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ndt-content

Content made entirely on NeuralDeep models (api.neuraldeep.ru), no external APIs: research, script, voice, images, speech verification, word-level subtitles, frame review, music on your own GPU. Triggers: "make a video on our models", "NDT only", "content through the hub", "witho

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Harga belum dikonfirmasi★ 20 Star GitHubDirektori diperbarui · 9 Okt 2026agent-skill

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ndt-content: from brief to MP4 on hub models

One key, ND_API_KEY (your own hub sk-), for everything. Frame building, sound and rendering are the same as in the nd-video skill: its "HyperFrames techniques", "Voice: pitfalls" and "Music: pitfalls" sections apply here too. This skill changes the sources: instead of OpenRouter (gpt-audio, Lyria) and Gemini, only the hub and your own GPU.

What comes from where

step	hub model	endpoint	script
research	Search API (web, tg)	`/v1/search/web`, `/v1/search/tg`	`nd_research.py`
script	`qwen3.8-27b-noreason` (or `kimi-k2.6`, `gemma-4-31b-noreason`)	`/v1/chat/completions`	`nd_script.py`
voice	`qwen3-tts` (8 voices, style as text), Russian via ESpeech + RUAccent	`/v1/audio/speech`	`tts_hub.py`
timing	from the real voiceover length	—	`nd_retime.py`, `fit_narration.py`
speech check + words	`whisper-1`	`/v1/audio/transcriptions`	`nd_align.py`
images	FLUX, background removal, upscale	`/v1/images/*`	`nd_images.py`
frame review	`qwen3.6-35b-a3b-noreason` (vision)	`/v1/chat/completions`	`nd_review.py`
music	self-hosted ACE-Step 1.5 on your own GPU (the hub has no music model)	box REST	`music_acestep.py`, docs/acestep.md
voice processing, mixing	local, ffmpeg	—	`voice_fx.py`, `mix.py`

For JSON and short structured answers use only the -noreason aliases: base qwen models always think and return an empty content on short answers.

Check that everything above is up before a run: python3 scripts/nd_probe.py pings every chat model from /v1/models and walks TTS → whisper, FLUX → vision, quota and search (--no-images saves image quota).

Workflow

Run everything from the project folder, S=../../scripts, key in env (never print or commit it).

mkdir -p projects/<slug> && cd projects/<slug>
$EDITOR brief.md                                   # topic, audience, tone, length

python3 $S/nd_research.py sources.md "<query 1>" "<query 2>" [--tg "<query>"]
python3 $S/nd_script.py brief.md narration.json --sources sources.md --lang en --seconds 60
#   → scenes (question heading, key_idea) and lines; read them, fix facts by hand

python3 $S/tts_hub.py narration.json audio/raw --voice ryan    # ru: language comes from narration.json
python3 $S/nd_retime.py narration.json audio/raw               # starts from the real voiceover
python3 $S/fit_narration.py narration.json audio/raw audio/fit
python3 $S/nd_align.py narration.json audio/fit words.json     # BAD → re-voice: tts_hub.py --only N

python3 $S/nd_images.py assets "bg:<prompt>" "hero:<prompt>" --remove-bg hero

# compose index.html from narration.json (scenes → headings, lines → subtitles,
# words.json → highlight each word as it is spoken), techniques in the nd-video skill
npx hyperframes check && npx hyperframes snapshot --at <t1>,<t2>,...
python3 $S/nd_review.py snapshots/contact-sheet-*.jpg --brief "horizontal 16:9 explainer"

bash $S/build_audio.sh . trailer                  # fx + music ducking → audio/mix.wav
npx hyperframes render --output renders/<slug>.mp4

npx, curl, gh run without the system proxy: env -u HTTPS_PROXY -u HTTP_PROXY -u https_proxy -u http_proxy -u ALL_PROXY <cmd>.

Order matters: voice first, then HTML

TTS pace can't be guessed from text: ryan speaks ~1.7 words/s while the script draft assumes 2.1. So the starts from nd_script.py are a draft; nd_retime.py sets the real ones from clip lengths. Lay out the composition after retime, otherwise the animation drifts from the voice. If the HTML is already laid out to fixed starts (like the Jev video), skip retime: there fit_narration.py squeezes lines into their windows, and long ones get shortened in the text.

