菜单
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.
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基于 SOC 职业分类
Autonomous setup and management of external service monitoring using UptimeRobot (HTTP endpoint monitoring) and Healthchecks.io (heartbeat/Dead Man's Switch monitoring). Use when setting up monitoring for Cloud Run Jobs, VM services, or investigating monitoring configuration issues. Includes Pushover integration, validated API patterns, and dual-service architecture.
Workflow for acquiring historical Ethereum blockchain data using Google BigQuery free tier. Empirically validated for cost estimation, streaming downloads, and DuckDB integration. Use when planning bulk historical data acquisition or comparing data source options for blockchain network metrics.
Empirical validation workflow for blockchain data collection pipelines before production implementation. Use when validating data sources, testing DuckDB integration, building POC collectors, or verifying complete fetch-to-storage pipelines for blockchain data.
Execute chunked historical blockchain data backfills using canonical 1-year pattern. Use when loading multi-year historical data, filling gaps in ClickHouse, or preventing OOM failures on Cloud Run. Keywords chunked_backfill.sh, BigQuery historical, gap filling, memory-safe backfill.
Troubleshoot and manage GCP e2-micro VM running eth-realtime-collector. Use when VM service is down, systemd failures occur, real-time data stream stops, or VM network issues arise. Keywords systemd, journalctl, eth-collector, gcloud compute.
Monitor and troubleshoot dual-pipeline data collection systems on GCP. This skill should be used when checking pipeline health, viewing logs, diagnosing failures, or monitoring long-running operations for data collection workflows. Supports Cloud Run Jobs (batch pipelines) and VM systemd services (real-time streams).
| 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. |
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/