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 perniemann/pnCore --skill pn-transcribegit clone --depth 1 https://github.com/perniemann/pnCoreWrote 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/perniemann/pncore/pn-transcribe)<a href="https://agentmods.dev/skills/perniemann/pncore/pn-transcribe"><img src="https://agentmods.dev/badge/skills/perniemann/pncore/pn-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/perniemann/pncore/pn-transcribe"><img src="https://agentmods.dev/badge/skills/perniemann/pncore/pn-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 | $0.00051 | $0.01991 |
| Opus 5 | $0.00026 | $0.00996 |
| Sonnet 5 | $0.00010 | $0.00398 |
| Haiku 4.5 | $0.00005 | $0.00199 |
Grade A, and why
pn-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 8d 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcription and captions (STT)
When to use
- Generating a timestamped SRT or VTT caption track from narration audio produced by
pn-tts. - Transcribing screen recording audio, interview recordings, or voiceover for accessibility compliance.
- Producing word-level timestamps for caption-synced animation (bouncy captions, karaoke-style reveals).
- Validating that AI-generated TTS matches the intended script (hallucination check).
Tool selection
| Tool | Model | Best fit | Notes |
|---|---|---|---|
| Whisper (OpenAI API) | whisper-1 |
Quick, accurate, many languages, word timestamps | response_format: verbose_json for timestamps |
| Whisper (local / faster-whisper) | large-v3, medium | Offline, batching, custom alignment | faster-whisper is 4× faster than original; CTranslate2 backend |
| AssemblyAI | Universal-2 | Speaker diarization, auto-chapters, redaction | Best for multi-speaker; paid per minute |
| ElevenLabs timestamps API | — | When audio was generated by ElevenLabs TTS | Returns character-level alignment; no separate transcription step |
| Gentle (forced alignment) | Kaldi | When you have the transcript and just need timestamps | Requires known-correct transcript; aligns speech to text exactly |
Default: Whisper via OpenAI API (simple, no infra) or faster-whisper local (batch, cost-sensitive, offline).
Workflow
1. Pre-process audio
Ensure clean audio before transcription — noise, music, and reverb increase error rate.
# Strip music bed, keep voice (if isolated tracks are available)
# Normalize loudness
ffmpeg -i narration.wav -af loudnorm=I=-14:TP=-1:LRA=11 narration-clean.wav
2. Transcribe with word-level timestamps
OpenAI API:
from openai import OpenAI
client = OpenAI()
with open('narration-clean.wav', 'rb') as f:
result = client.audio.transcriptions.create(
model='whisper-1',
file=f,
response_format='verbose_json',
timestamp_granularities=['word', 'segment'],
)
# result.words: [{'word': 'Hello', 'start': 0.0, 'end': 0.24}, ...]
# result.segments: segment-level blocks for SRT generation
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.
- 8d ago First seen · 204 lines · 51 tokens per session scan A 2028057664a6
pn-transcribe is a skill published in the GitHub repository perniemann/pnCore (0 stars, last pushed 5d ago), licensed MIT. It adds 51 tokens to every session and 1,991 once invoked, about $0.0003 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 2026-09-03.
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