General
optimize-simplicite-logs - Claude MCP Skill
capability to parse Simplicité logs from a raw `.txt` file, filter fields to reduce noise, and output the result as structured JSON.
SEO Guide: Enhance your AI agent with the optimize-simplicite-logs tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to capability to parse simplicité logs from a raw `.txt` file, filter fields to reduce noise, and outpu... Download and configure this skill to unlock new capabilities for your AI workflow.
Documentation
SKILL.md# 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: ```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.
Signals
Information
- Repository
- github/awesome-copilot
- Author
- github
- Last Sync
- 9/5/2026
- Repo Updated
- 9/5/2026
- Created
- 5/18/2026
Reviews (0)
No reviews yet. Be the first to review this skill!
Related Skills
cursorrules
CrewAI Development Rules
README
Agents — Working Implementations
firecrawl-build-search
Integrate Firecrawl `/search` into product code and agent workflows. Use when an app needs discovery before extraction, when the feature starts with a query instead of a URL, or when the system should search the web and optionally hydrate result content.
firecrawl-build-onboarding
Get Firecrawl credentials and SDK setup into a project. Use when an application needs `FIRECRAWL_API_KEY`, when an agent should add Firecrawl to `.env`, when the user wants to authenticate Firecrawl for app code, or when choosing the first SDK and docs for a new Firecrawl integration. This skill includes its own browser auth flow, so it does not depend on the website onboarding skill.
Related Guides
Mastering the Oracle CLI: A Complete Guide to the Claude Skill for Database Professionals
Learn how to use the oracle Claude skill. Complete guide with installation instructions and examples.
Python Django Best Practices: A Comprehensive Guide to the Claude Skill
Learn how to use the python django best practices Claude skill. Complete guide with installation instructions and examples.
Mastering Python and TypeScript Development with the Claude Skill Guide
Learn how to use the python typescript guide Claude skill. Complete guide with installation instructions and examples.