webhook-transforms

Transform external webhook events into cited brain pages via a transform-write-enrich pipeline.

1|Updated May 12, 2026
One-click install
npx skills add https://github.com/ltdrew/Agent-brain-2 --skill webhook-transforms-ltdrew
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: webhook-transforms
Source: https://github.com/ltdrew/Agent-brain-2/tree/main/skills/webhook-transforms
Command: npx skills add https://github.com/ltdrew/Agent-brain-2 --skill webhook-transforms-ltdrew

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

External events (SMS, meetings, social mentions) arrive as raw payloads that must be transformed into brain-friendly pages with proper citations. This framework provides a deterministic path to map payloads, sanitize input, and preserve raw payloads for auditing and retries.

Core Features & Use Cases

  • Define a transform function that maps inbound payloads to brain pages (markdown) and metadata.
  • Register a webhook URL and route events through the brain pipeline for automated ingestion, entity extraction, and timeline enrichment.
  • Robust error handling: dead-letter queue for failed payloads, single retry, and no data loss.

Quick Start

Register the webhook endpoint and configure a transform to begin processing events.

Frequently Asked Questions about webhook-transforms

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

FAQPage Schema
How do I transform external webhook events into markdown pages for ingestion?

To transform external webhook events into markdown pages, you define a transform function that maps inbound payloads to brain pages and metadata, then route events through a transform-write-enrich pipeline. This sanitizes inputs and preserves raw payloads for auditing.

What is the best way to automate SMS and meeting payload ingestion into a knowledge base?

Automating SMS and meeting payload ingestion involves registering a webhook URL and configuring a deterministic transform to map the raw payload data into brain-friendly markdown pages with proper citations, entity extraction, and timeline enrichment.

How does data sanitization work when processing inbound webhook payloads?

Data sanitization occurs during the initial transform stage of the pipeline, cleaning the inbound webhook payloads before they are mapped to markdown pages, while simultaneously preserving the original raw payloads for auditing and retry purposes.

Can I recover failed webhook payloads if the entity extraction process fails?

You can recover failed webhook payloads using a dead-letter queue that prevents data loss. The system preserves the raw payload and applies a single retry mechanism for failed transforms to ensure no inbound events are permanently dropped.

Does this webhook transformation pipeline support social mentions and timeline enrichment?

The webhook transformation pipeline supports social mentions by routing the payload through an automated ingestion process that handles entity extraction and timeline enrichment, converting the external event data into a brain-ready signal with citations.