Wallet-score-checker

Wallet Score Checker is a front-end demo for a wallet-level trust signal: an analogy to how credit bureaus (for example CIBIL in India) turn repayment history into a bounded score and short explanations so many lenders share a common lens. Blockchains do not have payslips or EMIs, but they do have years of verifiable behaviour tied to each address. This project asks a softer question: given what this wallet has actually done on-chain, how much trust should I start with?

It is not claiming to predict loan repayment or to replace legal due diligence. It frames the score as one structured input about history and hygiene, alongside whatever else you already use (allowlists, governance, manual review).

Why something like this matters in Web3 Teams often rely on allowlists, social reputation, snapshot votes, or intuition before moving treasury funds, onboarding contributors, or extending DeFi-style exposure. That does not scale. A portable wallet score—used as one signal among many—could reduce friction for legitimate actors and flag wallets that look new, recycled, or entangled with risky flows. The /docs page (“The idea”) spells out this motivation explicitly.

TradFi analogy vs chain reality Similar: a familiar band (here 300–850), named tiers (e.g. Strong / Stable / Developing / Elevated risk), and short reason lines so the output is discussable, not a silent black box.

Different: there is no bank reconciling your ledger. Signals would come from transactions, counterparties, protocols, timing, and optionally attestations (ENS, credentials). Privacy and pseudonymity mean any real product must separate what is inferred vs proven and what users consent to share.

What a serious scorer might look at (conceptual pillars) The docs describe pillars that a backend could ingest (with ML optional):

Activity and throughput — how often the wallet transacts, over 30/90/365-day windows, steady vs suspicious bursts; thin history limits confidence (like a thin credit file). Breadth across protocols — interaction across DEXs, lending, staking, NFT venues, bridges vs repetitive dust patterns. Recency and continuity — last activity, long dormancy, sudden reactivation after silence. Counterparty and risk hygiene — ties to known exploit-related flows, risky routers, layering-like patterns; policy-driven lists, similar in spirit to bureau exclusion logic. Maturity and proxies — wallet age, nonce/tx counts, optional identity layers (ENS, verifiable credentials). Optional AI — compressing messy trails into anomalies or clusters, ideally still tied to explainable drivers. Those pillars can be weighted and combined, then mapped into 300–850 with caps when data is sparse so scores do not overclaim.

It’s a Next.js site (App Router) with React and TypeScript. The UI is Tailwind CSS plus small Radix-based components (buttons, inputs, cards), Lucide icons, and light Framer Motion for transitions.

Wallet flow: RainbowKit sits on wagmi and viem so users can connect an injected wallet (e.g. MetaMask) and see chain + address. Addresses can also be pasted; viem checks they’re valid.

Extras: Basic SEO—metadata, Open Graph / Twitter images, sitemap, robots, manifest—so the project looks polished when shared.

Partner tech (why): RainbowKit + wagmi + viem = standard Ethereum connect + validation without building that from scratch. Tailwind = fast layout; Motion = a bit of polish.

Nothing wild: No exotic architecture—mostly a single-page checker, a /docs idea page, and backend-driven score fetching.