Skill

transcribe-gemini-raw

Transcribe an audio file to a raw verbatim transcript using the gemini-transcription MCP server. Preserves every word including fillers. Use when the user asks for a raw transcript, verbatim transcription, or unedited text via Gemini.

npx claudepluginhub danielrosehill/claude-code-plugins --plugin claude-transcription

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Produce a word-for-word transcript including fillers (ums, ahs, repetitions).

SKILL.md

Similar Skills

cache-components

139.3k

Guides Next.js Cache Components and Partial Prerendering (PPR): 'use cache' directives, cacheLife(), cacheTag(), revalidateTag() for caching, invalidation, static/dynamic optimization. Auto-activates on cacheComponents: true.

cache-components

mcp-builder

124.2k

Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).

9 files

anthropics-skills-13

Stats

Stars0

Forks0

Last CommitApr 19, 2026

Actions

View Source View Plugin View on GitHub View README

Help us improve

Share bugs, ideas, or general feedback.

Transcribe (Gemini, raw verbatim)

Produce a word-for-word transcript including fillers (ums, ahs, repetitions).

How

Call the gemini-transcription MCP tool transcribe_audio_raw (or transcribe_audio with style: "raw" / preset: "verbatim"). Check available presets first with list_transcription_presets.

System prompt (if tool accepts custom)

Transcribe this audio verbatim. Include every word the speaker says, including filler words (um, uh, like), false starts, and repetitions. Do not paraphrase. Do not clean up grammar. Output plain text only, no speaker labels unless multiple speakers are clearly present.

Output convention

Save to <source_stem>.raw.txt in the source directory (or user-specified path).

Errors

If the MCP server is not available, tell the user to install the gemini-transcription MCP and point them at the server's docs.

If the file is too large for a single Gemini request, suggest chunking via the normalize-format skill first or splitting with ffmpeg.