dag-performance-profiler

Name: dag-performance-profiler
Availability: InStock
Author: Curiositech

Profile DAG execution performance across latency, tokens, cost, and resource usage.

10|1|Updated Mar 17, 2026

Search Tags:#performance #optimization #metrics #profiling #observability #dag #cost

Ready to run

npx skills add https://github.com/curiositech/windags-skills --skill dag-performance-profiler

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profiles DAG execution performance across latency, token usage, cost, and resource consumption to help teams identify bottlenecks and optimization opportunities.

Core Features & Use Cases

End-to-end DAG profiling with per-node metrics
Bottleneck detection and actionable optimization recommendations
Cost-aware analysis and parallelization guidance for DAG workloads

Quick Start

Run the profiler on a representative DAG by collecting a trace with dag-execution-tracer and feeding it to the profiler to generate a performance report.

Frequently Asked Questions about dag-performance-profiler

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

FAQPage Schema

How do I profile DAG execution performance to identify bottlenecks?▼

DAG performance profiling requires collecting a trace with dag-execution-tracer and feeding it to the profiler to generate a report. It analyzes latency, token usage, cost, and resource consumption across individual nodes to surface actionable bottlenecks.

What metrics are included in DAG profiling for AI agent pipelines?▼

DAG profiling metrics cover end-to-end latency, token usage, cost computation, and resource consumption. Per-node metrics collection allows granular visibility into AI agent pipeline workflows to pinpoint specific execution bottlenecks.

How do I detect and optimize cost bottlenecks in DAG workflows?▼

Cost bottleneck detection in DAG workflows is achieved by computing per-node costs during profiling and generating optimization recommendations. The profiler provides cost-aware analysis and parallelization guidance to reduce overall DAG execution expenses.

Can I use dag-execution-tracer to collect metrics before profiling?▼

Dag-execution-tracer is required to collect the representative DAG trace before profiling. You run the tracer on your DAG workflow, then feed the collected trace data into the profiler to generate the performance report.

What is the best way to get parallelization guidance for DAG workloads?▼

Parallelization guidance for DAG workloads is generated by analyzing per-node latency and resource usage metrics. The profiler detects sequential bottlenecks and provides actionable optimization recommendations to improve DAG throughput.

Does DAG profiling work without dependencies or external components?▼

DAG profiling operates without external dependencies or components. The profiler independently processes trace data to compute costs, detect bottlenecks, generate optimizations, and present a performance report for your AI agent pipelines.