dag-ops

What problem does it solve?

Provides operations, debugging, and optimization for DAG workflows by performing root-cause analysis on failures, profiling execution performance, aggregating results from parallel branches, bridging context between nodes, and producing remediation or escalation plans so agents can recover work and avoid repeated failures.

Core Features & Use Cases

• Root Cause Analysis: Trace failures backward through dependency graphs to find the first deviation and quantify confidence for remediation decisions.
• Auto-Remediation & Escalation: Apply retries with exponential backoff, model substitution, or auto-fixes when confidence thresholds are met, and prepare structured escalation packages when they are not.
• Result Aggregation: Deduplicate, synthesize, or concatenate parallel branch outputs with similarity analysis and conflict detection before merging.
• Performance Profiling: Identify critical-path bottlenecks, recommend parallelization or model downgrades, and capture cost/performance metrics for feedback loops.
• Context Bridging & Pattern Learning: Maintain context registries across node waves and extract execution patterns to reduce future failures.
• Use Case: Recover a cascade failure where upstream API timeouts corrupted downstream analysis, automatically retrying and re-executing affected nodes when confidence permits or escalating with a structured diagnosis otherwise.

Quick Start

Use the dag-ops skill to analyze a failed DAG run, identify the root cause with confidence scoring, and produce recommended remediation steps or an escalation package.