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 xuansenpa1/skillrevise --skill text-to-speechgit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote 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/xuansenpa1/skillrevise/text-to-speech)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/text-to-speech"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/text-to-speech/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/xuansenpa1/skillrevise/text-to-speech"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/text-to-speech.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 | $0.00027 | $0.00551 |
| Opus 5 | $0.00014 | $0.00275 |
| Sonnet 5 | $0.00005 | $0.00110 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
TTS Audio Mastering 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.
This is a copy
91% identical to text-to-speech — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: TTS Audio Mastering
This skill focuses on producing clean, consistent, and delivery-ready TTS audio for video tasks. It covers speech cleanup, loudness normalization, segment boundaries, and export specs.
1. TTS Engine & Output Basics
Choose a TTS engine based on deployment constraints and quality needs:
- Neural offline (e.g., Kokoro): stable, high quality, no network dependency.
- Cloud TTS (e.g., Edge-TTS / OpenAI TTS): convenient, higher naturalness but network-dependent.
- Formant TTS (e.g., espeak-ng): for prototyping only; often less natural.
Key rule: Always confirm the native sample rate of the generated audio before resampling for video delivery.
2. Speech Cleanup (Per Segment)
Apply lightweight processing to avoid common artifacts:
- Rumble/DC removal: high-pass filter around 20 Hz
- Harshness control: optional low-pass around 16 kHz (helps remove digital fizz)
- Click/pop prevention: short fades at boundaries (e.g., 50 ms fade-in and fade-out)
Recommended FFmpeg pattern (example):
- Add filters in a single chain, and keep them consistent across segments.
3. Loudness Normalization
Target loudness depends on the benchmark/task spec. A common target is ITU-R BS.1770 loudness measurement:
- Integrated loudness: -23 LUFS
- True peak: around -1.5 dBTP
- LRA: around 11 (optional)
Recommended workflow:
- Measure loudness using FFmpeg
ebur128(or equivalent meter). - Apply normalization (e.g.,
loudnorm) as the final step after cleanup and timing edits. - If you adjust tempo/duration after normalization, re-normalize again.
4. Timing & Segment Boundary Handling
When stitching segment-level TTS into a full track:
- Match each segment to its target window as closely as possible.
- If a segment is shorter than its window, pad with silence.
- If a segment is longer, use gentle duration control (small speed change) or truncate carefully.
- Always apply boundary fades after padding/trimming to avoid clicks.
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 · 63 lines · 27 tokens per session scan A cfb22003f4e9
TTS Audio Mastering is a skill published in the GitHub repository xuansenpa1/skillrevise (56 stars, last pushed 6d ago), licensed MIT. It adds 27 tokens to every session and 551 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to text-to-speech, differing in 2 lines, and is treated as a copy.
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