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ajmeyers42
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weave-pipe

Generate Elasticsearch ingest pipeline JSON from field mapping specifications.

1|Updated Apr 15, 2026
Search Tags:#automation#grok#pipeline#ecs#elasticsearch#ingest
One-click install
npx skills add https://github.com/ajmeyers42/loom --skill weave-pipe
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weave-pipe
Source: https://github.com/ajmeyers42/loom/tree/main/skills/weave-pipe
Command: npx skills add https://github.com/ajmeyers42/loom --skill weave-pipe

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designs and generates Elasticsearch ingest pipeline definitions from a field mapping specification, producing deployment-ready pipeline JSON artifacts for Streams wiring or direct Elasticsearch API deployment.

Core Features & Use Cases

  • Analyze source log formats (JSON vs unstructured) and generate appropriate processors (json, grok, dissect)
  • Apply ECS normalization and enrichment wiring to normalize data and enrich events
  • Output deployment-ready pipeline artifacts in data/ingest-pipelines and registers manifests for deployment
  • Support simulation mode and guaranteed on_failure handling to prevent dropped documents
  • Enable per-tenant routing by producing a root pipeline that dispatches to tenant-specific sub-pipelines

Quick Start

Feed a data-model.json and a demo-script.md to weave-pipe to generate the ingest pipelines.

Frequently Asked Questions about weave-pipe

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate Elasticsearch ingest pipelines from field mappings?▼

Elasticsearch ingest pipelines can be generated from field mappings by providing a data-model specification, which produces deployment-ready pipeline JSON artifacts for direct API deployment or Streams wiring.

What is the best way to normalize log data to ECS in an ingest pipeline?▼

ECS normalization is applied during pipeline generation by mapping source fields to ECS standards, automatically wiring enrichment processors to normalize events within the deployment-ready JSON artifacts.

How do I handle unstructured logs and JSON extraction in Elasticsearch pipelines?▼

Elasticsearch pipeline generation analyzes source log formats to apply appropriate extraction processors, automatically selecting grok or dissect for unstructured text and json parsing for structured data.

Can I route logs to tenant-specific pipelines in Elasticsearch?▼

Tenant-specific routing is supported by generating a root ingest pipeline that dynamically dispatches events to tenant-specific sub-pipelines based on routing logic.

How do I prevent dropped documents during Elasticsearch ingest pipeline failures?▼

Dropped documents are prevented during ingest pipeline failures through guaranteed on_failure handling, which ensures errors are caught and managed without losing data during processing.

What inputs do I need to start automating Elasticsearch pipeline creation?▼

Automating Elasticsearch pipeline creation requires feeding a data-model.json file containing field mappings and a demo-script.md file to define extraction, ECS normalization, and routing logic.

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