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nd-video

Explainer videos with HyperFrames (HTML → MP4) plus narrator, music and mixing on NeuralDeep infrastructure. Triggers: "make a video", "explainer", "explain visually", "add voiceover", "add music", "re-render", "change the voice / reverb".

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Prix non confirmé★ 20 Stars GitHubRegistre mis à jour · 9 oct. 2026agent-skill

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nd-video: from idea to MP4

The pipeline was built on the LLM vs Jev video (112 s, media/jev-vs-llm.mp4). Everything that worked there is here. Projects (projects/) are local and not tracked in git. The HyperFrames composition contract is in vendor/hyperframes/skills/hyperframes-core/SKILL.md; read it before writing the first line of HTML.

Workflow

Brief and storyboard. One idea per scene. Each scene gets a question-style heading, 2–4 narrator lines and a "key idea" at the end. Without subtitles and a route map viewers don't understand what they are looking at (the main feedback on the first cut).
Composition projects/<slug>/index.html: npx hyperframes init <slug> --example blank. Proven techniques are below, under "Techniques".
Check: npx hyperframes check and npx hyperframes snapshot --at t1,t2,..., then look at the contact sheet yourself. check flags overlaps where the camera intentionally goes under overlays (subtitles, magnifier); those are false positives, trust the frames.
Narration text in projects/<slug>/narration.json (start + text, windows are computed).
Voice → audio/raw/line_NN.wav:
- scripts/tts_gpt_audio.py: OpenAI gpt-audio via OpenRouter, best delivery. Voice ash.
- scripts/tts_hub.py: the hub TTS (Qwen3-TTS, 8 presets). A bit weak for trailer delivery.
- scripts/tts_elevenlabs.py: ElevenLabs eleven_multilingual_v2. The best Russian of the three; voices lists the account's voices, speak voices narration.json (neighbouring lines go as context).
Music → audio/music.mp3:
- scripts/music_lyria.py: Lyria 3 Pro, $0.08 per track.
- scripts/music_acestep.py: self-hosted ACE-Step 1.5 (MIT), see docs/acestep.md.
Sound: scripts/build_audio.sh projects/<slug> trailer = fit lines into windows (fit_narration.py) → voice processing (voice_fx.py) → mix with ducking (mix.py). The composition has a single <audio id="soundtrack" src="audio/mix.wav"> for the full length.
Render: npx hyperframes render --output renders/<name>.mp4; 112 s renders in ~1.5 min.
Compress for sharing: ffmpeg -i in.mp4 -c:v libx264 -preset slow -crf 30 -tune animation -pix_fmt yuv420p -c:a aac -b:a 96k -movflags +faststart out.mp4. For the Jev video this took 51 MB down to 8.7 MB with no visible loss, small text included.

Outbound commands (gh, curl, npx) run without the system proxy: env -u HTTPS_PROXY -u HTTP_PROXY -u https_proxy -u http_proxy -u ALL_PROXY <command>.

Keys and where to run

what	key	where from
gpt-audio, Lyria	`OPENROUTER_API_KEY`	not from a Russian IP: OpenRouter returns 403. Use a server outside Russia or `OPENROUTER_API_BASE` pointing at your egress
hub TTS	`ND_API_KEY` (your own `sk-`)	anywhere
ACE-Step	`ACESTEP_API_KEY` (if set)	ssh tunnel to the box, the API listens on 127.0.0.1

Never print secrets or commit them (.env is in .gitignore).

Voice: pitfalls

gpt-audio answers like an assistant ("Understood, here is…") and reads tags such as <line> aloud. Fix: a "you are a TTS engine" system prompt, bare text in the user turn, then transcribe the result, compare it with the text and retry (tts_gpt_audio.py does this).
OpenRouter audio output works only with stream: true; the format is pcm16, 24 kHz mono.
A line longer than its window: fit_narration.py speeds it up to ×1.12, beyond that it is audible. TOO LONG = shorten the text and re-voice only that line: --only N.
Processing presets (voice_fx.py): natural (no processing), trailer (final Jev mix: bass +5 at 110 Hz, presence +2.5 at 3 kHz, 4:1 compression, 1.4 s / 18% reverb), deep (pitch −2 semitones), titan (−4), cathedral (3.2 s / 45% reverb), radio (350–3400 Hz band). Small EQ tweaks after loudnorm are barely audible (the first A/B sounded "like clones"), so the presets are spread far apart. Fine-tune with flags: --pitch -1 --bass 7 --wet 0.25 --room 2. Compare by ear: voice_fx.py fit ab --compare 0.
ElevenLabs free tier: pcm output and library voices (native Russian speakers) are paid only (403 / 402), so the script takes mp3 and the premade voices work (Brian nPczCjzI2devNBz1zQrb was picked for Russian). 10 000 characters a month; a 90 s video is ~1 300. Reachable from RU IPs.
Hub TTS for Russian: language Russian goes to ESpeech, which answers 500 on words it doesn't know (terms, transliterated names); Auto goes to Qwen3-TTS, whose presets are not native Russian speakers and drift in timbre and emotion between lines.

