[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73GJ5FKSWXTE2D30FKQVRP4":3,"skill-same:01M73GJ5FKSWXTE2D30FKQVRP4":51},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":32,"ownerHtmlUrl":33,"ownerBlogUrl":34,"ownerDescription":35,"updatedAt":36,"install":37,"resources":50},"01M73GJ5FKSWXTE2D30FKQVRP4","venice--venice-audio-transcription",null,"Transcribe audio files to text via POST \u002Faudio\u002Ftranscriptions. Covers supported models (Parakeet, Whisper, Wizper, Scribe, xAI STT), supported formats (wav\u002Fflac\u002Fm4a\u002Faac\u002Fmp4\u002Fmp3\u002Fogg\u002Fwebm), response formats (json\u002Ftext), timestamps, and language hints. OpenAI-compatible multipart.","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fvenice--venice-audio-transcription\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: venice--venice-audio-transcription\ndescription: >-\n  Transcribe audio files to text via POST \u002Faudio\u002Ftranscriptions. Covers supported models (Parakeet, Whisper, Wizper, Scribe, xAI STT), supported formats (wav\u002Fflac\u002Fm4a\u002Faac\u002Fmp4\u002Fmp3\u002Fogg\u002Fwebm), response formats (json\u002Ftext), timestamps, and language hints. OpenAI-compatible multipart.\norigin: \"github.com\u002Fveniceai\u002Fskills (skill: venice-audio-transcription)\"\nlicense: MIT\nversion: \"1.0.0\"\ncompatibility: \"yana-ai >= 0.14.0\"\n---\n\n# Venice Transcription (`\u002Faudio\u002Ftranscriptions`)\n\n`POST \u002Fapi\u002Fv1\u002Faudio\u002Ftranscriptions` takes an audio file and returns text. It's OpenAI-compatible with `multipart\u002Fform-data` — the OpenAI SDK's `audio.transcriptions.create()` works unchanged.\n\n## Use when\n\n- You need STT (speech-to-text) for voice notes, meetings, podcasts, short audio.\n- You need timestamps for subtitles \u002F chapters.\n- You want to pick between fast local-style models (Parakeet) and large multilingual ones (Whisper, Wizper, Scribe).\n\nFor long video \u002F YouTube transcription, see [`venice-video`](..\u002Fvenice-video\u002FSKILL.md)'s `\u002Fvideo\u002Ftranscriptions` (takes a public video URL directly).\n\n## Minimal request\n\n```bash\ncurl https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1\u002Faudio\u002Ftranscriptions \\\n  -H \"Authorization: Bearer $VENICE_API_KEY\" \\\n  -F \"file=@.\u002Fmeeting.m4a\" \\\n  -F \"model=nvidia\u002Fparakeet-tdt-0.6b-v3\" \\\n  -F \"response_format=json\" \\\n  -F \"timestamps=false\"\n```\n\n```json\n{ \"text\": \"Alright everyone, let's kick off the meeting...\" }\n```\n\nWith `timestamps=true`, `json` format also returns segment\u002Fword timings (schema is model-specific).\n\n## Request (`multipart\u002Fform-data`)\n\n| Field | Type | Default | Notes |\n|---|---|---|---|\n| `file` | binary | — | **Required.** Audio file. Supported: `wav`, `wave`, `flac`, `m4a`, `aac`, `mp4`, `mp3`, `ogg`, `webm`. Base64 is **not** accepted — upload as a real file. |\n| `model` | enum | `nvidia\u002Fparakeet-tdt-0.6b-v3` | See models below. |\n| `response_format` | `json` \u002F `text` | `json` | `text` returns `text\u002Fplain` body. |\n| `timestamps` | bool | `false` | Include segment\u002Fword timestamps (JSON only). |\n| `language` | string | — | ISO 639-1 hint (e.g. `en`, `ja`). Only Whisper-family models honor it; others auto-detect. |\n\n## Models\n\n| Model ID | Notes |\n|---|---|\n| `nvidia\u002Fparakeet-tdt-0.6b-v3` | Default. Fast, English-first, great for real-time-ish flows. |\n| `openai\u002Fwhisper-large-v3` | Large multilingual, honors `language` hint. |\n| `fal-ai\u002Fwizper` | Whisper variant, competitive on quality\u002Flatency tradeoff. |\n| `elevenlabs\u002Fscribe-v2` | ElevenLabs Scribe, strong on noisy audio. |\n| `stt-xai-v1` | xAI Speech-to-Text. |\n\n`GET \u002Fmodels?type=asr` returns the current catalog. ASR pricing is `pricing.per_audio_second.usd` — cost scales with audio duration.