timeline-trace-investigator

Reconstruct incident timelines from distributed traces, logs, deployments, and metrics.

8|1|Updated Jan 24, 2026

Search Tags:#debugging #correlation #incident-investigation #timeline-reconstruction #gap-detection #trace-analysis

Ready to run

npx skills add https://github.com/bordenet/superpowers-plus --skill timeline-trace-investigator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: timeline-trace-investigator
Source: https://github.com/bordenet/superpowers-plus/tree/main/skills/engineering/timeline-trace-investigator
Command: npx skills add https://github.com/bordenet/superpowers-plus --skill timeline-trace-investigator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

When incidents span multiple services, tracing root causes is hard. This skill reconstructs a detailed, chronological timeline from distributed traces, logs, deployments, and metrics, giving engineers a clear view of what happened and when.

Core Features & Use Cases

Multi-source evidence collection: pulls data from tracing systems, deployment history, logs, and metrics.
Gap detection: identifies missing spans, time gaps, and silent services.
Correlation analysis: links deployments, config changes, and metric spikes to errors.
Evidence production: outputs a structured TimelineEvidence object for downstream debugging.

Use cases include post-mortem analysis of cascading failures, debugging latency spikes across microservices, and investigating deployment-related incidents.

Quick Start

Ask the debug conductor to run the timeline-trace investigator on the current incident packet.

Frequently Asked Questions about timeline-trace-investigator

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

FAQPage Schema

How do I reconstruct an incident timeline from distributed traces and logs?▼

Incident timeline reconstruction from distributed traces requires aggregating chronological evidence from logs, deployments, and metrics to identify gaps and correlate events across multiple services for downstream debugging.

What is the best way to debug latency spikes across microservices?▼

Debugging latency spikes across microservices requires trace analysis to identify missing spans, time gaps, and silent services, then correlating those gaps with deployment changes and metric spikes to pinpoint root causes.

How does gap detection work in distributed trace analysis?▼

Gap detection in distributed trace analysis identifies missing spans, time gaps between service calls, and silent services that fail to emit traces, helping engineers find broken links in multi-service incident chains.

Can I correlate deployments and config changes with errors during incident investigation?▼

Correlating deployments and config changes with errors requires linking deployment history and metrics spikes to trace anomalies, enabling structured TimelineEvidence output that maps chronological events to root causes.

Does timeline trace investigation work for post-mortem analysis of cascading failures?▼

Timeline trace investigation supports post-mortem analysis of cascading failures by collecting multi-source evidence from distributed traces, logs, and deployments, then producing structured TimelineEvidence with events, gaps, and correlations.

What do I need to provide for multi-service incident timeline reconstruction?▼

Multi-service incident timeline reconstruction requires distributed traces, deployment history, logs, and metrics as input, which the analysis engine processes to generate structured TimelineEvidence with chronological events and gap detections.