log-analyzer

Analyze production logs to surface trade history, errors, and performance metrics.

18|3|Updated Jan 2, 2026
One-click install
npx skills add https://github.com/Niller2005/PolyFlup --skill log-analyzer-niller2005
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: log-analyzer
Source: https://github.com/Niller2005/PolyFlup/tree/main/.opencode/skill/log-analyzer
Command: npx skills add https://github.com/Niller2005/PolyFlup --skill log-analyzer-niller2005

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Syncing and analyzing production logs to quickly identify trade outcomes, failures, and performance bottlenecks.

Core Features & Use Cases

  • Synchronize production logs from multiple sources into a unified view.
  • Analyze logs/trades_*.log and logs/errors.log to surface outcomes and recurring issues.
  • Correlate window logs (logs/window_*.log) with market events to diagnose performance issues.

Quick Start

Run the log-sync tool to fetch the latest production logs and start a first-pass analysis.

Frequently Asked Questions about log-analyzer

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

FAQPage Schema
How do I sync production logs from multiple sources for debugging?

To sync production logs from multiple sources, run the log-sync tool to fetch the latest files into a unified view. This deterministic synchronization enables targeted searches across trade, error, and window logs.

How does log correlation work for diagnosing production performance issues?

Log correlation works by aligning windowed events from logs/window_*.log with market events to diagnose performance issues. This process surfaces trade outcomes and recurring failures within a specific time window.

Can I analyze trade history and errors from production logs?

Yes, you can analyze trade history and errors by parsing logs/trades_*.log and logs/errors.log. The analysis surfaces trade outcomes, identifies recurring issues, and generates summarized actionable reports.

What's the best way to investigate failures in a post-mortem analysis using log files?

The best way to investigate failures in a post-mortem analysis is synchronizing multiple log sources and correlating windowed events. This surfaces performance bottlenecks and trade failures into a summarized actionable report.

Does this log analysis approach work without external dependencies?

Yes, this log analysis approach works without external dependencies. It supports deterministic log syncing, targeted searches, and correlation of windowed events directly across multiple production log sources.