Pitfalls caught on the demo

TTS swallows and mixes up words, easy to miss by ear: "reuse" came out as "ray use". nd_align.py compares the transcript with the text and exits with code 2 and an --only N list. Fix it by rephrasing the line, not by rerunning the same text.
A one-off gateway 503 can hit any endpoint. ndv_common._retry retries 429 and 5xx with a pause (Retry-After if present).
Images are square 1312×1312 by default. Set the size with --options '{...}', or simply place the image with object-fit: cover.
Vision review reads the contact-sheet grid imprecisely (mixes up timestamps), but it does catch problems: small text, overlaps, empty frames. It is a second pair of eyes, not a replacement for looking at the frames yourself.
Quotas: search (web costs more than tg), images (/v1/images/quota), TTS in characters, all per the key's plan. A quota refusal is a 429 with Retry-After.

Music

The hub has no music model. Self-hosted ACE-Step 1.5 XL (MIT) runs on your own GPU box (<gpu-box> is its ssh alias); setup and card limits are in docs/acestep.md. To play by hand: ssh -N -L 18001:127.0.0.1:8001 <gpu-box> & and python3 scripts/music_studio.py → http://127.0.0.1:8765 (presets, up to 2 variants, history; the "to project" button puts the track into projects/<slug>/audio/music.mp3 and keeps the previous one next to it). Reference: XL turbo 4B + LM 1.7B on a single A4500 20 GB, two 60 s variants in 11 s. While the box is down, build the video without music: mix.py with an empty track or build_audio.sh without audio/music.mp3.

Syncing to music

From vibecoder-anthem (the "ЖГИ ТОКЕНЫ" clip): cuts, camera hits and karaoke land on the track's real beats, not on a BPM grid.

uv run --no-project --with librosa python3 $S/beat_map.py audio/music.mp3 beats.json --bpm 174

Paste beats.json into the page as window.BEATS = {...}, next to tools/beat-sync.js: beat.snap(t) / beat.nextBar(t) move a rough time onto a beat or downbeat, beat.hits(from, to, min) returns hits for shakes and flashes, beat.warpFrom(anchors) moves a finished animation onto a new version of the track: [new, old] anchors on the same events, linear in between. Word-level timings for narration or vocals come from nd_align.py.

The tempo detector gets octave errors: the Jev track from Lyria was requested at 110 and came out as 73.8 (i.e. 147.6/2). The --bpm hint helps, but always check the number.
ACE-Step holds the requested tempo: DnB at 174 gave 172.3 BPM, beat jitter ±5 ms, no drift.
Verify sync with a contact sheet on the hits: snapshot --at at the times from hits.

Honesty

The script is written only from sources.md; estimates are marked in the text ("likely", "est.").
In the post about the video, state exactly what was used. If even one step bypassed the hub (e.g. music from Lyria), don't write "made entirely on neuraldeep.ru models".

Example

A 31 s demo about KV cache went through the whole pipeline on the hub (research → script → ryan → retime → whisper check with one caught TTS error → image → review).

Lihat sumber ↗

Metadata berkas

name: ndt-content
description: Content made entirely on NeuralDeep models (api.neuraldeep.ru), no external APIs: research, script, voice, images, speech verification, word-level subtitles, frame review, music on your own GPU. Triggers: "make a video on our models", "NDT only", "content through the hub", "without OpenRouter", "video from a brief".

Lihat teks asli

---
name: ndt-content
description: Content made entirely on NeuralDeep models (api.neuraldeep.ru), no external APIs: research, script, voice, images, speech verification, word-level subtitles, frame review, music on your own GPU. Triggers: "make a video on our models", "NDT only", "content through the hub", "without OpenRouter", "video from a brief".
---

# ndt-content: from brief to MP4 on hub models

One key, `ND_API_KEY` (your own hub `sk-`), for everything. Frame building, sound and rendering
are the same as in the [`nd-video`](../nd-video/SKILL.md) skill: its "HyperFrames techniques",
"Voice: pitfalls" and "Music: pitfalls" sections apply here too. This skill changes the sources:
instead of OpenRouter (gpt-audio, Lyria) and Gemini, only the hub and your own GPU.