Music: pitfalls

Lyria rejects prompts that mention AI/video/product as PROHIBITED_CONTENT (no charge). Describe only the music: genre, BPM, instruments, mood, "No vocals".
A Lyria track is ~170 s; mix.py trims it to the video length, 1.5 s fade-in, 4 s fade-out; --music-offset shifts the start.
Loudness: voice −16 LUFS, music −27 LUFS with sidechain ducking under the voice.
loudnorm resamples to 192 kHz and eats the tail: follow it with aresample and apad to length.

HyperFrames techniques proven on the video

Camera over a "world": a single #world (e.g. 3400×1800) holding two architecture "crystals"; the camera is gsap.to("#world", {x, y, scale}) via camState(x, y, s, sx, sy), which puts a world point at a screen point. Wide shot ~0.43, block ~1.15, detail ~2.7. Read finer details through a magnifier: a round overlay with a separate full-size SVG.
Counters without onUpdate (callbacks are muted on seek): @property --n { syntax: "<integer>"; inherits: true } + counter-reset: n var(--n) in ::before, tween "--n" with snap. inherits must be true, otherwise the pseudo-element sees 0.
Repeated tweens on the same element: fromTo(..., {immediateRender: false}), otherwise a late fromTo resets the earlier state on frame zero.
Don't tween letterSpacing or other layout properties (lint gsap_non_transform_motion), don't tween visibility/autoAlpha on .clip.
Seeded randomness only (mulberry32), no Date.now/Math.random.
Put a dark gradient and a backing plate behind the header, otherwise zoomed text lands on blocks.
Copyright: a corner badge + a faint centered watermark (7% white), removed in the finale.

Factual honesty

When breaking down someone else's model, mark on screen what a source confirms and what is an estimate ("likely", "est.", "illustrative"). In the Jev video MoE, ~10B active parameters and expert specialization are marked this way.

Voir la source ↗

Métadonnées du fichier

name: nd-video
description: Explainer videos with HyperFrames (HTML → MP4) plus narrator, music and mixing on NeuralDeep infrastructure. Triggers: "make a video", "explainer", "explain visually", "add voiceover", "add music", "re-render", "change the voice / reverb".

Voir le texte original

---
name: nd-video
description: Explainer videos with HyperFrames (HTML → MP4) plus narrator, music and mixing on NeuralDeep infrastructure. Triggers: "make a video", "explainer", "explain visually", "add voiceover", "add music", "re-render", "change the voice / reverb".
---

# nd-video: from idea to MP4

The pipeline was built on the LLM vs Jev video (112 s, [`media/jev-vs-llm.mp4`](../../media/jev-vs-llm.mp4)).
Everything that worked there is here. Projects (`projects/`) are local and not tracked in git.
The HyperFrames composition contract is in `vendor/hyperframes/skills/hyperframes-core/SKILL.md`;
read it before writing the first line of HTML.

