| 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:
- Research Official Documentation - Survey provider docs, pricing, archive access
- Calculate Theoretical Timeline - Compute expected collection time from documented limits
- Empirical Validation with POC Testing - Test actual rate limits (CRITICAL STEP)
- Create Comparison Matrix - Build side-by-side provider comparison
- 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 stepreferences/rate-limiting-guide.md - Best practices for conservative rate targeting and monitoringreferences/common-pitfalls.md - Anti-patterns to avoid with real-world examplesreferences/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 comparisonreferences/rpc-comparison-template.md - Template for creating provider comparison matrices
Scripts
scripts/README.md - Complete usage guide for all scriptsscripts/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/