## What comes from where

| step | hub model | endpoint | script |
|---|---|---|---|
| research | Search API (web, tg) | `/v1/search/web`, `/v1/search/tg` | `nd_research.py` |
| script | `qwen3.8-27b-noreason` (or `kimi-k2.6`, `gemma-4-31b-noreason`) | `/v1/chat/completions` | `nd_script.py` |
| voice | `qwen3-tts` (8 voices, style as text), Russian via ESpeech + RUAccent | `/v1/audio/speech` | `tts_hub.py` |
| timing | from the real voiceover length | — | `nd_retime.py`, `fit_narration.py` |
| speech check + words | `whisper-1` | `/v1/audio/transcriptions` | `nd_align.py` |
| images | FLUX, background removal, upscale | `/v1/images/*` | `nd_images.py` |
| frame review | `qwen3.6-35b-a3b-noreason` (vision) | `/v1/chat/completions` | `nd_review.py` |
| music | self-hosted ACE-Step 1.5 on your own GPU (the hub has no music model) | box REST | `music_acestep.py`, [docs/acestep.md](../../docs/acestep.md) |
| voice processing, mixing | local, ffmpeg | — | `voice_fx.py`, `mix.py` |

For JSON and short structured answers use only the `-noreason` aliases: base qwen models always
think and return an empty `content` on short answers.

Check that everything above is up before a run: `python3 scripts/nd_probe.py` pings every chat
model from `/v1/models` and walks TTS → whisper, FLUX → vision, quota and search
(`--no-images` saves image quota).

## Workflow

Run everything from the project folder, `S=../../scripts`, key in env (never print or commit it).

```bash
mkdir -p projects/<slug> && cd projects/<slug>
$EDITOR brief.md                                   # topic, audience, tone, length

python3 $S/nd_research.py sources.md "<query 1>" "<query 2>" [--tg "<query>"]
python3 $S/nd_script.py brief.md narration.json --sources sources.md --lang en --seconds 60
#   → scenes (question heading, key_idea) and lines; read them, fix facts by hand

python3 $S/tts_hub.py narration.json audio/raw --voice ryan    # ru: language comes from narration.json
python3 $S/nd_retime.py narration.json audio/raw               # starts from the real voiceover
python3 $S/fit_narration.py narration.json audio/raw audio/fit
python3 $S/nd_align.py narration.json audio/fit words.json     # BAD → re-voice: tts_hub.py --only N

python3 $S/nd_images.py assets "bg:<prompt>" "hero:<prompt>" --remove-bg hero

# compose index.html from narration.json (scenes → headings, lines → subtitles,
# words.json → highlight each word as it is spoken), techniques in the nd-video skill
npx hyperframes check && npx hyperframes snapshot --at <t1>,<t2>,...
python3 $S/nd_review.py snapshots/contact-sheet-*.jpg --brief "horizontal 16:9 explainer"

bash $S/build_audio.sh . trailer                  # fx + music ducking → audio/mix.wav
npx hyperframes render --output renders/<slug>.mp4
```

`npx`, `curl`, `gh` run without the system proxy:
`env -u HTTPS_PROXY -u HTTP_PROXY -u https_proxy -u http_proxy -u ALL_PROXY <cmd>`.

## Order matters: voice first, then HTML

TTS pace can't be guessed from text: `ryan` speaks ~1.7 words/s while the script draft assumes
2.1. So the starts from `nd_script.py` are a draft; `nd_retime.py` sets the real ones from clip
lengths. Lay out the composition after retime, otherwise the animation drifts from the voice. If
the HTML is already laid out to fixed starts (like the Jev video), skip retime: there
`fit_narration.py` squeezes lines into their windows, and long ones get shortened in the text.

