breakdown level.
- **Key fields**: `dimensions`, `chain_id`, `product_id`, `user_type`, `user_wau` (weekly active user count)

### Final aggregation

- **Combining components**: Union all dimensional breakdowns (project-level, chain-level, user type, product-level) into a single dataset with consistent schema
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses rolling 7-day windows calculated daily, with each date representing the WAU for that date and the preceding 6 days
- **Final calculation**: Apply the `finalize_metric_user_wau` macro to standardize the output format, add metadata fields, and implement zero-filling strategy for dates with no user activity. The final `user_wau` value represents the estimated count of unique users who were active within the 7-day window ending on each date.

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 7-day window.

SkySKY

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown