What are the system requirements?

The skill requires macOS or Linux, with ripgrep installed. YAKE analysis requires the 'uv' package manager to run the Python-based extraction.

What does density analysis show?

Density analysis identifies sections of the log with the highest concentration of specific keywords, helping you find the most active parts of a recording quickly.

Does this skill work directly with .cast files?

No, it requires terminal recordings to be converted to .txt first. You can use the companion /asciinema-tools:convert command to prepare your files for analysis.

Can I search for specific financial or ML terms?

Yes, the skill includes pre-defined domain sets for Trading (Sharpe, PnL, CAGR) and ML/AI (Epoch, Loss, SOTA) to speed up information retrieval.

What is the benefit of the YAKE integration?

YAKE allows for unsupervised keyword discovery, meaning the skill can find unexpected terms and patterns in your logs that aren't part of the curated domain lists.

Asciinema Analyzer

Name: Asciinema Analyzer
Author: terrylica

byterrylica

•

分析と監視

Performs deep semantic and density analysis on terminal recordings to extract actionable insights and patterns.

Asciinema Analyzer is a specialized skill designed to process converted terminal recordings for Claude Code. It utilizes a three-tiered analysis approach—leveraging ripgrep for sub-second keyword searches, YAKE for unsupervised keyword discovery, and density mapping to identify peak activity windows. Whether auditing quantitative backtests, monitoring ML training epochs, or reviewing complex development workflows, this skill provides a structured, interactive way to navigate and summarize large terminal logs through domain-specific keyword sets and automated extraction techniques.

主な機能

01Tiered analysis architecture using ripgrep, YAKE, and TF-IDF

02Interactive multi-phase workflow with mandatory preflight checks

03Domain-specific keyword sets for Trading, ML/AI, and DevOps

04Flexible reporting options including Summary, JSON, and Markdown

058 GitHub stars

06Activity density analysis to locate high-concentration log sections

ユースケース

01Extracting performance metrics and drawdowns from quantitative trading logs

02Identifying anomalies or specific training milestones in long ML session recordings

03Auditing CLI-based development workflows to generate documentation or bug reports

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add terrylica/cc-skills asciinema-analyzer

For use in Claude.ai and ChatGPT

Download Skill