\n\n## OpenAI SDK\n\n```ts\nimport OpenAI from 'openai'\nimport fs from 'node:fs'\n\nconst client = new OpenAI({\n  apiKey: process.env.VENICE_API_KEY,\n  baseURL: 'https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1',\n})\n\nconst out = await client.audio.transcriptions.create({\n  file: fs.createReadStream('meeting.m4a'),\n  model: 'openai\u002Fwhisper-large-v3',\n  response_format: 'json',\n  language: 'en',\n  \u002F\u002F @ts-expect-error — Venice-specific extra, passes through multipart\n  timestamps: true,\n})\n\nconsole.log(out.text)\n```\n\n## Batch \u002F long files\n\nVenice doesn't expose native chunking. For files > ~30 min, split client-side on silence with `ffmpeg` or `pydub`, transcribe each chunk, then concatenate with offset timestamps.\n\n```bash\nffmpeg -i long.mp3 -f segment -segment_time 600 -c copy chunk_%03d.mp3\n```\n\n## Errors\n\n| Code | Meaning |\n|---|---|\n| `400` | Bad params, unsupported audio format, empty file, or **file larger than 25 MB** (this endpoint returns `400` with `\"Maximum size is 25MB\"`, not `413`). |\n| `401` | Auth \u002F Pro-only. |\n| `402` | Insufficient balance. |\n| `415` | Wrong `Content-Type` — must be `multipart\u002Fform-data`. |\n| `422` | Validation \u002F upstream ASR error (e.g. zero-length audio, upstream provider 422). Not a \"content policy\" code on this path. |\n| `429` | Rate limited. |\n| `500` \u002F `503` | Transient; retry with jitter. |\n\n## Gotchas\n\n- `file` must be uploaded as a real multipart file part. JSON + base64 is **not** supported here.\n- Timestamps are only surfaced in the JSON response shapes (`json`, `verbose_json`, `srt`, `vtt`). With `response_format: text` the handler returns a plain `text\u002Fplain` body containing just the transcript — you'll lose any timestamp data, so pick `verbose_json` \u002F `srt` \u002F `vtt` when you need timings.\n- `language` is Whisper-specific. Parakeet \u002F Scribe ignore it and auto-detect.\n- Peak concurrency limits apply — on `429`, back off; big batches should throttle to ~5 parallel requests.\n- Content-policy rejection on the transcript is returned as `422` with an error string; it does not surface `suggested_prompt` on this path.\n","# Venice Transcription (`\u002Faudio\u002Ftranscriptions`)\n\n`POST \u002Fapi\u002Fv1\u002Faudio\u002Ftranscriptions` takes an audio file and returns text. It's OpenAI-compatible with `multipart\u002Fform-data` — the OpenAI SDK's `audio.transcriptions.create()` works unchanged.\n\n## Use when\n\n- You need STT (speech-to-text) for voice notes, meetings, podcasts, short audio.\n- You need timestamps for subtitles \u002F chapters.\n- You want to pick between fast local-style models (Parakeet) and large multilingual ones (Whisper, Wizper, Scribe).\n\nFor long video \u002F YouTube transcription, see [`venice-video`](..\u002Fvenice-video\u002FSKILL.md)'s `\u002Fvideo\u002Ftranscriptions` (takes a public video URL directly).\n\n## Minimal request\n\n```bash\ncurl https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1\u002Faudio\u002Ftranscriptions \\\n  -H \"Authorization: Bearer $VENICE_API_KEY\" \\\n  -F \"file=@.\u002Fmeeting.m4a\" \\\n  -F \"model=nvidia\u002Fparakeet-tdt-0.6b-v3\" \\\n  -F \"response_format=json\" \\\n  -F \"timestamps=false\"\n```\n\n```json\n{ \"text\": \"Alright everyone, let's kick off the meeting...\" }\n```\n\nWith `timestamps=true`, `json` format also returns segment\u002Fword timings (schema is model-specific).\n\n## Request (`multipart\u002Fform-data`)\n\n| Field | Type | Default | Notes |\n|---|---|---|---|\n| `file` | binary | — | **Required.** Audio file. Supported: `wav`, `wave`, `flac`, `m4a`, `aac`, `mp4`, `mp3`, `ogg`, `webm`. Base64 is **not** accepted — upload as a real file. |\n| `model` | enum | `nvidia\u002Fparakeet-tdt-0.6b-v3` | See models below. |\n| `response_format` | `json` \u002F `text` | `json` | `text` returns `text\u002Fplain` body. |\n| `timestamps` | bool | `false` | Include segment\u002Fword timestamps (JSON only). |\n| `language` | string | — | ISO 639-1 hint (e.g. `en`, `ja`). Only Whisper-family models honor it; others auto-detect. |\n\n## Models\n\n| Model ID | Notes |\n|---|---|\n| `nvidia\u002Fparakeet-tdt-0.6b-v3` | Default. Fast, English-first, great for real-time-ish flows. |\n| `openai\u002Fwhisper-large-v3` | Large multilingual, honors `language` hint. |\n| `fal-ai\u002Fwizper` | Whisper variant, competitive on quality\u002Flatency tradeoff. |\n| `elevenlabs\u002Fscribe-v2` | ElevenLabs Scribe, strong on noisy audio. |\n| `stt-xai-v1` | xAI Speech-to-Text. |\n\n`GET \u002Fmodels?type=asr` returns the current catalog. ASR pricing is `pricing.per_audio_second.usd` — cost scales with audio duration.