edgedelta-pipelines

EdgeDelta Pipelines Skill

Creates, validates, and deploys EdgeDelta pipeline v3 configurations. Provides production-tested templates, validation tools, environment inspection, and interactive pipeline building.

When to Use This Skill

Activate this skill when the user:

• Wants to create an EdgeDelta pipeline
• Asks about EdgeDelta configuration or monitoring
• Needs to collect logs, metrics, or traces
• Wants to validate a pipeline YAML
• Asks "what can I monitor" or wants environment inspection
• Mentions pipelines, telemetry, observability with EdgeDelta context

Core Capabilities

1. Quick Deploy: Choose from 7 production-tested templates
2. Custom Builder: Interactive pipeline creation with environment inspection
3. Validation: Check pipelines against EdgeDelta rules before deployment
4. Environment Discovery: Inspect K8s/Linux/Windows for monitoring opportunities
5. Direct Deployment: Deploy pipelines via EdgeDelta API

Quick Reference Searches

For fast lookups without loading full skill context:

# Find template by use case
grep -n "Use case:" SKILL.md

# Find template architecture
grep -n "Architecture:" SKILL.md

# Find validation rules
grep -n "validation" assets/scripts/validate_pipeline.py

# Find processor examples
grep -rn "type: generic_mask" assets/templates/

# Find API deployment script
ls assets/scripts/deploy_pipeline.py

# See all templates
ls assets/templates/template-*.yaml

Available Workflows

Workflow 1: Quick Template Deployment

When: User wants to quickly deploy a pipeline

Steps:

1. Ask for EdgeDelta credentials:

   • Organization ID
   • API Token
   • Check ~/.edgedelta.env or project .env file first
   • Store in conversation context

2. Present template options:

   • Template 1: Log Ingestion with PII Masking

     • Use case: Application logs with compliance requirements
     • Architecture: file_input → sequence (3x generic_mask + extract_metric) → ed_output

   • Template 2: OTLP Dual Receiver

     • Use case: OpenTelemetry data collection
     • Architecture: otlp_input (gRPC+HTTP) → sequence (transform + metrics) → ed_output