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.
npx skills add terrylica/cc-skills --skill asciinema-analyzergit clone --depth 1 https://github.com/terrylica/cc-skillsWrote 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.
[](https://agentmods.dev/skills/terrylica/cc-skills/asciinema-analyzer)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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
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.
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.
- 11d ago First seen · 419 lines · 29 tokens per session scan A ec68f91a4ba3
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.
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