[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M72A8D4MT9VP6NP45QB7W6RF":3,"skill-same:01M72A8D4MT9VP6NP45QB7W6RF":90},{"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":69,"commitSha":70,"skillDirectory":71,"forksCount":72,"repositoryHtmlUrl":73,"repositoryHomepageUrl":6,"ownerHtmlUrl":74,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":75,"install":76,"resources":89},"01M72A8D4MT9VP6NP45QB7W6RF","Transcription Automation",null,"Automate audio\u002Fvideo transcription, meeting notes, subtitle generation, and content processing","claude-office-skills\u002Fskills","claude-office-skills","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F258027507?v=4","01M729XQC9FBRQ54AQZS0KY3ZK","AGI Lab","01M729XQCYW6WF9GY762P9RNQJ",575,"MIT","https:\u002F\u002Fgithub.com\u002Fclaude-office-skills\u002Fskills\u002Fblob\u002Fmain\u002Ftranscription-automation\u002FSKILL.md",[],[],"2026-11-10 18:39:20",false,"---\nname: Transcription Automation\ndescription: Automate audio\u002Fvideo transcription, meeting notes, subtitle generation, and content processing\nversion: 1.0.0\nauthor: Claude Office Skills\ncategory: multimedia\ntags:\n  - transcription\n  - audio\n  - video\n  - meetings\n  - subtitles\ndepartment: content\nmodels:\n  - claude-3-opus\n  - claude-3-sonnet\n  - gpt-4\n  - whisper\nmcp:\n  server: media-mcp\n  tools:\n    - whisper_transcribe\n    - assembly_ai\n    - deepgram_api\n    - subtitle_generate\ncapabilities:\n  - Speech-to-text conversion\n  - Meeting transcription\n  - Subtitle generation\n  - Speaker diarization\ninput:\n  - Audio files\n  - Video files\n  - Live streams\n  - Meeting recordings\noutput:\n  - Text transcripts\n  - SRT\u002FVTT subtitles\n  - Meeting notes\n  - Searchable archives\nlanguages:\n  - en\n  - zh\n  - multi\nrelated_skills:\n  - youtube-automation\n  - podcast-automation\n  - meeting-notes\n---\n\n# Transcription Automation\n\nComprehensive skill for automating audio\u002Fvideo transcription and content processing.\n\n## Core Workflows\n\n### 1. Transcription Pipeline\n\n```\nTRANSCRIPTION FLOW:\n┌─────────────────┐\n│  Audio\u002FVideo    │\n│     Input       │\n└────────┬────────┘\n         ▼\n┌─────────────────┐\n│  Pre-Processing │\n│  - Convert      │\n│  - Enhance      │\n│  - Split        │\n└────────┬────────┘\n         ▼\n┌─────────────────┐\n│  Transcription  │\n│  - STT Engine   │\n│  - Diarization  │\n└────────┬────────┘\n         ▼\n┌─────────────────┐\n│ Post-Processing │\n│  - Format       │\n│  - Timestamps   │\n│  - Speakers     │\n└────────┬────────┘\n         ▼\n┌─────────────────┐\n│     Output      │\n│  - Text\u002FSRT\u002FVTT │\n│  - Summary      │\n└─────────────────┘\n```\n\n### 2. Transcription Configuration\n\n```yaml\ntranscription_config:\n  engine: whisper  # whisper, assembly_ai, deepgram\n  \n  audio_settings:\n    sample_rate: 16000\n    channels: mono\n    format: wav\n    \n  transcription:\n    language: auto  # or specific: en, zh, es\n    model: large  # tiny, base, small, medium, large\n    task: transcribe  # transcribe or translate\n    \n  features:\n    speaker_diarization: true\n    word_timestamps: true\n    punctuation: true\n    profanity_filter: false\n    \n  output:\n    formats:\n      - txt\n      - srt\n      - vtt\n      - json\n    include_confidence: true\n    include_timestamps: true\n```\n\n## Meeting Transcription\n\n### Meeting Notes Template\n\n```yaml\nmeeting_transcript:\n  metadata:\n    title: \"{{meeting_title}}\"\n    date: \"{{date}}\"\n    duration: \"{{duration}}\"\n    attendees: \"{{speakers}}\"\n    \n  output_template: |\n    # {{title}}\n    \n    **Date:** {{date}}\n    **Duration:** {{duration}}\n    **Attendees:** {{attendees}}\n    \n    ## Summary\n    {{ai_summary}}\n    \n    ## Key Points\n    {{#each key_points}}\n    - {{this}}\n    {{\u002Feach}}\n    \n    ## Action Items\n    {{#each action_items}}\n    - [ ] {{task}} - @{{assignee}} - Due: {{due_date}}\n    {{\u002Feach}}\n    \n    ## Full Transcript\n    {{#each segments}}\n    **[{{timestamp}}] {{speaker}}:** {{text}}\n    \n    {{\u002Feach}}\n```\n\n### Speaker Diarization\n\n```yaml\ndiarization_config:\n  min_speakers: 2\n  max_speakers: 10\n  \n  speaker_labels:\n    - name: \"Speaker 1\"\n      voice_sample: \"sample_1.wav\"  # Optional\n    - name: \"Speaker 2\"\n      voice_sample: \"sample_2.wav\"\n      \n  output_format:\n    speaker_prefix: true\n    speaker_timestamps: true\n    \n  example_output: |\n    [00:00:05] SPEAKER_1: Welcome everyone to today's meeting.