## Pitfalls caught on the demo

- **TTS swallows and mixes up words**, easy to miss by ear: "reuse" came out as "ray use".
  `nd_align.py` compares the transcript with the text and exits with code 2 and an `--only N` list.
  Fix it by rephrasing the line, not by rerunning the same text.
- **A one-off gateway 503** can hit any endpoint. `ndv_common._retry` retries 429 and 5xx
  with a pause (`Retry-After` if present).
- **Images are square 1312×1312** by default. Set the size with `--options '{...}'`,
  or simply place the image with `object-fit: cover`.
- **Vision review** reads the contact-sheet grid imprecisely (mixes up timestamps), but it does
  catch problems: small text, overlaps, empty frames. It is a second pair of eyes, not a
  replacement for looking at the frames yourself.
- **Quotas**: search (web costs more than tg), images (`/v1/images/quota`), TTS in characters,
  all per the key's plan. A quota refusal is a 429 with `Retry-After`.

## Music

The hub has no music model. Self-hosted ACE-Step 1.5 XL (MIT) runs on your own GPU box
(`<gpu-box>` is its ssh alias); setup and card limits are in [docs/acestep.md](../../docs/acestep.md).
To play by hand: `ssh -N -L 18001:127.0.0.1:8001 <gpu-box> &` and
`python3 scripts/music_studio.py` → http://127.0.0.1:8765 (presets, up to 2 variants, history;
the "to project" button puts the track into `projects/<slug>/audio/music.mp3` and keeps the
previous one next to it). Reference: XL turbo 4B + LM 1.7B on a single A4500 20 GB, two 60 s
variants in 11 s. While the box is down, build the video without music: `mix.py` with an empty
track or `build_audio.sh` without `audio/music.mp3`.

## Syncing to music

From [vibecoder-anthem](https://github.com/rocketmandrey/vibecoder-anthem) (the "ЖГИ ТОКЕНЫ"
clip): cuts, camera hits and karaoke land on the **track's real beats**, not on a BPM grid.

```bash
uv run --no-project --with librosa python3 $S/beat_map.py audio/music.mp3 beats.json --bpm 174
```

Paste `beats.json` into the page as `window.BEATS = {...}`, next to `tools/beat-sync.js`:
`beat.snap(t)` / `beat.nextBar(t)` move a rough time onto a beat or downbeat,
`beat.hits(from, to, min)` returns hits for shakes and flashes, `beat.warpFrom(anchors)` moves a
finished animation onto a new version of the track: `[new, old]` anchors on the same events,
linear in between. Word-level timings for narration or vocals come from `nd_align.py`.

- The tempo detector gets octave errors: the Jev track from Lyria was requested at 110 and came
  out as 73.8 (i.e. 147.6/2). The `--bpm` hint helps, but always check the number.
- ACE-Step holds the requested tempo: DnB at 174 gave 172.3 BPM, beat jitter ±5 ms, no drift.
- Verify sync with a contact sheet on the hits: `snapshot --at` at the times from `hits`.

## Honesty

- The script is written only from `sources.md`; estimates are marked in the text ("likely", "est.").
- In the post about the video, state exactly what was used. If even one step bypassed the hub
  (e.g. music from Lyria), don't write "made entirely on neuraldeep.ru models".

## Example

A 31 s demo about KV cache went through the whole pipeline on the hub (research → script →
`ryan` → retime → whisper check with one caught TTS error → image → review).

Tinjau sumber

Harga dan biaya penggunaan

Dapatkan skill: Harga belum dikonfirmasi
Jalankan: Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
Lisensi: MIT
Harga belum dikonfirmasi: Harga belum dikonfirmasi. Tautan sumber dan instalasi yang ada tetap tersedia.

Gratis diperoleh bukan berarti gratis dijalankan. Harga bukan penilaian keamanan. Kirim informasi harga →

Sumber perlu ditinjau

Sumber berubah atau gagal disinkronkan. Tinjau sumber terbaru sebelum memasang.

