xiaohongshu
xiaohongshu
blockchain-rpc-provider-research
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.
Full skill instructions
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.
This skill follows a 5-step workflow. Each step builds on the previous:
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.
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.
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).
calculate_timeline.py - Calculate collection timeline from rate limits (RPS or compute units)test_rpc_rate_limits.py - Empirical rate limit testing templateUsage guide: See scripts/README.md for detailed usage examples, configuration options, and success criteria.
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 selectionreferences/validated-providers.md - Alchemy vs LlamaRPC vs Infura vs QuickNode empirical comparisonreferences/rpc-comparison-template.md - Template for creating provider comparison matricesscripts/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 templateCase 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.
Invoke this skill when:
This skill pairs well with:
blockchain-data-collection-validation - For validating the complete data pipeline after provider selectionscratch/ethereum-collector-poc/ and scratch/rpc-provider-comparison/xiaohongshu
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