## Workflow

1. **Brief and storyboard.** One idea per scene. Each scene gets a question-style heading, 2–4
   narrator lines and a "key idea" at the end. Without subtitles and a route map viewers don't
   understand what they are looking at (the main feedback on the first cut).
2. **Composition** `projects/<slug>/index.html`: `npx hyperframes init <slug> --example blank`.
   Proven techniques are below, under "Techniques".
3. **Check**: `npx hyperframes check` and `npx hyperframes snapshot --at t1,t2,...`, then look at
   the contact sheet yourself. `check` flags overlaps where the camera intentionally goes under
   overlays (subtitles, magnifier); those are false positives, trust the frames.
4. **Narration text** in `projects/<slug>/narration.json` (`start` + `text`, windows are computed).
5. **Voice** → `audio/raw/line_NN.wav`:
   - `scripts/tts_gpt_audio.py`: OpenAI gpt-audio via OpenRouter, best delivery. Voice `ash`.
   - `scripts/tts_hub.py`: the hub TTS (Qwen3-TTS, 8 presets). A bit weak for trailer delivery.
   - `scripts/tts_elevenlabs.py`: ElevenLabs `eleven_multilingual_v2`. The best Russian of the three;
     `voices` lists the account's voices, `speak` voices narration.json (neighbouring lines go as context).
6. **Music** → `audio/music.mp3`:
   - `scripts/music_lyria.py`: Lyria 3 Pro, $0.08 per track.
   - `scripts/music_acestep.py`: self-hosted ACE-Step 1.5 (MIT), see `docs/acestep.md`.
7. **Sound**: `scripts/build_audio.sh projects/<slug> trailer` = fit lines into windows
   (`fit_narration.py`) → voice processing (`voice_fx.py`) → mix with ducking (`mix.py`).
   The composition has a single `<audio id="soundtrack" src="audio/mix.wav">` for the full length.
8. **Render**: `npx hyperframes render --output renders/<name>.mp4`; 112 s renders in ~1.5 min.
9. **Compress for sharing**: `ffmpeg -i in.mp4 -c:v libx264 -preset slow -crf 30 -tune animation
   -pix_fmt yuv420p -c:a aac -b:a 96k -movflags +faststart out.mp4`. For the Jev video this took
   51 MB down to 8.7 MB with no visible loss, small text included.

Outbound commands (`gh`, `curl`, `npx`) run without the system proxy:
`env -u HTTPS_PROXY -u HTTP_PROXY -u https_proxy -u http_proxy -u ALL_PROXY <command>`.

## Keys and where to run

| what | key | where from |
|---|---|---|
| gpt-audio, Lyria | `OPENROUTER_API_KEY` | not from a Russian IP: OpenRouter returns 403. Use a server outside Russia or `OPENROUTER_API_BASE` pointing at your egress |
| hub TTS | `ND_API_KEY` (your own `sk-`) | anywhere |
| ACE-Step | `ACESTEP_API_KEY` (if set) | ssh tunnel to the box, the API listens on 127.0.0.1 |

Never print secrets or commit them (`.env` is in `.gitignore`).

## Voice: pitfalls

- gpt-audio answers like an assistant ("Understood, here is…") and reads tags such as `<line>`
  aloud. Fix: a "you are a TTS engine" system prompt, bare text in the user turn, then transcribe
  the result, compare it with the text and retry (`tts_gpt_audio.py` does this).
- OpenRouter audio output works only with `stream: true`; the format is `pcm16`, 24 kHz mono.
- A line longer than its window: `fit_narration.py` speeds it up to ×1.12, beyond that it is
  audible. `TOO LONG` = shorten the text and re-voice only that line: `--only N`.
- Processing presets (`voice_fx.py`): `natural` (no processing), `trailer` (final Jev mix: bass
  +5 at 110 Hz, presence +2.5 at 3 kHz, 4:1 compression, 1.4 s / 18% reverb), `deep` (pitch
  −2 semitones), `titan` (−4), `cathedral` (3.2 s / 45% reverb), `radio` (350–3400 Hz band).
  Small EQ tweaks after loudnorm are barely audible (the first A/B sounded "like clones"), so the
  presets are spread far apart. Fine-tune with flags: `--pitch -1 --bass 7 --wet 0.25 --room 2`.
  Compare by ear: `voice_fx.py fit ab --compare 0`.

- ElevenLabs free tier: pcm output and library voices (native Russian speakers) are paid only
  (403 / 402), so the script takes mp3 and the premade voices work (Brian `nPczCjzI2devNBz1zQrb`
  was picked for Russian). 10 000 characters a month; a 90 s video is ~1 300. Reachable from RU IPs.
- Hub TTS for Russian: `language` Russian goes to ESpeech, which answers 500 on words it doesn't
  know (terms, transliterated names); `Auto` goes to Qwen3-TTS, whose presets are not native
  Russian speakers and drift in timbre and emotion between lines.

## Music: pitfalls

- Lyria rejects prompts that mention AI/video/product as `PROHIBITED_CONTENT` (no charge).
  Describe only the music: genre, BPM, instruments, mood, "No vocals".
- A Lyria track is ~170 s; `mix.py` trims it to the video length, 1.5 s fade-in, 4 s fade-out;
  `--music-offset` shifts the start.
- Loudness: voice −16 LUFS, music −27 LUFS with sidechain ducking under the voice.
- `loudnorm` resamples to 192 kHz and eats the tail: follow it with `aresample` and `apad` to length.

