transcribe – Other skill for AI agents

Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings.

OtherSKILL.mdClaude Code · Codex · Hermes · DeepSeek

Install the transcribe skill with one command

Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.

Claude Code

mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/transcribe-e38cb.tgz | tar xz -C ~/.claude/skills

Call it in Claude Code: /transcribe – then describe the task you want done.

Codex

mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/transcribe-e38cb.tgz | tar xz -C ~/.codex/skills

Call it in Codex: $transcribe – then describe the task you want done.

Hermes Agent

mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/transcribe-e38cb.tgz | tar xz -C ~/.hermes/skills

Call it in Hermes Agent: /transcribe – then describe the task you want done.

DeepSeek

mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/transcribe-e38cb.tgz | tar xz -C ~/.deepcode/skills

Call it in DeepSeek: /transcribe – then describe the task you want done.

SKILL.md content

Audio Transcribe

Transcribe audio using OpenAI, with optional speaker diarization when requested. Prefer the bundled CLI for deterministic, repeatable runs.

Workflow

1. Collect inputs: audio file path(s), desired response format (text/json/diarized_json), optional language hint, and any known speaker references.

2. Verify OPENAI_API_KEY is set. If missing, ask the user to set it locally (do not ask them to paste the key).

3. Run the bundled transcribe_diarize.py CLI with sensible defaults (fast text transcription).

4. Validate the output: transcription quality, speaker labels, and segment boundaries; iterate with a single targeted change if needed.

5. Save outputs under output/transcribe/ when working in this repo.

Decision rules

Default to gpt-4o-mini-transcribe with --response-format text for fast transcription.
If the user wants speaker labels or diarization, use --model gpt-4o-transcribe-diarize --response-format diarized_json.
If audio is longer than ~30 seconds, keep --chunking-strategy auto.
Prompting is not supported for gpt-4o-transcribe-diarize.

Output conventions

Use output/transcribe/<job-id>/ for evaluation runs.
Use --out-dir for multiple files to avoid overwriting.

Dependencies (install if missing)

Prefer uv for dependency management.

uv pip install openai

If uv is unavailable:

python3 -m pip install openai

Environment

OPENAI_API_KEY must be set for live API calls.
If the key is missing, instruct the user to create one in the OpenAI platform UI and export it in their shell.
Never ask the user to paste the full key in chat.

Skill path (set once)

export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export TRANSCRIBE_CLI="$CODEX_HOME/skills/transcribe/scripts/transcribe_diarize.py"

User-scoped skills install under $CODEX_HOME/skills (default: ~/.codex/skills).

CLI quick start

Single file (fast text default):

python3 "$TRANSCRIBE_CLI" \
  path/to/audio.wav \
  --out transcript.txt

Diarization with known speakers (up to 4):

python3 "$TRANSCRIBE_CLI" \
  meeting.m4a \
  --model gpt-4o-transcribe-diarize \
  --known-speaker "Alice=refs/alice.wav" \
  --known-speaker "Bob=refs/bob.wav" \
  --response-format diarized_json \
  --out-dir output/transcribe/meeting

Plain text output (explicit):

python3 "$TRANSCRIBE_CLI" \
  interview.mp3 \
  --response-format text \
  --out interview.txt

Reference map

references/api.md: supported formats, limits, response formats, and known-speaker notes.

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