transcript-critic

A Claude Code Skill that transcribes audio files or YouTube videos using whisper.cpp and then generates a structured, critical analysis of the transcript -- all from a single slash command.

Run /transcribe followed by a .vtt file, an audio file, or a URL (including YouTube and other yt-dlp supported sites) and Claude will handle the rest: downloading, converting, transcribing, and producing a detailed markdown summary.

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Prerequisites | Installation | Usage | How it works | Transcript summary prompt | Personal Project Disclosure

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Disclaimer

This software was developed with the assistance of AI (Anthropic Claude). It is provided "as is", without warranty of any kind, express or implied. Use at your own risk. The author assumes no liability for any damages arising from its use.

Prerequisites

Tool	Purpose
whisper.cpp	Local speech-to-text transcription engine
ffmpeg	Audio format conversion
yt-dlp	Downloading audio from YouTube and other sites
Claude Code	CLI for Claude that supports custom skills

Installation

1. Install the skill

Copy SKILL.md into Claude Code's global skills directory:

mkdir -p ~/.claude/skills/transcribe
cp SKILL.md ~/.claude/skills/transcribe/SKILL.md

2. Configure script paths

In transcribe.sh, update the following variables at the top of the script to match your local whisper.cpp installation:

WHISPER_ROOT="${HOME}/github.com/ggerganov/whisper.cpp"
PGM="${WHISPER_ROOT}/build/bin/whisper-cli"
MODEL="${WHISPER_ROOT}/models/ggml-medium.en.bin"

• WHISPER_ROOT must point to your whisper.cpp checkout.
• PGM must point to the compiled whisper-cli binary.
• MODEL must point to the GGML model file you want to use (e.g., ggml-medium.en.bin).

Note

In SKILL.md, the paths to transcribe.sh and ANALYSIS_PROMPT.md are referenced as instructions for Claude. If you clone this repository to a location other than ~/github.com/jftuga/transcript-critic/, update those paths in SKILL.md accordingly.

Usage

Once installed, use the /transcribe slash command inside Claude Code:

/transcribe recording.m4a
/transcribe interview.mp3
/transcribe podcast.vtt
/transcribe https://www.youtube.com/watch?v=VIDEO_ID

Claude will:

1. Detect the input type (.vtt file, audio file, or URL).
2. Convert or download the audio as needed using ffmpeg and yt-dlp.
3. Transcribe the audio to .txt and .vtt format via whisper.cpp.
4. Analyze the transcript using the prompt template and write a structured .md summary.

How it works

The skill is composed of two parts:

• SKILL.md -- The skill definition file. It tells Claude how to handle each input type, to invoke transcribe.sh, and how to produce the final analysis. Claude Code reads this file when the /transcribe command is invoked.

• transcribe.sh -- Accepts either a local audio/video file or a URL. Local, non-audio files are converted to MP3 via ffmpeg; URLs are downloaded and extracted as MP3 via yt-dlp. In both cases, whisper-cli is run to produce .txt and .vtt transcription files. Because yt-dlp is used, audio can be downloaded and transcribed from many websites -- not just YouTube.

Transcript summary prompt

The file ANALYSIS_PROMPT.md is the prompt template that drives the analysis step. It instructs Claude to produce a structured markdown document with the following sections:

Section	Description
Overview	A concise thesis summary.
Key Terms and Concepts	Definitions anchored to first-mention timestamps.
Detailed Summary	Section-by-section breakdown with timestamp ranges.
Scripture References	Included only when the content is theological.
Evidentiary Notes	Categorizes each claim by its type of support (anecdotal, appeal to authority, logical argument, or cited source).
Logical Fallacies	Identifies reasoning errors using standard fallacy types.
Questions and Underdeveloped Areas	Flags ambiguities and gaps.

This prompt is effective because it enforces objectivity because Claude is explicitly told to analyze based solely on the transcript content without injecting prior knowledge about the speaker or topic. It requires timestamp citations throughout, grounding every observation in a specific moment. The evidentiary notes and logical fallacy sections push the analysis beyond simple summarization into critical evaluation, categorizing claims by the strength of their supporting evidence and surfacing reasoning errors. The result is an analysis that is structured, verifiable, and avoids the uncritical paraphrasing that plagues most AI-generated summaries.

Personal Project Disclosure

This program is my own original idea, conceived and developed entirely:

• On my own personal time, outside of work hours
• For my own personal benefit and use
• On my personally owned equipment
• Without using any employer resources, proprietary information, or trade secrets
• Without any connection to my employer's business, products, or services
• Independent of any duties or responsibilities of my employment

This project does not relate to my employer's actual or demonstrably anticipated research, development, or business activities. No confidential or proprietary information from any employer was used in its creation.