blockchain-rpc-provider-research

name	blockchain-rpc-provider-research
description	Systematic workflow for researching and validating blockchain RPC providers. Use when evaluating RPC providers for historical data collection, rate limits, archive access, compute unit costs, or timeline estimation for large-scale blockchain data backfills.

Blockchain RPC Provider Research

Overview

This skill provides a systematic, empirically-validated workflow for researching blockchain RPC providers before committing to large-scale data collection projects. Use when selecting an RPC provider for historical blockchain data backfill, evaluating rate limits, comparing free tier options, or estimating collection timelines.

Key principle: Never trust documented rate limits—always validate empirically with POC testing.

Investigation Workflow

This skill follows a 5-step workflow. Each step builds on the previous:

1. Research Official Documentation - Survey provider docs, pricing, archive access
2. Calculate Theoretical Timeline - Compute expected collection time from documented limits
3. Empirical Validation with POC Testing - Test actual rate limits (CRITICAL STEP)
4. Create Comparison Matrix - Build side-by-side provider comparison
5. Document Findings and Make Recommendation - Write comprehensive analysis report

Detailed workflow: See references/workflow-steps.md for complete step-by-step guide with code examples, questions to answer, and success criteria.

Quick start: For immediate testing, jump to Step 3 (Empirical Validation) using scripts/test_rpc_rate_limits.py.

Rate Limiting Best Practices

When implementing the selected provider, use conservative targeting (80-90% of empirically validated rate) with monitoring and fallback strategy.

Full guide: See references/rate-limiting-guide.md for detailed monitoring requirements, fallback strategies, and safety margins.

Common Pitfalls

Critical mistakes to avoid: Trusting documented burst limits (always validate empirically), testing with <50 blocks, parallel fetching on free tiers, ignoring compute unit costs, and forgetting archive access restrictions.

Full guide: See references/common-pitfalls.md for detailed anti-patterns with real-world examples (e.g., LlamaRPC 50 RPS → 1.37 RPS case).

Scripts

• calculate_timeline.py - Calculate collection timeline from rate limits (RPS or compute units)
• test_rpc_rate_limits.py - Empirical rate limit testing template

Usage guide: See scripts/README.md for detailed usage examples, configuration options, and success criteria.

References

Workflow Documentation

• references/workflow-steps.md - Complete 5-step workflow with detailed guidance for each step
• references/rate-limiting-guide.md - Best practices for conservative rate targeting and monitoring
• references/common-pitfalls.md - Anti-patterns to avoid with real-world examples
• references/example-workflow.md - Complete case study: 13M Ethereum blocks RPC selection

Data References

• references/validated-providers.md - Alchemy vs LlamaRPC vs Infura vs QuickNode empirical comparison
• references/rpc-comparison-template.md - Template for creating provider comparison matrices

Scripts

• scripts/README.md - Complete usage guide for all scripts
• scripts/calculate_timeline.py - Timeline calculator (RPS and compute unit modes)
• scripts/test_rpc_rate_limits.py - Empirical rate limit testing template

Example Workflow

Case study: Selecting RPC provider for 13M Ethereum blocks → Alchemy chosen at 5.79 RPS (26 days timeline, 4.2x faster than LlamaRPC).

Full walkthrough: See references/example-workflow.md for complete step-by-step case study showing research, calculation, validation, comparison, and final recommendation.

When to Use This Skill

Invoke this skill when:

• Evaluating blockchain RPC providers for a new project
• Planning historical data backfill timelines
• Comparing free tier vs paid provider options
• Investigating rate limiting issues with current provider
• Estimating collection timelines for multi-million block datasets
• Validating archive node access for historical queries
• Researching compute unit or API credit costs
• Building POC before production implementation

Related Patterns

This skill pairs well with:

• blockchain-data-collection-validation - For validating the complete data pipeline after provider selection
• Project scratch investigations in scratch/ethereum-collector-poc/ and scratch/rpc-provider-comparison/