optimize-simplicite-logs
Helps your agent process Simplicité logs. It filters out unnecessary information and organizes the data into structured JSON format for analysis.
What it does
This tool organizes Simplicité log files. It transforms a raw text file into a cleaner, structured format (JSON), removing unnecessary information to make analysis easier.
When to use it
- ✓When you have a large Simplicité log file in text format.
- ✓To prevent the AI from having to read a giant raw log file.
- ✓To extract important information like time, error level, and message from a log.
Example of what to ask your AI
“Convert this Simplicité log file into an organized JSON, including only the timestamp, level, and message.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses scripts (Python or PowerShell) to read the log text file. It filters and selects only important data, like time and message, and organizes it into a new JSON file.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add github/awesome-copilot --skill optimize-simplicite-logsOr download just the file:
curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/optimize-simplicite-logs/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
.txtformat. - 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
- Always Convert First: Never directly read
.txtlog files from Simplicité using standard text reading tools. Always convert them to JSON using the available scripts. - Filter Fields: Use
--include(Python) or-Include(PowerShell) to restrict fields to what is absolutely necessary to diagnose the issue (usuallytimestamp,level,body). - 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.
FAQ
Why can't I just give the raw log text file to the AI?
Raw log files can be very large and full of information not useful to the AI, consuming too much 'space' (tokens) and making analysis difficult.
What important information can I extract?
You can extract fields like 'timestamp' (time), 'level' (error/info level), and 'body' (the main log message).
What happens if a log entry spans multiple lines?
The tool can combine these multi-line entries into a single JSON record, ensuring that detailed error information is not lost.
Details
Original description by the author
capability to parse Simplicité logs from a raw `.txt` file, filter fields to reduce noise, and output the result as structured JSON.
repo: github/awesome-copilot
Popularity
- GitHub stars
- 40k
- Ranking position
- #5257
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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