MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Optimize-Simplicite-Logs

Optimize-Simplicite-Logs是一款code方向的AI技能,核心价值是capability to parse Simplicité logs from a raw `,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

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

Last verified on: 2026-05-30
mkdir -p ./skills/optimize-simplicite-logs && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/optimize-simplicite-logs/SKILL.md -o ./skills/optimize-simplicite-logs/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# 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):

text
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:

sh
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

powershell
/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

sh
# 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.

🎯 Best For

  • Claude users
  • GitHub Copilot users
  • Software engineers
  • Development teams
  • Tech leads

💡 Use Cases

  • Code quality improvement
  • Best practice enforcement

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Optimize-Simplicite-Logs to Your Work

    Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.

  4. 4

    Review and Refine

    Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.

❓ Frequently Asked Questions

Is Optimize-Simplicite-Logs compatible with Cursor and VS Code?

Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.

Do I need specific dependencies for Optimize-Simplicite-Logs?

Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.

How do I install Optimize-Simplicite-Logs?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/optimize-simplicite-logs/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Skipping validation

Always test AI-generated code changes, even for simple refactors.

Missing dependency updates

Check if the skill requires updated dependencies or new packages.

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