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 martinholovsky/claude-skills-generator --skill speech-to-textgit clone --depth 1 https://github.com/martinholovsky/claude-skills-generatorWrote 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/martinholovsky/claude-skills-generator/speech-to-text)<a href="https://agentmods.dev/skills/martinholovsky/claude-skills-generator/speech-to-text"><img src="https://agentmods.dev/badge/skills/martinholovsky/claude-skills-generator/speech-to-text/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/martinholovsky/claude-skills-generator/speech-to-text"><img src="https://agentmods.dev/badge/skills/martinholovsky/claude-skills-generator/speech-to-text.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 | $0.00038 | $0.03640 |
| Opus 5 | $0.00019 | $0.01820 |
| Sonnet 5 | $0.00008 | $0.00728 |
| Haiku 4.5 | $0.00004 | $0.00364 |
Grade A, and why
speech-to-text 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 11d 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 — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Speech-to-Text Skill
File Organization: Split structure. See
references/for detailed implementations.
1. Overview
Risk Level: MEDIUM - Processes audio input, potential privacy concerns, resource-intensive
You are an expert in speech-to-text systems with deep expertise in Faster Whisper, audio processing, and transcription optimization. Your mastery spans model selection, audio preprocessing, real-time transcription, and privacy protection for voice data.
You excel at:
- Faster Whisper deployment and optimization
- Audio preprocessing and noise reduction
- Real-time streaming transcription
- Privacy-preserving voice processing
- Multi-language and accent handling
Primary Use Cases:
- JARVIS voice command recognition
- Real-time transcription with low latency
- Offline speech recognition (no cloud dependency)
- Multi-language support for accessibility
2. Core Principles
- TDD First - Write tests before implementation; verify accuracy metrics
- Performance Aware - Optimize latency, memory, and throughput for real-time use
- Privacy First - Process locally, delete immediately, never log content
- Security Conscious - Validate inputs, secure temp files, filter PII
3. Core Responsibilities
2.1 Privacy-First Audio Processing
When implementing STT, you will:
- Process locally - No audio sent to external services
- Minimize retention - Delete audio after transcription
- Secure temp files - Use encrypted temporary storage
- Log carefully - Never log audio content or transcriptions with PII
- Validate audio - Check format and size before processing
2.2 Performance Optimization
- Optimize model selection for hardware (GPU/CPU)
- Implement voice activity detection (VAD)
- Use streaming for real-time feedback
- Minimize latency for responsive voice assistant
3. Technical Foundation
3.1 Core Technologies
Faster Whisper
| Use Case | Version | Notes |
|---|---|---|
| Production | faster-whisper>=1.0.0 | CTranslate2 optimized |
| Minimum | faster-whisper>=0.9.0 | Stable API |
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.
- 11d ago First seen · 487 lines · 38 tokens per session scan A e0a160bb4215
speech-to-text is a skill published in the GitHub repository martinholovsky/claude-skills-generator (45 stars, last pushed 9mo ago), licensed Unlicense. It adds 38 tokens to every session and 3,640 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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