\n    [00:00:12] SPEAKER_2: Thanks for having us.\n    [00:00:18] SPEAKER_1: Let's start with the agenda.\n```\n\n## Subtitle Generation\n\n### SRT Format\n\n```yaml\nsubtitle_config:\n  format: srt\n  \n  timing:\n    max_duration: 7  # seconds per subtitle\n    min_gap: 0.1     # seconds between subtitles\n    chars_per_line: 42\n    max_lines: 2\n    \n  style:\n    case: sentence  # sentence, upper, lower\n    numbers: words  # words, digits\n    \n  example_output: |\n    1\n    00:00:05,000 --> 00:00:08,500\n    Welcome to today's presentation\n    about transcription automation.\n    \n    2\n    00:00:09,000 --> 00:00:12,000\n    Let me start by explaining\n    the basic concepts.\n```\n\n### VTT Format\n\n```yaml\nvtt_config:\n  format: vtt\n  \n  features:\n    cue_settings: true\n    styling: true\n    \n  example_output: |\n    WEBVTT\n    \n    00:00:05.000 --> 00:00:08.500 align:center\n    Welcome to today's presentation\n    about transcription automation.\n    \n    00:00:09.000 --> 00:00:12.000 align:center\n    \u003Cv Speaker 1>Let me start by explaining\n    the basic concepts.\n```\n\n## Integration Workflows\n\n### Zoom Integration\n\n```yaml\nzoom_transcription:\n  trigger:\n    event: recording_completed\n    \n  workflow:\n    - step: download_recording\n      source: zoom_cloud\n      \n    - step: transcribe\n      engine: whisper\n      language: auto\n      \n    - step: diarize\n      identify_speakers: true\n      \n    - step: generate_notes\n      template: meeting_notes\n      include_summary: true\n      extract_action_items: true\n      \n    - step: distribute\n      destinations:\n        - notion_page\n        - slack_channel\n        - email_attendees\n```\n\n### YouTube Integration\n\n```yaml\nyoutube_subtitles:\n  trigger:\n    event: video_uploaded\n    \n  workflow:\n    - step: download_audio\n      source: youtube_video\n      \n    - step: transcribe\n      engine: whisper\n      task: transcribe\n      \n    - step: generate_subtitles\n      formats: [srt, vtt]\n      \n    - step: translate\n      target_languages: [es, zh, ja, de, fr]\n      \n    - step: upload_subtitles\n      destination: youtube\n      as_cc: true\n```\n\n### Podcast Processing\n\n```yaml\npodcast_workflow:\n  input:\n    source: rss_feed\n    format: audio\u002Fmp3\n    \n  processing:\n    - transcribe:\n        engine: whisper\n        model: large\n        \n    - generate_chapters:\n        detect_topics: true\n        min_duration: 60  # seconds\n        \n    - create_show_notes:\n        summarize: true\n        extract_links: true\n        highlight_quotes: true\n        \n    - create_searchable_index:\n        full_text: true\n        timestamps: true\n        \n  output:\n    - transcript_txt\n    - chapters_json\n    - show_notes_md\n    - search_index\n```\n\n## Language Support\n\n### Multi-Language Transcription\n\n```yaml\nmultilingual:\n  auto_detect: true\n  \n  supported_languages:\n    - code: en\n      name: English\n      model: large\n      \n    - code: zh\n      name: Chinese\n      model: large\n      \n    - code: es\n      name: Spanish\n      model: large\n      \n    - code: ja\n      name: Japanese\n      model: medium\n      \n  translation:\n    enabled: true\n    target: en\n    preserve_original: true\n```\n\n### Code-Switching\n\n```yaml\ncode_switching:\n  enabled: true\n  primary_language: en\n  secondary_languages: [zh, es]\n  \n  output: |\n    [00:01:23] The next topic is about 人工智能,\n    which has been muy importante in recent years.