Crypto Review Lab (CRL)

     

Algorithmic Security Intelligence for Digital Assets & Tokenized Equities
Powered by the Algorithmic Verification Framework (AVF) Tripartite Core Engine & Blueprint v2.4.

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📌 Overview

Crypto Review Lab (CRL) is an independent cryptocurrency research, security evaluation, and asset verification platform. Designed for decentralized finance (DeFi) protocols, digital asset treasuries, web3 developers, and market participants, CRL delivers rigorous, data-driven security evaluations, real-time telemetry surveillance, and independent transparency across native crypto assets and tokenized real-world equities (xStocks).

All evaluations are governed by the deterministic Algorithmic Verification Framework (AVF) and audited through an automated 8-Gate Re-Control pipeline and multi-module deterministic verification layer with deterministic verification with no AI estimation in F3.

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⚙️ Core Architecture: AVF Tripartite Engine

The AVF architecture operates three sequential execution layers to ensure mathematical precision and reproducible auditing:

┌────────────────────────────────────────────────────────┐
│  F1 — Candidate Engine (Telemetry & Draft Vectors)     │
└───────────────────────────┬────────────────────────────┘
                            │
┌───────────────────────────▼────────────────────────────┐
│  F2 — Phase Two Re-Control (8-Gate Quality Pipeline)   │
└───────────────────────────┬────────────────────────────┘
                            │
┌───────────────────────────▼────────────────────────────┐
│  F3 — Deterministic Verification (8 AVF Rule Modules)  │
└────────────────────────────────────────────────────────┘

1. F1 — Candidate Engine

Automatically ingests contract bytecode, tokenomics schedules, and multi-node on-chain telemetry (GoPlus, RugCheck, Blockscout, CoinGecko, CoinMarketCap) to generate initial multidimensional evaluation vectors.

2. F2 — Phase Two Automated Re-Control Engine

Candidate evaluations are subjected to 8 Automated Control Gates (Gate 0–7) enforcing strict consistency and convergence:

• Gate 0 — Structural Completeness Check: Verifies presence of mandatory fields (name, symbol, scores, summary, verdict, pros/cons).
• Gate 1 — Multi-Source Triangulation: Cross-verifies market telemetry across CoinGecko, CMC, and related price/volume feeds.
• Gate 2 — On-Chain Cross-Check: Verifies on-chain scan cleanliness and contract parameters (open-source bytecode, honeypot risk, mintable supply, blacklist, taxes, concentration, custody, and exploit vectors). When live GoPlus/RugCheck telemetry is present, Gate 2 is evaluated from a neutral baseline of 90 (applying scan adjustments) rather than anchoring to the F1 subjective security dimension; falls back to F1 security score × 10 only if scan telemetry is unavailable. Requires score ≥ 90% and no honeypot detection to pass.
• Gate 3 — Cross-Framework Consistency Check: Enforces strict < 3.0 point composite score drift convergence between F1 and F2 models.
• Gate 4 — Tokenomics Re-Verification: Evaluates token distribution models, circulating vs. max supply ratios, and unlock overhang.
• Gate 5 — Score Arithmetic Check: Recomputes category-weighted dimension math to ensure exact mathematical consistency without drift.
• Gate 6 — Risk Level Evidence Check: Verifies that a declared "Low / Low Risk" classification is not contradicted by critical honeypot or cannot-sell-all telemetry.
• Gate 7 — Formatting Integrity: Checks presence of core narrative sections (via keyword), pros/cons balance (≥3 each), and verdict length.

3. F3 — Deterministic Verification Layer

Executes 8 automated verification modules with strictly zero AI / LLM estimation:

1. AVF-01 (Classification & Taxonomy): Validates protocol taxonomy against CoinGecko and DEX market categories.
2. AVF-02 (Evidence & Provenance Traceability): Verifies public documentation links, official block explorer registries, and security feeds.
3. AVF-03 (Methodology & Weighting Compliance): Verifies that weights are category-specific and deterministically fixed per protocol category (not a single universal split), sourced from getCategoryDimensionWeights() in EvaluationBlueprint.ts, and applied without ad-hoc parameter tampering.
4. AVF-04 (Scenario Readiness & Stress-Input Verification): Tracks scenario readiness and lifecycle state for stress-test inputs (TVL, live price, contract address) and F1/F2 convergence.
5. AVF-05 (Score Arithmetic & Zero-Drift Verification): Algorithmically recomputes weighted averages and confirms aggregation math within a strict ±0.5 point tolerance.
6. AVF-06 (Semantic Risk Consistency Check): Verifies whether the declared official risk classification is supported by independent evidence and security telemetry; decoupled from numerical score calculation.
7. AVF-07 (Calibrated Data Confidence Scoring): Deterministic confidence calculation across verified on-chain addresses, public audits, and telemetry depth (HIGH ≥85%, MODERATE 70–84%, LOW <70%).
8. AVF-08 (Cryptographic Provenance & Attestation): Normalizes canonical audit payloads, computes SHA-256 digests, and validates Ed25519 digital signatures.

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📊 100-Point Master Scoring Formula & Category Weight Matrix

Linear Weight Formulation

A project's numerical score is calculated across 5 core dimensions (evaluated on a 1.0 to 10.0 scale):

$$\text{Score (100)} = (\text{Utility} \times M_U) + (\text{Tokenomics} \times M_T) + (\text{Security} \times M_S) + (\text{Team} \times M_{\text{Tm}}) + (\text{Community} \times M_C)$$

Where each multiplier $M_i = \text{Weight}_i \times 10$. Multipliers are not a single fixed split; they vary deterministically by protocol category.

