What problem does it solve?
Users struggle with unreliable RPC provider data, leading to slow, costly, or failed blockchain data collection. This skill provides a systematic, empirically-validated workflow to select the best RPC provider, avoiding common pitfalls like trusting documented rate limits. It ensures your large-scale blockchain data backfills are efficient and accurate.
Core Features & Use Cases
- Empirical Rate Limit Validation: Never trust documented limits—test actual sustained rates to prevent 429 errors and ensure consistent data flow.
- Timeline Estimation: Accurately predict historical data backfill durations for multi-million block datasets, optimizing resource allocation.
- Provider Comparison: Create side-by-side matrices to evaluate RPC providers on cost, archive access, compute unit costs, and validated performance.
- Use Case: You need to backfill 13 million Ethereum blocks. Use this skill to compare Alchemy, Infura, and LlamaRPC, empirically validate their true sustained RPS, and select the provider that completes the task in 26 days instead of 110 days, all within free tier limits.
Quick Start
Help me research blockchain RPC providers for historical Ethereum data. I need to compare Alchemy, Infura, and LlamaRPC for a 13 million block backfill. Focus on empirical rate limit validation and timeline estimation.
