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 humancto/indic-voice-pipeline --skill whisper-transcribegit clone --depth 1 https://github.com/humancto/indic-voice-pipelineWrote 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/humancto/indic-voice-pipeline/whisper-transcribe)<a href="https://agentmods.dev/skills/humancto/indic-voice-pipeline/whisper-transcribe"><img src="https://agentmods.dev/badge/skills/humancto/indic-voice-pipeline/whisper-transcribe/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/humancto/indic-voice-pipeline/whisper-transcribe"><img src="https://agentmods.dev/badge/skills/humancto/indic-voice-pipeline/whisper-transcribe.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 | $0.00125 | $0.02086 |
| Opus 5.5 | $0.00050 | $0.00834 |
| Sonnet 5.5 | $0.00025 | $0.00417 |
| Haiku 4.5 | $0.00013 | $0.00209 |
Grade A, and why
whisper-transcribe 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 2mo 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Whisper Transcribe
Transcribe and translate audio/video files locally using OpenAI Whisper. Supports 99 languages, runs entirely on your machine.
Prerequisites
Run once to install dependencies:
pip install openai-whisper --quiet
pip install transformers accelerate --quiet # For HuggingFace fine-tuned models
ffmpeg is required for audio processing:
brew install ffmpeg # macOS
Step-by-Step Workflow
For ANY transcription/translation request, follow these steps:
Step 1: Check dependencies
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/check_deps.py
Step 2: Determine intent and run the appropriate command
User wants to transcribe audio/video to text:
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "<FILE_PATH>" --output-dir ~/Downloads
User wants to translate audio/video to English:
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py translate "<FILE_PATH>" --output-dir ~/Downloads
User wants to detect the language:
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py detect "<FILE_PATH>"
User wants file info without transcribing:
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py info "<FILE_PATH>"
Step 3: Report results
Tell the user:
- Detected language and confidence
- The full transcription text
- Where output files were saved (text, SRT subtitles, JSON)
- Processing time
- If translated: both original language and English translation
All Commands
# Transcribe audio/video (auto-detects language, saves .txt + .srt + .json)
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "<FILE>" --output-dir ~/Downloads
# Transcribe with a specific source language (faster, skips detection)
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "<FILE>" --language te
# Transcribe with a larger model for better accuracy
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "<FILE>" --model medium
# Transcribe with a specific HuggingFace fine-tuned model
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "<FILE>" --hf-model "vasista22/whisper-telugu-large-v2"
# Translate any language to English
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py translate "<FILE>" --output-dir ~/Downloads
# Translate with known source language
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py translate "<FILE>" --language te
# Detect language of audio
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py detect "<FILE>"
# Show audio file metadata
/usr/local/opt/[email protected]/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py info "<FILE>"
What ships with it
2 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.
- 2mo ago First seen · 183 lines · 125 tokens per session scan A 84149f862a64
whisper-transcribe is a skill published in the GitHub repository humancto/indic-voice-pipeline (1 stars, last pushed 8mo ago), licensed MIT. It adds 125 tokens to every session and 2,086 once invoked, about $0.0005 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 2026-08-31.
whisper-transcribe compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| whisper-transcribehumancto/indic-voice-pipeline | Skill | 1 | 8mo ago | MIT | A | 125 | Claude Code, Codex |
| configuring-windows-defender-advanced-settingsxalgorix/xalgorix | Skill | 1.3k | today | Apache-2.0 | A | 88 | Claude Code, Codex |
| benchmarksoniqo/speech-swift | Skill | 1.3k | 2d ago | Apache-2.0 | A | 0 | Claude Code |
| review-prsoniqo/speech-swift | Skill | 1.3k | 2d ago | Apache-2.0 | A | 71 | Claude Code |
| delive-transcript-analyzerXimilalaXiang/DeLive | Skill | 261 | 17d ago | Apache-2.0 | A | 98 | Claude Code, Codex |
| kesha-voice-kitdrakulavich/kesha-voice-kit | Skill | 80 | today | MIT | A | 111 | Claude Code, Codex |
| configuring-windows-defender-advanced-settings26zl/cybersec-toolkit | Skill | 93 | 6d ago | MIT | A | 88 | Claude Code |