Tinjau sebelum memasang: Hindari pemasangan otomatis

Lisensi: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
The skill relies on external services (api.neuraldeep.ru) and a self-hosted GPU box, which may introduce availability or configuration dependencies not fully covered in SKILL.md.
The skill references other skills (nd-video) and documentation (docs/acestep.md) that are not included in the excerpt; ensure they are present in the repository for full reproducibility.
Low GitHub adoption signal
Financial research output is not financial advice; require human review before any live investment decision.
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
Dependency/runtime risk: command execution surface, credential or environment access

Buka audit lengkap

Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.

Mulai dengan tugas kecil

1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.

Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

Sumber dan catatan penggunaan

Terindeks

Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.

Repositori sumber: vakovalskii/nd-video-studio
Lisensi: MIT
Versi: Unknown
Push GitHub terakhir: 30 Sep 2026
Direktori diperbarui: 9 Okt 2026
Jalur instruksi: skills/ndt-content/SKILL.md @ 6243cd7cd7b3

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

60/100

Menjanjikan

Kepercayaan

54/100

Do not auto-install

Audit

71/100

Perlu ditinjau

Buka audit lengkap Lihat laporan evaluasi

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
The skill relies on external services (api.neuraldeep.ru) and a self-hosted GPU box, which may introduce availability or configuration dependencies not fully covered in SKILL.md.
The skill references other skills (nd-video) and documentation (docs/acestep.md) that are not included in the excerpt; ensure they are present in the repository for full reproducibility.
Low GitHub adoption signal
Financial research output is not financial advice; require human review before any live investment decision.
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
Dependency/runtime risk: command execution surface, credential or environment access

Verified installs: —
Hasil: —

Menyalin bukan memasang. Jumlah instalasi memerlukan laporan berhasil dan bukan jaminan kualitas menyeluruh.

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Detail lainnya

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  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 71,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "The skill relies on external services (api.neuraldeep.ru) and a self-hosted GPU box, which may introduce availability or configuration dependencies not fully covered in SKILL.md.",
      "The skill references other skills (nd-video) and documentation (docs/acestep.md) that are not included in the excerpt; ensure they are present in the repository for full reproducibility.",
      "Low GitHub adoption signal",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 60,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "13d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "mvanhorn-last30days-skill",
      "name": "Last30days Skill",
      "url": "https://www.openagentskill.com/skills/mvanhorn-last30days-skill",
      "stars": 63666,
      "install_command": "",
      "trust_score": 94,
      "audit_score": 95
    },
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 83,
      "audit_score": 90
    },
    {
      "slug": "imbad0202-academic-research-skills",
      "name": "Academic Research Skills",
      "url": "https://www.openagentskill.com/skills/imbad0202-academic-research-skills",
      "stars": 38374,
      "install_command": "",
      "trust_score": 89,
      "audit_score": 91
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "The skill relies on external services (api.neuraldeep.ru) and a self-hosted GPU box, which may introduce availability or configuration dependencies not fully covered in SKILL.md.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision"
  ],
  "agent_contract": {
    "task_input": "Use ndt-content 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: 62/100 Manual review",
      "Audit: 71/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "vakovalskii-ndt-content (ndt-content)",
      "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-ndt-content",
      "task": "Use ndt-content 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-ndt-content",
    "api": "https://www.openagentskill.com/api/agent/skills/vakovalskii-ndt-content",
    "audit": "https://www.openagentskill.com/skills/vakovalskii-ndt-content/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=vakovalskii-ndt-content&task=Use%20ndt-content%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ndt-content%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ndt-content%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/vakovalskii-ndt-content/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/vakovalskii-ndt-content"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: vakovalskii
Sumber: vakovalskii/nd-video-studio
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan vakovalskii, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/vakovalskii-ndt-content?metric=listed&label=Listed)](https://www.openagentskill.com/skills/vakovalskii-ndt-content?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/vakovalskii-ndt-content?metric=trust&label=Trust)](https://www.openagentskill.com/skills/vakovalskii-ndt-content?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/vakovalskii-ndt-content?metric=audit&label=Audit)](https://www.openagentskill.com/skills/vakovalskii-ndt-content/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/vakovalskii-ndt-content?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/vakovalskii-ndt-content?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.