Why should I convert .cast files to .txt for Claude Code?

.cast files contain heavy JSON structures and ANSI escape codes that confuse AI models; converting to .txt provides a clean, searchable format that fits easily within context limits.

Can I preserve timing information in the converted text?

Yes, the skill offers an optional 'Create timestamp index' phase that generates HH:MM:SS markers for easier navigation within the text file.

What are the system requirements for this skill?

You need a macOS or Linux environment with the asciinema CLI (v2.4 or higher) installed and available in your system path.

Does this skill support batch processing?

Yes, it includes a dedicated batch mode activated via the --batch flag, perfect for converting entire directories of iTerm2 auto-logged recordings.

How much space can I save with this conversion?

The converter typically achieves a 950:1 compression ratio, potentially turning a 3.8GB session recording into a highly manageable 4MB text file.

Asciinema Converter

Name: Asciinema Converter
Author: terrylica

byterrylica

•

Analytics & Monitoring

Converts terminal session recordings from .cast to compressed, clean .txt files optimized for AI analysis.

About

Asciinema Converter is a high-performance utility designed to bridge the gap between complex terminal recordings and AI-driven analysis. By stripping nested JSON structures and ANSI escape codes from .cast files, it achieves up to 950:1 compression, transforming gigabytes of log data into lightweight text files that Claude Code can efficiently read, grep, and analyze. The skill features a mandatory multi-phase workflow that guides users through file discovery, output configuration, and batch processing, making it an essential tool for developers who need to audit long sessions or manage massive iTerm2 auto-log directories.

Key Features

Batch conversion mode for processing thousands of recordings simultaneously
High-ratio compression (950:1) for efficient AI context window usage
Optional timestamp indexing (HH:MM:SS) for navigable session logs
ANSI code and JSON metadata stripping for clean, plain-text output
8 GitHub stars
Interactive workflow with preflight checks and output organization

Use Cases

Migrating iTerm2 auto-logs into searchable, text-based historical archives
Preparing long terminal sessions for AI-assisted debugging or code review
Generating clean documentation from live command-line demonstrations

About

Key Features

Batch conversion mode for processing thousands of recordings simultaneously
High-ratio compression (950:1) for efficient AI context window usage
Optional timestamp indexing (HH:MM:SS) for navigable session logs
ANSI code and JSON metadata stripping for clean, plain-text output
8 GitHub stars
Interactive workflow with preflight checks and output organization

Use Cases

Migrating iTerm2 auto-logs into searchable, text-based historical archives
Preparing long terminal sessions for AI-assisted debugging or code review
Generating clean documentation from live command-line demonstrations