Deterministic Category Dimension Weights

Weights are category-specific (not a single fixed split) and sourced from getCategoryDimensionWeights() in EvaluationBlueprint.ts. Examples:

• DeFi: 25/20/35/10/10 (Security emphasized)
• Layer 1: 25/20/30/10/15
• Default / Specialized: 25/25/25/15/10

Full matrix by protocol category is in ARCHITECTURE.md and the Technical Whitepaper.

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🛡️ The Fundamental Decoupling Principle

CRL enforces an absolute separation between Numerical Evaluative Scores (/100) and Assessed Risk Severity Classifications:

• Evaluative Scores (/100): Measure market-product utility, network adoption, token design, and developer velocity. A high adoption protocol can legitimately achieve a score of 85/100.
• Risk Severity (Low Risk, Medium Risk, Declared Risk / Critical): Evaluates empirical capital vulnerability. If automated security telemetry detects unverified delegatecalls, centralized mint functions, or treasury custody concentration, the protocol is assigned an overriding HIGH or CRITICAL risk classification.
• AVF-06 Verification: Confirms that a declared low risk rating is corroborated by evidence. It does not overwrite the official classification from the numerical score.

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📑 Technical Whitepaper & Cryptographic Attestation

• Official 6-Page Technical Whitepaper PDF: Fully comprehensive, authoritative documentation detailing the 5-Dimension Master Scoring Rubric, the Category Weight Matrix, the 8-Gate Re-Control pipeline, the AVF Tripartite Core (F1/F2/F3), and all 8 Verification Modules. Can be exported directly via the platform UI.
• Audit Dossier PDF: Exportable comprehensive asset review reports complete with SHA-256 canonical hashing and Ed25519 digital cryptographic signatures for cryptographic provenance (SHA-256 + Ed25519).

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📈 Verification & Integrity Panel (Tokenized Stocks / xStocks)

A dedicated surveillance engine designed for tokenized equities (such as Backed Finance collateralized tracker certificates on Solana and EVM):

• Market-Price Consistency: Cross-corroborates token market prices across independent aggregators (CoinGecko and CoinMarketCap) to ensure pricing consensus across decentralized liquidity pools.
• Underlying-Equity Tracking: Tracks tokenized stock spot prices against real-world equity markets using Finnhub market data feeds to measure tracking parity and detect pricing divergences.
• On-Chain Security & Authority: Continuously scans token mint authorities, freeze controls, and contract ownership structures for centralized control risks.
• Deterministic Provenance Matrix: Key-level data provenance tracking (Source vs. Derived data, provider timestamps, freshness status, and verification status).
• Live Market Hours Engine: Tracks real-time NYSE/NASDAQ market sessions, holidays, pre-market, regular hours, and after-hours trading with dynamic status telemetry.

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🛠️ Technology Stack

• Frontend: React 19, Vite 6, TypeScript 5.8, Tailwind CSS v4, Lucide React, Framer Motion
• Analytics & Visualizations: Recharts, D3-based charting
• PDF & Cryptography: jsPDF, Web Crypto API (Ed25519, SHA-256)
• Data Sources & Security Providers: CoinGecko API, CoinMarketCap API, Finnhub Stock API, GoPlus Security, RugCheck, Blockscout
• Backend: Node.js, Express proxy routes with server-side secret management

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🚀 Getting Started

Prerequisites

• Node.js 18.x or higher
• npm or yarn

Installation & Run

# Clone repository
git clone https://github.com/chokri29/Crypto-Review-Lab.git
cd Crypto-Review-Lab

# Install dependencies
npm install

# Start local development server
npm run dev

# Run TypeScript checks
npm run lint

# Build for production
npm run build

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☁️ Production Ops & Deployment Notes

1. Postgres Database Migrations (Cloud SQL):

   • Before executing any F2/F3 review writes or enabling DB persistence in production, take a full database backup of your Cloud SQL instance.
   • Apply the migration script drizzle/0004_clumsy_penance.sql to Cloud SQL:

     ALTER TABLE "crypto_reviews" ALTER COLUMN "scores" SET DATA TYPE jsonb USING "scores"::jsonb;
     ALTER TABLE "crypto_reviews" ALTER COLUMN "pros" SET DATA TYPE jsonb USING "pros"::jsonb;
     ALTER TABLE "crypto_reviews" ALTER COLUMN "cons" SET DATA TYPE jsonb USING "cons"::jsonb;
     ALTER TABLE "crypto_reviews" ALTER COLUMN "security_scan" SET DATA TYPE jsonb USING "security_scan"::jsonb;
     ALTER TABLE "crypto_reviews" ADD COLUMN "phase_two_re_control" jsonb;
     ALTER TABLE "crypto_reviews" ADD COLUMN "audit_signature" jsonb;
     ALTER TABLE "crypto_reviews" ADD COLUMN "f3_verification" jsonb;
     ALTER TABLE "crypto_reviews" ADD COLUMN "is_signed" boolean DEFAULT false;

     (Note: If any existing rows contain invalid JSON strings, clean or cast them before running the ::jsonb migration).

2. Cloud Scheduler for Market Intelligence Sync:

   • On serverless container runtimes (such as Google Cloud Run with scale-to-zero), in-process setInterval timers are suspended when instances are idle.
   • Set ENABLE_IN_PROCESS_TIMERS="false" in production .env.
   • Configure an external Cloud Scheduler job to trigger POST https://your-cloud-run-url/api/market-intelligence/sync with header x-sync-secret: YOUR_SYNC_SECRET (every 15–60 minutes).

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📄 License & Disclaimer

Crypto Review Lab provides algorithmic verification, market-data synthesis, and technical diagnostics for research and transparency purposes. Nothing in this application constitutes financial or investment advice.