\n\n## OpenAI SDK\n\n```ts\nimport OpenAI from 'openai'\nimport fs from 'node:fs'\n\nconst client = new OpenAI({\n  apiKey: process.env.VENICE_API_KEY,\n  baseURL: 'https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1',\n})\n\nconst out = await client.audio.transcriptions.create({\n  file: fs.createReadStream('meeting.m4a'),\n  model: 'openai\u002Fwhisper-large-v3',\n  response_format: 'json',\n  language: 'en',\n  \u002F\u002F @ts-expect-error — Venice-specific extra, passes through multipart\n  timestamps: true,\n})\n\nconsole.log(out.text)\n```\n\n## Batch \u002F long files\n\nVenice doesn't expose native chunking. For files > ~30 min, split client-side on silence with `ffmpeg` or `pydub`, transcribe each chunk, then concatenate with offset timestamps.\n\n```bash\nffmpeg -i long.mp3 -f segment -segment_time 600 -c copy chunk_%03d.mp3\n```\n\n## Errors\n\n| Code | Meaning |\n|---|---|\n| `400` | Bad params, unsupported audio format, empty file, or **file larger than 25 MB** (this endpoint returns `400` with `\"Maximum size is 25MB\"`, not `413`). |\n| `401` | Auth \u002F Pro-only. |\n| `402` | Insufficient balance. |\n| `415` | Wrong `Content-Type` — must be `multipart\u002Fform-data`. |\n| `422` | Validation \u002F upstream ASR error (e.g. zero-length audio, upstream provider 422). Not a \"content policy\" code on this path. |\n| `429` | Rate limited. |\n| `500` \u002F `503` | Transient; retry with jitter. |\n\n## Gotchas\n\n- `file` must be uploaded as a real multipart file part. JSON + base64 is **not** supported here.\n- Timestamps are only surfaced in the JSON response shapes (`json`, `verbose_json`, `srt`, `vtt`). With `response_format: text` the handler returns a plain `text\u002Fplain` body containing just the transcript — you'll lose any timestamp data, so pick `verbose_json` \u002F `srt` \u002F `vtt` when you need timings.\n- `language` is Whisper-specific. Parakeet \u002F Scribe ignore it and auto-detect.\n- Peak concurrency limits apply — on `429`, back off; big batches should throttle to ~5 parallel requests.\n- Content-policy rejection on the transcript is returned as `422` with an error string; it does not surface `suggested_prompt` on this path.\n",{"name":5,"origin":24,"license":15,"version":25,"description":7,"compatibility":26},"github.com\u002Fveniceai\u002Fskills (skill: venice-audio-transcription)","1.0.0","yana-ai >= 0.14.0","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fvenice--venice-audio-transcription",1,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-11 23:56:26",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill venice--venice-audio-transcription",{"tool":43,"command":44},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill venice--venice-audio-transcription",{"tool":46,"command":47},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill venice--venice-audio-transcription","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fvenice--venice-audio-transcription\u002FSKILL.md",true,[],[52,59,67,75,83,91],{"skillNo":53,"name":54,"nameZh":6,"description":55,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":56,"categories":57,"keywords":58,"publishedAt":19},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":6,"sourceHtmlUrl":64,"categories":65,"keywords":66,"publishedAt":19},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. 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Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":87,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":63,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]