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optimize-simplicite-logs

Parse Simplicité log files into filtered structured JSON output.

38.5k|4.9k|Updated Jun 11, 2025
Search Tags:#Python#Troubleshooting#Json#log parsing#powerShell#Log Analysis#simplicite
One-click install
npx skills add https://github.com/github/awesome-copilot --skill optimize-simplicite-logs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: optimize-simplicite-logs
Source: https://github.com/github/awesome-copilot/tree/main/skills/optimize-simplicite-logs
Command: npx skills add https://github.com/github/awesome-copilot --skill optimize-simplicite-logs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Raw Simplicité log files are verbose, multi-line, and too large to load into an AI context window for troubleshooting. This Skill converts them into compact, structured JSON containing only the fields you need, reducing token usage by roughly 56%.

Core Features & Use Cases

  • Log Parsing: Converts raw Simplicité .txt logs into structured JSON with fields like timestamp, level, and body.
  • Field Filtering: Include or exclude specific fields (e.g. keep only timestamp,level,body) to discard noise such as app, endpoint, or contextPath.
  • Multi-line Support: Captures stack traces and multi-line error messages inside the body field that simple text searches would miss.
  • Use Case: A user shares a 10MB Simplicité error log. Run the Python script to produce a minified JSON with only the essential fields, then read that JSON to diagnose the issue without exhausting the context window.

Quick Start

Ask the AI to convert your Simplicité log file to JSON using the included Python script with only the timestamp, level, and body fields, then analyze the resulting output file.

Frequently Asked Questions about optimize-simplicite-logs

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

FAQPage Schema
How do I convert Simplicité logs to JSON?▼

Run the bundled Python script with the input .txt file path, optionally specifying --include fields and an --output file. For example: python simplicite-log2json.py logs.txt --include timestamp,level,body --output logs.json.

How do I reduce Simplicité log size for AI analysis?▼

Use the --include flag to keep only essential fields like timestamp, level, and body, discarding noisy fields such as app, endpoint, and contextPath. This reduces token consumption by roughly 56% while preserving diagnostic content.

Is there a PowerShell version of the Simplicité log parser?▼

Yes, a PowerShell script SimpliciteLog2Json.ps1 is included alongside the Python version. It accepts -InputPath, -Output, -Include, and -Exclude parameters with the same field filtering behavior.

Can the parser handle multi-line stack traces in logs?▼

Yes, the parser groups continuation lines under the current log entry until a new timestamp line appears. Multi-line stack traces and errors are preserved inside the body field of the JSON output.

What fields can I filter in Simplicité log output?▼

The available fields are timestamp, app, level, endpoint, contextPath, event, user, class, function, rowId, and body. You can pass them as comma-separated values to --include or --exclude, but not both at once.

Why does Simplicité log parsing fail or skip entries?▼

Parsing depends on a fixed regex matching the standard Simplicité log format. Heavily customized log formats may fail to match, and malformed entries are counted as skipped in the summary output.

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