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pipeline-investigate

Aggregate timelines, Docker logs, and decision events to diagnose pipeline run failures.

Updated Sep 22, 2025
Search Tags:#timeline#investigation#logs#docker#pipeline#decision-events
One-click install
npx skills add https://github.com/tinkermonkey/switchyard --skill pipeline-investigate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pipeline-investigate
Source: https://github.com/tinkermonkey/switchyard/tree/main/.claude/skills/pipeline-investigate
Command: npx skills add https://github.com/tinkermonkey/switchyard --skill pipeline-investigate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Investigate a pipeline run to quickly identify bottlenecks, failures, and root causes by collecting and correlating timeline data, Docker logs, and decision events.

Core Features & Use Cases

  • Timeline assembly: correlate events across stages to form a coherent run narrative.
  • Logs and decisions: fetch Docker logs and decision-event history to diagnose issues and verify progress.
  • Use Case: When a pipeline run stalls or fails, automatically gather metadata, timelines, and logs into a consolidated root-cause report for engineering teams.

Quick Start

Invoke the pipeline-investigate workflow with a valid pipeline_run_id to generate a timeline and root-cause report.

Frequently Asked Questions about pipeline-investigate

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

FAQPage Schema
How do I investigate a stalled Docker pipeline run and find the root cause?▼

To investigate a stalled Docker pipeline run, you can aggregate timeline data, Docker logs, and decision events to correlate failures across stages and generate a consolidated root-cause report.

What is the best way to correlate decision events and logs for pipeline post-mortem analysis?▼

Correlating decision events and logs for pipeline post-mortem analysis involves fetching decision-event history and Docker logs, then assembling them into an end-to-end timeline to verify progress and diagnose issues.

Do I need an Elasticsearch cluster to diagnose orchestrated pipeline runs?▼

Yes, diagnosing orchestrated pipeline runs requires access to an Elasticsearch cluster with indices like pipeline-runs-*, decision-events-*, and agent-events-* to aggregate the necessary timeline and event data.

Can I use pipeline timeline assembly to debug failures across multiple orchestrated stages?▼

Yes, timeline assembly correlates events across multiple orchestrated stages to form a coherent run narrative, allowing you to quickly identify bottlenecks and debug failures in Claude Code pipelines.

What tools are used to fetch Docker logs and decision events for pipeline investigation?▼

Pipeline investigation uses curl, docker logs, and a Python timeline script to fetch metadata, logs, and decision events from a locally running orchestrator stack to produce the investigation results.

Why does my pipeline run fail to generate a root-cause report after a stall?▼

A pipeline root-cause report may fail if the locally running orchestrator stack is unavailable or if the Elasticsearch cluster lacks the required pipeline-runs-* and decision-events-* indices to aggregate timeline data.

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