observation-rollup

Automate weekly summarization of observation logs from JSONL files.

2|Updated Apr 20, 2026

Search Tags:#weekly review #log management #Python script #data compression #log summarization

Ready to run

npx skills add https://github.com/ARCASSystems/FounderOS --skill observation-rollup

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: observation-rollup
Source: https://github.com/ARCASSystems/FounderOS/tree/main/skills/observation-rollup
Command: npx skills add https://github.com/ARCASSystems/FounderOS --skill observation-rollup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill compresses observation logs into weekly summaries, helping to manage and review large volumes of data efficiently.

Core Features & Use Cases

Weekly Summarization: Automatically rolls up observation logs into weekly summaries.
Data Retention: Archives completed weeks' data, ensuring recoverability and minimal disk usage.
Idempotent Operation: Safely runs anytime without causing duplicates or deletions.
Use Case: For instance, after a session, you can run this skill to quickly review the summary of observations from the past week without sifting through raw logs.

Quick Start

Run the command "roll up observations" or "compress old logs" to automatically summarize your observation logs.

Frequently Asked Questions about observation-rollup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate weekly log summarization for large observation datasets?▼

Weekly log summarization is automated by processing JSONL files into compact summary files. This compresses observation logs into weekly rollups, enabling efficient review without sifting through raw data.

What is the best way to compress observation logs for weekly review?▼

Compressing observation logs is best handled by rolling up raw JSONL entries into weekly summary files. This archives completed weeks' data to ensure recoverability while minimizing disk usage.

Do I need Python to process JSONL observation logs into summaries?▼

Yes, Python is required to process JSONL observation logs. The summarization relies on executing specific Python scripts to parse raw log data and generate the weekly summary files.

Can I safely run weekly log rollups multiple times without creating duplicates?▼

Yes, the weekly log rollup operation is fully idempotent. You can run the summarization process anytime without risking data duplicates, unintended deletions, or corrupted log archives.

How do I trigger observation log compression without manual script configuration?▼

Trigger log compression by running commands like "roll up observations" or "compress old logs". This executes the underlying Python scripts to summarize and archive your weekly data.