historical-backfill-execution

terrylica/gapless-network-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.

0 stars

0 forks

5 views

View on GitHub Add to Favorites

Installation

Option 1: Use slash command in Claude Code

/install-skill https://github.com/terrylica/gapless-network-data/tree/main/.claude/skills/historical-backfill-execution

Option 2: Clone to skills directory

# Global (all projects)

git clone https://github.com/terrylica/gapless-network-data/tree/main/.claude/skills/historical-backfill-execution ~/.claude/skills/gapless-network-data

# Project-specific

git clone https://github.com/terrylica/gapless-network-data/tree/main/.claude/skills/historical-backfill-execution .claude/skills/gapless-network-data

Add MCP server to .cursor/mcp.json:

{
  "mcpServers": {
    "skillz": {
      "command": "npx",
      "args": ["-y", "skillz-mcp", "https://github.com/terrylica/gapless-network-data/tree/main/.claude/skills/historical-backfill-execution"]
    }
  }
}

Restart Cursor after adding the configuration.

Option 1: Use Gemini CLI command

gemini extensions install https://github.com/terrylica/gapless-network-data/tree/main/.claude/skills/historical-backfill-execution

Option 2: Clone to extensions directory

git clone https://github.com/terrylica/gapless-network-data/tree/main/.claude/skills/historical-backfill-execution ~/.gemini/extensions/gapless-network-data

Related Skills

skill-writer

Tensors and Dynamic neural networks in Python with strong GPU acceleration

docstring

Tensors and Dynamic neural networks in Python with strong GPU acceleration

at-dispatch-v2

Tensors and Dynamic neural networks in Python with strong GPU acceleration

add-uint-support

Tensors and Dynamic neural networks in Python with strong GPU acceleration