From Napkin
Parses Simplicité log files from .txt to structured JSON, filtering fields to reduce noise and save ~56% tokens. Use when analyzing raw Simplicité logs for troubleshooting.
How this skill is triggered — by the user, by Claude, or both
Slash command
/napkin:optimize-simplicite-logsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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.
Use this skill when you need to:
.txt format.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.
/scripts/SimpliciteLog2Json.ps1) or the Python script (/scripts/simplicite-log2json.py).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).
Properly captures stack traces and multiline errors inside the body field of the JSON structure, which a simple text search might miss.
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.
After processing, the tool prints a summary to stderr (or console):
Processed: 123 entries, Skipped: 2 entries
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>
/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.
.txt log files from Simplicité using standard text reading tools. Always convert them to JSON using the available scripts.--include (Python) or -Include (PowerShell) to restrict fields to what is absolutely necessary to diagnose the issue (usually timestamp,level,body).timestamp, app, level, endpoint, contextPath, event, user, class, function, rowId, body.# 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.
npx claudepluginhub ani1797/forge --plugin copilot-sdk2plugins reuse this skill
First indexed Jun 6, 2026
Analyzes large JSONL log files via schema-aware partitioned analysis. Discovers field schema, generates jq extraction recipes, and orchestrates parallel chunk analysts with synthesis. Use for log analytics or incident investigation on logs exceeding context limits.
Analyzes JSONL and other log files using rg and jq: structured extraction, cross-log correlation, timeline reconstruction, pattern search.
Anonymize and sanitize customer-provided log files before they are committed as pipeline test fixtures or sample events. Performs a line-by-line review and replaces all sensitive values inline, preserving log structure and format exactly — never reformats, re-indents, or restructures content. Invoke manually with /anonymize-logs.