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 agentmods add skills/opencue/cuecards/edge-ttsnpx skills add opencue/cuecards --skill edge-ttsgit clone --depth 1 https://github.com/opencue/cuecardsWrote 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/opencue/cuecards/edge-tts)<a href="https://agentmods.dev/skills/opencue/cuecards/edge-tts"><img src="https://agentmods.dev/badge/skills/opencue/cuecards/edge-tts.svg" alt="Measured on agentmods" 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 | $0.00085 | $0.00973 |
| Opus 5 | $0.00043 | $0.00487 |
| Sonnet 5 | $0.00017 | $0.00195 |
| Haiku 4.5 | $0.00009 | $0.00097 |
Grade A, and why
edge-tts 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 6d 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
100% identical to edge-tts — 0 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Edge-TTS
Generate high-quality text-to-speech audio using Microsoft Edge's neural TTS service via the uvx edge-tts command.
Supports multiple languages, voices, adjustable speed/pitch, and subtitle generation.
Usage
uvx edge-tts --text "{msg}" --write-media {tempdir}/{filename}.mp3
# With subtitles
uvx edge-tts --text "{msg}" --write-media {tempdir}/{filename}.mp3 --write-subtitles -
Changing rate(speed), volume and pitch
uvx edge-tts --text "{msg}" --write-media {tempdir}/{filename}.mp3 --rate=+50%
uvx edge-tts --text "{msg}" --write-media {tempdir}/{filename}.mp3 --volume=+50% --pitch=-50Hz
Changing the voice
uvx edge-tts --text "{msg}" --write-media {tempdir}/{filename}.mp3 --voice zh-CN-XiaoxiaoNeural
Available voices
Name Gender ContentCategories VoicePersonalities
en-GB-LibbyNeural Female General Friendly, Positive
en-GB-RyanNeural Male General Friendly, Positive
en-GB-SoniaNeural Female General Friendly, Positive
en-GB-ThomasNeural Male General Friendly, Positive
en-HK-SamNeural Male General Friendly, Positive
en-HK-YanNeural Female General Friendly, Positive
en-US-AnaNeural Female Cartoon, Conversation Cute
en-US-AndrewMultilingualNeural Male Conversation, Copilot Warm, Confident, Authentic, Honest
en-US-AndrewNeural Male Conversation, Copilot Warm, Confident, Authentic, Honest
en-US-AriaNeural Female News, Novel Positive, Confident
en-US-AvaMultilingualNeural Female Conversation, Copilot Expressive, Caring, Pleasant, Friendly
en-US-AvaNeural Female Conversation, Copilot Expressive, Caring, Pleasant, Friendly
en-US-BrianMultilingualNeural Male Conversation, Copilot Approachable, Casual, Sincere
en-US-BrianNeural Male Conversation, Copilot Approachable, Casual, Sincere
en-US-ChristopherNeural Male News, Novel Reliable, Authority
en-US-EmmaMultilingualNeural Female Conversation, Copilot Cheerful, Clear, Conversational
en-US-EmmaNeural Female Conversation, Copilot Cheerful, Clear, Conversational
en-US-EricNeural Male News, Novel Rational
en-US-GuyNeural Male News, Novel Passion
en-US-JennyNeural Female General Friendly, Considerate, Comfort
en-US-MichelleNeural Female News, Novel Friendly, Pleasant
en-US-RogerNeural Male News, Novel Lively
en-US-SteffanNeural Male News, Novel Rational
fr-FR-DeniseNeural Female General Friendly, Positive
fr-FR-HenriNeural Male General Friendly, Positive
zh-CN-XiaoxiaoNeural Female News, Novel Warm
zh-CN-YunjianNeural Male Sports, Novel Passion
zh-CN-liaoning-XiaobeiNeural Female Dialect Humorous
zh-CN-shaanxi-XiaoniNeural Female Dialect Bright
zh-HK-HiuGaaiNeural Female General Friendly, Positive
zh-HK-WanLungNeural Male General Friendly, Positive
zh-TW-HsiaoChenNeural Female General Friendly, Positive
zh-TW-YunJheNeural Male General Friendly, Positive
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.
- 6d ago First seen · 72 lines · 85 tokens per session scan A d5b45ae56a54
edge-tts is a skill published in the GitHub repository opencue/cuecards (5 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 973 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to edge-tts, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
go-testing
Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.
rulesync
Generates and syncs AI rule configuration files (.cursorrules, CLAUDE.md, copilot-instructions.md) across 20+ coding tools from a single source. Use when syncing AI rules, running rulesync commands, importing or generating rule files, or managing shared AI coding configurations.
autoprompt
Explicit-only useful-first orchestration. Invoke /autoprompt to turn a mission into one executable roadmap, build dependency-safe lanes, and verify the result with independent reviewers. Never infer invocation from ordinary requests. Never resume from leftover artifacts without an explicit resume instruction.
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.
gitnexus
将 GitNexus 作为受控可选代码智能能力接入，用于 brownfield MCP 查询、影响面分析、 detectchanges、多仓分析和更深代码图谱证据。输出必须回落到 /team- 主链和 artifacts。.
loop
Full execution protocol for MODE: LOOP — the compound-engineering loop: brainstorm → plan → build → review → improve, iterating under defense-in-depth stop conditions with generator/critic separation, durable resumable state, and mandatory compounding learning capture. Loaded on demand by the architect when the loop…