Skip to content
cccskills
FreePublished on GitHub

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.

OriginalShow the original description

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

See how to install

Good for

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

Files included (3)

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

SKILL.md (original)

Review the instructions the agent will receive before you install.

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.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.

  • Get oriented in an unfamiliar codebase
  • Prepare documentation for a project handover
  • Compare documented architecture with implementation
github/awesome-copilot40.2KUpdated Oct 17, 2026

Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.

  • Assessing a repository’s readiness for AI agents
  • Prioritizing improvements from assessment results
  • Reviewing scores under an organization’s own policy
github/awesome-copilot40.2KUpdated Oct 17, 2026

Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.

  • Create project instructions for VS Code Copilot
  • Refresh agent rules after project changes
  • Split monorepo instructions by project area
github/awesome-copilot40.2KUpdated Oct 17, 2026

Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.

  • Set shared readiness standards across an organization
  • Focus assessments on AI tooling
  • Adjust the importance of individual checks
github/awesome-copilot40.2KUpdated Oct 17, 2026

Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.

  • Identify ads and keywords to pause
  • Find campaigns and audiences worth scaling
  • Assess whether A/B test results are meaningful
github/awesome-copilot40.2KUpdated Oct 17, 2026

Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.

  • Add beginner-friendly explanations to existing code
  • Document the reasoning behind syntax and design choices
  • Adjust comment depth for readers with different experience
github/awesome-copilot40.2KUpdated Oct 17, 2026

See all skills by github

Report a problem with this skill