Borrowing it
Nothing to install: this file belongs to 2SSK/dot-files. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/2SSK/dot-files/main/.claude/commands/transcript.mdgit clone --depth 1 https://github.com/2SSK/dot-filesWrote 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/commands/2ssk/dot-files/transcript)<a href="https://agentmods.dev/commands/2ssk/dot-files/transcript"><img src="https://agentmods.dev/badge/commands/2ssk/dot-files/transcript/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/commands/2ssk/dot-files/transcript"><img src="https://agentmods.dev/badge/commands/2ssk/dot-files/transcript.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00005 | $0.00070 |
| Opus 5.5 | $0.00002 | $0.00028 |
| Sonnet 5.5 | $0.00001 | $0.00014 |
| Haiku 4.5 | $0.00001 | $0.00007 |
Grade A, and why
transcript 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 14d 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.
What it actually says
Download the transcript for the YouTube video at $ARGUMENTS using yt-dlp:
yt-dlp --write-subs --write-auto-subs --skip-download --convert-subs srt "$ARGUMENTS"
Then convert the .srt file to plain text and display the transcript content.
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.
- 14d ago First seen · 9 lines · 5 tokens per session scan A 98b9ac5f4f82
transcript is a command published in the GitHub repository 2SSK/dot-files (250 stars, last pushed yesterday), licensed MIT. It adds 5 tokens to every session and 70 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-10-02.
Other commands, from other repositories
tokenless-stats
Show Tokenless compression statistics.
run
A command that starts a planned, isolated Windows computer-use task. It follows prepared steps, checks the computer setup, uses screenshots when needed, and reports what was actually verified.
port-loop
Iterate research → implement → verify autonomously until a capability works or the model of it is exhausted (linux-phone-porting).
test
A command for running general capability tests for computer-use features, which let an agent interact with supported computer applications. It uses a quick test by default and does not operate real user applications.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.