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debug-pipeline

Debug and inspect Tower app runs using local logs and status.

19|Updated Apr 1, 2026
Search Tags:#debugging#logs#python#pipelines#asgi#tower
One-click install
npx skills add https://github.com/tower/agentic-data-engineering --skill debug-pipeline-tower
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debug-pipeline
Source: https://github.com/tower/agentic-data-engineering/tree/main/.claude/skills/debug-pipeline
Command: npx skills add https://github.com/tower/agentic-data-engineering --skill debug-pipeline-tower

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Debug and inspect a Tower app after running it, to quickly diagnose errors, inspect logs, and fix issues across pipelines, ASGI apps, and plain Python scripts.

Core Features & Use Cases

  • Unified post-run debugging for dlt pipelines, ASGI apps, and Python scripts.
  • Guided troubleshooting steps, log inspection, and status checks to identify root causes.
  • Use case: after a run, inspect logs to locate a failing step and propose fixes, then re-run locally.

Quick Start

Run the debug-pipeline skill on a failed or successful run to start diagnosing immediately.

Frequently Asked Questions about debug-pipeline

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

FAQPage Schema
How do I debug a failed dlt pipeline after a run completes?▼

To debug a failed dlt pipeline, you inspect post-run logs and status checks to locate the failing step. This process identifies the root cause and proposes fixes for a local re-run.

What is the best way to inspect ASGI app logs for troubleshooting?▼

Inspecting ASGI app logs through unified post-run debugging provides guided troubleshooting steps and status checks to quickly diagnose errors and resolve issues.

Can I diagnose errors in plain Python scripts locally after execution?▼

Yes, post-run debugging supports plain Python scripts, allowing you to inspect logs, check status, and resolve issues after execution in a local environment.

Do I need a local Tower environment to troubleshoot pipeline runs?▼

Yes, troubleshooting pipeline runs requires a local Tower environment. The debugging process relies on tower_run_local along with log and status access tools to diagnose and resolve issues.

How do I find the root cause of a failing step in my Python pipeline?▼

To find the root cause of a failing step, run post-run diagnostics to inspect logs and check application status. This guided troubleshooting locates the failure and helps you propose fixes.

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