dag-pattern-learner

Name: dag-pattern-learner
Availability: InStock
Author: Curiositech

Extract patterns and generate recommendations from DAG execution history.

10|1|Updated Mar 17, 2026

Search Tags:#analytics #optimization #learning #patterns #observability #graph #dag

Ready to run

npx skills add https://github.com/curiositech/windags-skills --skill dag-pattern-learner

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dag-pattern-learner
Source: https://github.com/curiositech/windags-skills/tree/main/skills/dag-pattern-learner
Command: npx skills add https://github.com/curiositech/windags-skills --skill dag-pattern-learner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, correlates configurations with outcomes, and provides recommendations that improve future DAG performance.

Core Features & Use Cases

Pattern extraction from DAG execution traces to identify recurring structures and actionable templates.
Anti-pattern detection to surface risky configurations, bottlenecks, and dependencies.
Recommendation generation to optimize configurations, parallelization, and scheduling order.
Knowledge accumulation to build and maintain a library of patterns, outcomes, and confidence scores for continuous improvement.

Quick Start

Provide your DAG execution history to begin learning patterns and receiving actionable recommendations.

Frequently Asked Questions about dag-pattern-learner

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

FAQPage Schema

How do I optimize DAG workflows using execution history?▼

DAG workflow optimization from execution history involves analyzing past execution traces to extract successful patterns, detect anti-patterns, and generate actionable recommendations for improving future data pipeline performance.

What anti-patterns can be detected in data pipeline execution traces?▼

Data pipeline execution traces reveal anti-patterns such as risky configurations, performance bottlenecks, and problematic dependencies that correlate with failures, allowing you to correct structural issues before future runs.

What data do I need to provide for DAG pattern learning and observability?▼

DAG pattern learning requires access to execution traces, performance data, and failure signals from your data pipelines to extract recurring structures and generate accurate optimization recommendations.

Can I use execution history to recommend better parallelization and scheduling for DAGs?▼

Execution history can be used to recommend better parallelization and scheduling order by correlating past configurations with outcomes, identifying the most successful structures, and applying those templates to future runs.

How does a pattern learner maintain knowledge for continuous pipeline optimization?▼

A pattern learner maintains continuous pipeline optimization by building a knowledge library that accumulates execution patterns, outcomes, and confidence scores, refining its recommendations as more historical data is processed.

When should I not rely on historical patterns for DAG performance optimization?▼

Historical DAG performance pattern optimization is less effective when execution traces lack sufficient failure signals or performance data, as low-volume or incomplete history reduces the confidence scores needed for reliable recommendations.