ce-session-extract

Extract filtered conversation skeletons and error signals from JSONL session logs.

2|Updated Apr 30, 2026

Search Tags:#session #extraction #errors #jsonl #skeleton

Ready to run

npx skills add https://github.com/thisisryanswift/zellij-agent-tools --skill ce-session-extract

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ce-session-extract
Source: https://github.com/thisisryanswift/zellij-agent-tools/tree/main/.opencode/skills/ce-session-extract
Command: npx skills add https://github.com/thisisryanswift/zellij-agent-tools --skill ce-session-extract

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Extract filtered conversation skeletons or error signals from a single session file to minimize the amount of data loaded into context during analysis and review.

Core Features & Use Cases

Skeleton extraction: produce a compact narrative of user/assistant turns and collapsed tool calls.
Error extraction: surface error signals and failed tool calls from session logs.
Use Case: ideal for session-research agents selecting which sessions to deep-dive without loading megabytes of data.

Quick Start

Run ce-session-extract on a session.jsonl file in skeleton or errors mode to generate a compact digest.

Frequently Asked Questions about ce-session-extract

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

FAQPage Schema

How do I extract conversation skeletons from large JSONL session logs?▼

To extract conversation skeletons from large JSONL session logs, you can run a parsing script that filters user and assistant turns while collapsing tool calls into a compact digest. This significantly reduces the context size needed for analysis.

Can I use this to find error signals in Claude Code or Cursor session files?▼

Yes, you can surface error signals in Claude Code or Cursor session files by running an errors mode extraction. This process parses the JSONL data to isolate failed tool calls and error outputs into a succinct digest.

What is the best way to reduce context size when analyzing session logs?▼

The best way to reduce context size when analyzing session logs is to extract a filtered digest of the conversation. By emitting only the narrative skeleton or error signals, you load megabytes less data into your analysis context.

Do I need Python to parse JSONL files for error extraction?▼

Yes, you need Python to parse JSONL files for error extraction because the process relies on Python scripts to read the structured logs and emit the filtered digest with a trailing meta line.

Does session extraction work with Codex JSONL files?▼

Yes, session extraction works with Codex JSONL files. The parsing scripts apply to Claude Code, Codex, and Cursor file formats to generate a compact digest of either conversation skeletons or error signals.