asciinema-analyzer

asciinema-analyzer is a skill for Claude Code from terrylica/cc-skills. It costs 29 tokens per session (3,381 once invoked), scanned A, original, MIT.

A tool for finding meaning in asciinema recordings, which are saved terminal-session videos that can also be searched as text. It analyzes converted text files to find keywords, patterns, topics, commands, and errors.

In plain words
What is it for?
Use it to search session history, extract themes from recordings, and locate commands or errors for documentation or review.
Why use it?
It helps you review long terminal sessions without reading every line. It can find known terms directly and optionally discover unexpected terms or topics.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the AskUserQuestion tool; positional $N argument; mentions Claude Code.

Part of the asciinema-tools plugin — 24 skills shipped together , and of cc-skills

Good fit Use it to search session history, extract themes from recordings, and locate commands or errors for documentation or review.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/terrylica/cc-skills/asciinema-analyzer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add terrylica/cc-skills --skill asciinema-analyzer
Clone the repo
git clone --depth 1 https://github.com/terrylica/cc-skills

Made for: Claude Code.

Or install asciinema-tools, the plugin that ships this one along with the rest of its 24 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for asciinema-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/terrylica/cc-skills/asciinema-analyzer/github.svg)](https://agentmods.dev/skills/terrylica/cc-skills/asciinema-analyzer)
Your own site
<a href="https://agentmods.dev/skills/terrylica/cc-skills/asciinema-analyzer"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/asciinema-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for asciinema-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/skills/terrylica/cc-skills/asciinema-analyzer"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/asciinema-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,381 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.03381
Opus 5 $0.00015 $0.01690
Sonnet 5 $0.00006 $0.00676
Haiku 4.5 $0.00003 $0.00338

Measured 11d ago against content hash ec68f91a4ba3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

asciinema-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/asciinema-tools/skills/asciinema-analyzer/SKILL.md · 419 lines

How it starts

The opening of the file, as written. The whole thing — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.

asciinema-analyzer

Semantic analysis of converted .txt recordings for Claude Code consumption. Uses tiered analysis: ripgrep (primary, 50-200ms) -> YAKE (secondary, 1-5s) -> TF-IDF (optional).

Platform: macOS, Linux (requires ripgrep, optional YAKE)

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

When to Use This Skill

Use this skill when:

  • Searching for keywords or patterns in converted recordings
  • Extracting topics or themes from session transcripts
  • Finding specific commands or errors in session history
  • Auto-discovering unexpected terms in recordings
  • Analyzing session content for documentation or review

Analysis Tiers

Tier Tool Speed (4MB) When to Use
1 ripgrep 50-200ms Always start here (curated)
2 YAKE 1-5s Auto-discover unexpected terms
3 TF-IDF 5-30s Topic modeling (optional)

Decision: Start with Tier 1 (ripgrep + curated keywords). Only use Tier 2 (YAKE) when auto-discovery is explicitly requested.


Requirements

Component Required Installation Notes
ripgrep Yes brew install ripgrep Primary search tool
YAKE Optional uv run --with yake For auto-discovery tier

Workflow Phases (ALL MANDATORY)

IMPORTANT: All phases are MANDATORY. Do NOT skip any phase. AskUserQuestion MUST be used at each decision point.

Phase 0: Preflight Check

Purpose: Verify input file exists and check for .txt (converted) format.

/usr/bin/env bash << 'PREFLIGHT_EOF'
INPUT_FILE="${1:-}"

if [[ -z "$INPUT_FILE" ]]; then
  echo "NO_FILE_PROVIDED"
elif [[ ! -f "$INPUT_FILE" ]]; then
  echo "FILE_NOT_FOUND: $INPUT_FILE"
elif [[ "$INPUT_FILE" == *.cast ]]; then
  echo "WRONG_FORMAT: Convert to .txt first with /asciinema-tools:convert"
elif [[ "$INPUT_FILE" == *.txt ]]; then
  SIZE=$(ls -lh "$INPUT_FILE" | awk '{print $5}')
  LINES=$(wc -l < "$INPUT_FILE" | tr -d ' ')
  echo "READY: $INPUT_FILE ($SIZE, $LINES lines)"
else
  echo "UNKNOWN_FORMAT: Expected .txt file"
fi
PREFLIGHT_EOF

Read the full file on GitHub · 419 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 419 lines · 29 tokens per session scan A ec68f91a4ba3

Subscribe to this mod's changes

asciinema-analyzer is a skill published in the GitHub repository terrylica/cc-skills (72 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 3,381 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

android-development

Android development with Kotlin, Jetpack Compose, and modern Android architecture. Use when building Android apps, implementing Material Design, or following Android best practices.

travisjneuman/.claude · 33 tokens

email-systems

Transactional email (Resend, SendGrid, SES), templates (React Email, MJML), deliverability (SPF/DKIM/DMARC), and inboxing best practices. Use when building email infrastructure, designing templates, or troubleshooting deliverability.

travisjneuman/.claude · 55 tokens

customer-persona-builder

Data-driven customer persona development combining market research, user behavior analysis, and segmentation frameworks. Use when creating buyer personas, ideal customer profiles (ICPs), or user archetypes.

travisjneuman/.claude · 40 tokens

devops-cloud

DevOps, cloud infrastructure, and platform engineering. Use when working with AWS, GCP, Azure, Kubernetes, Terraform, CI/CD pipelines, or infrastructure as code.

travisjneuman/.claude · 38 tokens

generic-design-system

Complete design system reference for any project - colors, typography, spacing, components, animations. Adapts to project theme and tech stack. Use when implementing UI, choosing colors, creating animations, or ensuring brand consistency. For new design systems, use ui-research skill first.

travisjneuman/.claude · 60 tokens

generic-code-reviewer

Review code for bugs, security vulnerabilities, performance issues, accessibility gaps, and CLAUDE.md workflow compliance. Supports any tech stack - HTML/CSS/JS, React, TypeScript, Node.js, Python, NestJS, Next.js, and more. Use when completing features, before commits, or reviewing pull requests.

travisjneuman/.claude · 70 tokens