tRPC-Agent-Go is a Go framework for building AI agent applications with language models, graph-based workflows, tools, memory, retrieval, evaluation, and observability. Go developers use it to create deployable agent systems that can integrate with A2A, AG-UI, and MCP. The catalogue add-ons provide reusable workflows and agent integrations for the framework.
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 trpc-group/trpc-agent-go --skill whispergit clone --depth 1 https://github.com/trpc-group/trpc-agent-goWrote 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/trpc-group/trpc-agent-go/whisper)<a href="https://agentmods.dev/skills/trpc-group/trpc-agent-go/whisper"><img src="https://agentmods.dev/badge/skills/trpc-group/trpc-agent-go/whisper/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/trpc-group/trpc-agent-go/whisper"><img src="https://agentmods.dev/badge/skills/trpc-group/trpc-agent-go/whisper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Privilege Escalation · line 75 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00013 | $0.00552 |
| Opus 5 | $0.00006 | $0.00276 |
| Sonnet 5 | $0.00003 | $0.00110 |
| Haiku 4.5 | $0.00001 | $0.00055 |
Grade B, and why
whisper scanned grade B with 1 finding 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 9d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt-get install ffmpeg # Ubuntu/Debian What it actually says
Whisper Audio Transcription Skill
Transcribe audio files to text using OpenAI Whisper.
Capabilities
- Transcribe audio files (MP3, WAV, M4A, FLAC, OGG, etc.) to text
- Support for 90+ languages with auto-detection
- Optional timestamp generation
- Multiple model sizes (tiny/base/small/medium/large)
- Output in plain text or JSON format
Usage
Basic Transcription
python3 scripts/transcribe.py <audio_file> <output_file>
With Options
# Specify model size (default: base)
python3 scripts/transcribe.py audio.mp3 transcript.txt --model medium
# Specify language (improves accuracy)
python3 scripts/transcribe.py audio.mp3 transcript.txt --language zh
# Include timestamps
python3 scripts/transcribe.py audio.mp3 transcript.txt --timestamps
# JSON output with metadata
python3 scripts/transcribe.py audio.mp3 output.json --format json
Parameters
audio_file(required): Path to input audio fileoutput_file(required): Path to output text/JSON file--model: Whisper model size (tiny/base/small/medium/large, default: base)--language: Language code (e.g., en, zh, es, fr, auto for detection)--timestamps: Include word-level timestamps in output--format: Output format (text/json, default: text)
Model Sizes
| Model | Parameters | Speed | Accuracy | Memory |
|---|---|---|---|---|
| tiny | 39M | ~32x | Good | ~1GB |
| base | 74M | ~16x | Better | ~1GB |
| small | 244M | ~6x | Great | ~2GB |
| medium | 769M | ~2x | Excellent | ~5GB |
| large | 1.5B | 1x | Best | ~10GB |
Supported Audio Formats
MP3, WAV, M4A, FLAC, OGG, AAC, WMA, and more (via FFmpeg)
Dependencies
- Python 3.8+
- openai-whisper
- ffmpeg
Installation
pip install openai-whisper
sudo apt-get install ffmpeg # Ubuntu/Debian
What ships with it
1 file 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.
- 9d ago First seen · 77 lines · 13 tokens per session scan B 8c6d3ac6deb9
whisper is a skill published in the GitHub repository trpc-group/trpc-agent-go (1,770 stars, last pushed today), licensed Apache-2.0. It adds 13 tokens to every session and 552 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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