## HyperFrames techniques proven on the video

- **Camera over a "world"**: a single `#world` (e.g. 3400×1800) holding two architecture
  "crystals"; the camera is `gsap.to("#world", {x, y, scale})` via `camState(x, y, s, sx, sy)`,
  which puts a world point at a screen point. Wide shot ~0.43, block ~1.15, detail ~2.7. Read
  finer details through a **magnifier**: a round overlay with a separate full-size SVG.
- **Counters** without `onUpdate` (callbacks are muted on seek): `@property --n { syntax: "<integer>";
  inherits: true }` + `counter-reset: n var(--n)` in `::before`, tween `"--n"` with `snap`.
  `inherits` must be `true`, otherwise the pseudo-element sees 0.
- Repeated tweens on the same element: `fromTo(..., {immediateRender: false})`, otherwise a late
  `fromTo` resets the earlier state on frame zero.
- Don't tween `letterSpacing` or other layout properties (lint `gsap_non_transform_motion`),
  don't tween `visibility`/`autoAlpha` on `.clip`.
- Seeded randomness only (`mulberry32`), no `Date.now`/`Math.random`.
- Put a dark gradient and a backing plate behind the header, otherwise zoomed text lands on blocks.
- Copyright: a corner badge + a faint centered watermark (7% white), removed in the finale.

## Factual honesty

When breaking down someone else's model, mark on screen what a source confirms and what is an
estimate ("likely", "est.", "illustrative"). In the Jev video MoE, ~10B active parameters and
expert specialization are marked this way.

Examiner la source

Prix et coûts d’utilisation

Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: MIT
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source à réexaminer

La source a changé ou sa synchronisation a échoué. Vérifiez-la avant installation.

Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Permission surface may require sandboxing
The skill references a vendor/hyperframes directory that may not be included in the skill package; ensure it is available or document how to obtain it.
The skill assumes the presence of ffmpeg, node, and other command-line tools; list prerequisites explicitly.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 1 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: vakovalskii/nd-video-studio
Licence: MIT
Version: Unknown
Dernier push GitHub: 30 sept. 2026
Registre mis à jour: 9 oct. 2026
Chemin des instructions: skills/nd-video/SKILL.md @ 6243cd7cd7b3

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

60/100

Prometteur

Confiance

55/100

Do not auto-install

Audit

72/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Permission surface may require sandboxing
The skill references a vendor/hyperframes directory that may not be included in the skill package; ensure it is available or document how to obtain it.
The skill assumes the presence of ffmpeg, node, and other command-line tools; list prerequisites explicitly.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 1 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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    "maintenance": "13d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "krillinai-krillinai-render-horizontal",
      "name": "krillinai-render-horizontal",
      "url": "https://www.openagentskill.com/skills/krillinai-krillinai-render-horizontal",
      "stars": 12876,
      "install_command": "npx skills add krillinai/OpenCreator --skill krillinai-render-horizontal",
      "trust_score": 82,
      "audit_score": 85
    },
    {
      "slug": "latent-spaces-brag-slim",
      "name": "brag-slim",
      "url": "https://www.openagentskill.com/skills/latent-spaces-brag-slim",
      "stars": 13807,
      "install_command": "npx skills add latent-spaces/brag --skill brag-slim",
      "trust_score": 81,
      "audit_score": 84
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "The skill references a vendor/hyperframes directory that may not be included in the skill package; ensure it is available or document how to obtain it.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Permission surface may require sandboxing",
    "The skill assumes the presence of ffmpeg, node, and other command-line tools; list prerequisites explicitly.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use nd-video in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 63/100 Manual review",
      "Audit: 72/100 Needs review",
      "Safety: 36/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "vakovalskii-nd-video (nd-video)",
      "install_command": "",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "vakovalskii-nd-video",
      "task": "Use nd-video in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/vakovalskii-nd-video",
    "api": "https://www.openagentskill.com/api/agent/skills/vakovalskii-nd-video",
    "audit": "https://www.openagentskill.com/skills/vakovalskii-nd-video/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=vakovalskii-nd-video&task=Use%20nd-video%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20nd-video%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20nd-video%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/vakovalskii-nd-video/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/vakovalskii-nd-video"
  }
}

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Créateur: vakovalskii
Source: vakovalskii/nd-video-studio
Indexé par: Index communautaire OpenAgentSkill

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