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optimize-simplicite-logs

Convert Simplicité text logs into structured JSON while keeping only the fields needed for troubleshooting. Preserve multiline errors and stack traces, and reduce the amount of log content passed to an AI agent for analysis.

中文简介正在准备中。当前显示英语简介。

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capability to parse Simplicité logs from a raw `.txt` file, filter fields to reduce noise, and output the result as structured JSON.

查看安装方法

适用场景

  • Preparing large logs for AI analysis
  • Extracting timestamps, severity levels, and messages
  • Preserving multiline errors during conversion
  • Piping converted JSON into other tools

包含的文件(3)

  • SKILL.md3.7 KB
  • scripts/simplicite-log2json.py4.4 KB
  • scripts/SimpliciteLog2Json.ps14.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Optimize Simplicite Logs

This skill provides the capability to parse Simplicité logs from a raw .txt file, filter fields to reduce noise, and output the result as structured JSON. This is critical for optimizing AI context size (saving ~56% of tokens) and providing structured, predictable data for troubleshooting.

When to Use This Skill

Use this skill when you need to:

  • Analyze user-provided Simplicité log files in .txt format.
  • Avoid ingesting massive raw log files into your context window.
  • Extract structured fields (like timestamp, level, body) from verbose multi-line log output.

IMPORTANT: Instead of directly reading a raw .txt log file provided by the user using file read tools, you must use one of the log converter scripts (PowerShell or Python) to parse the file into a JSON format first, optionally extracting only the fields needed.

Prerequisites

  • Access to either the PowerShell script (/scripts/SimpliciteLog2Json.ps1) or the Python script (/scripts/simplicite-log2json.py).

Core Capabilities

1. Context Optimization

Reduces the tokens consumed by large Simplicité logs by extracting only relevant log fields (e.g. body, timestamp, level) and discarding non-relevant structural log data (like app, endpoint, contextPath).

2. Multi-line Support

Properly captures stack traces and multiline errors inside the body field of the JSON structure, which a simple text search might miss.

3. Stdout Support

If no output path is provided for the JSON file (e.g. omitting --output or -Output), the parsed JSON will be printed directly to stdout, allowing you to pipe the output to other tools.

Output Summary

After processing, the tool prints a summary to stderr (or console):

Processed: 123 entries, Skipped: 2 entries

Usage Examples

Example 1: Python Version (Recommended)

Convert a log file to JSON, keeping only the most important fields:

python /absolute/path/to/skills/optimize-simplicite-logs/scripts/simplicite-log2json.py <input.txt> --include timestamp,level,body --output <output.json>

Example 2: PowerShell Version

/python /absolute/path/to/skills/optimize-simplicite-logs/scripts/SimpliciteLog2Json.ps1 -InputPath "<input.txt>" -Output "<output.json>" -Include "body,timestamp,level"

After generating the <output.json>, you can safely read the resulting file to perform your analysis.

Guidelines

  1. Always Convert First: Never directly read .txt log files from Simplicité using standard text reading tools. Always convert them to JSON using the available scripts.
  2. Filter Fields: Use --include (Python) or -Include (PowerShell) to restrict fields to what is absolutely necessary to diagnose the issue (usually timestamp,level,body).
  3. Available Fields: The fields you can filter include: timestamp, app, level, endpoint, contextPath, event, user, class, function, rowId, body.

Common Patterns

Pattern: Fast Contextual Troubleshooting

# 1. Run the script to generate a minified JSON output in the current directory
python /absolute/path/to/skills/optimize-simplicite-logs/scripts/simplicite-log2json.py logs.txt --include timestamp,level,body --output logs_minified.json

# 2. Then read logs_minified.json to understand the context.

Limitations

  • The parser depends on a fixed regex pattern that matches the standard Simplicité log output. If the log format has been heavily customized, parsing might fail or degrade.

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