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 calesthio/generative-media-skills --skill kokoro-ttsgit clone --depth 1 https://github.com/calesthio/generative-media-skillsWrote 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/calesthio/generative-media-skills/kokoro-tts)<a href="https://agentmods.dev/skills/calesthio/generative-media-skills/kokoro-tts"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/kokoro-tts/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/calesthio/generative-media-skills/kokoro-tts"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/kokoro-tts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00200 | $0.05282 |
| Opus 5 | $0.00100 | $0.02641 |
| Sonnet 5 | $0.00040 | $0.01056 |
| Haiku 4.5 | $0.00020 | $0.00528 |
Grade A, and why
kokoro-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 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.
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 — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kokoro TTS (open-weight, self-hosted)
Kokoro is a small, fast, permissively licensed text-to-speech model. Its entire value proposition is that you run it yourself: no API key, no per-character billing, no audio leaving your machine. This skill helps an agent decide whether Kokoro fits a job, run it through the right runtime, and get acceptable output — and, just as importantly, recognize the jobs where Kokoro will disappoint the user and something else is the correct answer.
All version, license, voice-count, ranking, and performance facts below were verified on 2026-07-10 against the sources listed at the end. Treat them as volatile.
What Kokoro is (documented facts)
- Model. ~82 million parameters. Architecture is StyleTTS 2 (arXiv 2306.07691) with an ISTFTNet decoder (arXiv 2203.02395). The card describes it as "Decoder only: no diffusion, no encoder release." Source: hexgrad/Kokoro-82M model card.
- License. Apache 2.0, including the weights. v0.19 weights were released in full fp32 on 2024-12-25; v1.0 released 2025-01-27 and is the current default. Because weights are Apache-2.0 you may deploy commercially, redistribute, and fine-tune (subject to attribution). Source: model card.
- Training data & provenance. "Few hundred hrs" for v1.0, trained exclusively on permissive / non-copyrighted material: public-domain audio, Apache/MIT-licensed content, and synthetic audio generated by closed TTS models, plus <1 hr from Koniwa (CC BY 3.0) and <11 hrs from SIWIS (CC BY 4.0). Reported training cost ≈ $1000 (~1000 A100-80GB GPU-hours). The heavy reliance on synthetic data is the root cause of Kokoro's flat prosody and its uneven non-English quality — keep it in mind. Source: model card.
- Output. 24 kHz mono audio. Sample rate is fixed at 24000 Hz. Source: model card.
- Coverage. v1.0 ships 8 languages and 54 voices (American + British English count as one language). Sources: model card, VOICES.md.
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 · 337 lines · 200 tokens per session scan A 13f7c08711ef
kokoro-tts is a skill published in the GitHub repository calesthio/generative-media-skills (171 stars, last pushed 2mo ago), licensed MIT. It adds 200 tokens to every session and 5,282 once invoked, about $0.0010 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.
Other skills, from other repositories
cliptalk-cover-director
Produces evidence-backed cover candidates and reviewable cover variants for a ClipTalk video. Use when the user asks for a cover, poster frame, thumbnail, or multiple cover directions; do not use for timeline editing or social-video reframing.
cliptalk-smart-reframe
Creates a subject-aware, time-varying crop track and a review-only social-format preview from an accepted ClipTalk cut. Use for automatic vertical, square, or portrait reframing; do not use for a fixed manual crop or before content editing is accepted.
cliptalk-content-extractor
Locates and assembles source passages matching a semantic request. Use for extracting explanations, topics, quotes, demonstrations, or other specifically described content.
cliptalk-interview-editor
Produces a coherent interview edit by combining speaker discovery, topic selection, dialogue context, cleanup, subtitles, and preview. Use for interviews, podcasts, testimonials, or question-and-answer recordings.
cliptalk-shortform-hook-director
Finds and assembles a reviewable short-form cut with a strong opening hook. Use for Shorts, Reels, social clips, talking-head cutdowns, or requests for a punchier opening.
cliptalk-social-reframe-exporter
Creates a review-only 9:16, 4:5, 1:1, or 16:9 version from an existing accepted ClipTalk cut, then checks the rendered preview. Use only when a cut already exists and the user asks to adapt it for Shorts, Reels, Douyin, Xiaohongshu, WeChat Channels, or square feeds; do not use when the user still needs content found…