\n    \n  handling:\n    detect_language_per_segment: true\n    tag_language_switches: true\n```\n\n## Quality Enhancement\n\n### Post-Processing\n\n```yaml\npost_processing:\n  text_cleanup:\n    - remove_filler_words: [\"um\", \"uh\", \"like\"]\n    - fix_common_errors: true\n    - normalize_numbers: true\n    \n  formatting:\n    - add_punctuation: true\n    - capitalize_sentences: true\n    - paragraph_breaks: true\n    \n  speaker_attribution:\n    - merge_short_segments: true\n    - min_segment_duration: 1.0\n    \n  output_enhancement:\n    - add_timestamps: true\n    - highlight_keywords: true\n    - generate_summary: true\n```\n\n### Accuracy Metrics\n\n```\nTRANSCRIPTION QUALITY REPORT\n═══════════════════════════════════════\n\nFile: meeting_2024_01_15.mp3\nDuration: 45:32\nEngine: Whisper Large\n\nMETRICS:\nWord Error Rate (WER):  4.2%\nCharacter Error Rate:   2.8%\nConfidence Score:       0.94\n\nSPEAKER DIARIZATION:\nSpeakers Detected: 4\nDiarization Accuracy: 91%\n\nPROCESSING TIME:\nTotal: 8m 23s\nReal-time Factor: 0.18x\n\nDETECTED ISSUES:\n• Low confidence at 12:34 (background noise)\n• Overlapping speech at 23:45\n• Unknown speaker at 34:12\n```\n\n## API Examples\n\n### OpenAI Whisper\n\n```python\nimport openai\n\n# Transcribe audio\nwith open(\"meeting.mp3\", \"rb\") as audio_file:\n    transcript = openai.Audio.transcribe(\n        model=\"whisper-1\",\n        file=audio_file,\n        response_format=\"verbose_json\",\n        timestamp_granularities=[\"word\", \"segment\"]\n    )\n\n# Access results\nfor segment in transcript.segments:\n    print(f\"[{segment.start:.2f}] {segment.text}\")\n```\n\n### AssemblyAI\n\n```python\nimport assemblyai as aai\n\ntranscriber = aai.Transcriber()\n\nconfig = aai.TranscriptionConfig(\n    speaker_labels=True,\n    auto_chapters=True,\n    entity_detection=True\n)\n\ntranscript = transcriber.transcribe(\n    \"https:\u002F\u002Fexample.com\u002Fmeeting.mp3\",\n    config=config\n)\n\nfor utterance in transcript.utterances:\n    print(f\"Speaker {utterance.speaker}: {utterance.text}\")\n```\n\n## Best Practices\n\n1. **Quality Audio**: Clean input = better output\n2. **Choose Right Model**: Balance speed vs accuracy\n3. **Use Diarization**: Identify speakers clearly\n4. **Post-Process**: Clean up automated output\n5. **Verify Critical Content**: Human review important\n6. **Consider Privacy**: Handle sensitive content\n7. **Store Efficiently**: Compress and index\n8. **Provide Context**: Vocabulary hints help\n","# Transcription Automation\n\nComprehensive skill for automating audio\u002Fvideo transcription and content processing.\n\n## Core Workflows\n\n### 1. Transcription Pipeline\n\n```\nTRANSCRIPTION FLOW:\n┌─────────────────┐\n│  Audio\u002FVideo    │\n│     Input       │\n└────────┬────────┘\n         ▼\n┌─────────────────┐\n│  Pre-Processing │\n│  - Convert      │\n│  - Enhance      │\n│  - Split        │\n└────────┬────────┘\n         ▼\n┌─────────────────┐\n│  Transcription  │\n│  - STT Engine   │\n│  - Diarization  │\n└────────┬────────┘\n         ▼\n┌─────────────────┐\n│ Post-Processing │\n│  - Format       │\n│  - Timestamps   │\n│  - Speakers     │\n└────────┬────────┘\n         ▼\n┌─────────────────┐\n│     Output      │\n│  - Text\u002FSRT\u002FVTT │\n│  - Summary      │\n└─────────────────┘\n```\n\n### 2. Transcription Configuration\n\n```yaml\ntranscription_config:\n  engine: whisper  # whisper, assembly_ai, deepgram\n  \n  audio_settings:\n    sample_rate: 16000\n    channels: mono\n    format: wav\n    \n  transcription:\n    language: auto  # or specific: en, zh, es\n    model: large  # tiny, base, small, medium, large\n    task: transcribe  # transcribe or translate\n    \n  features:\n    speaker_diarization: true\n    word_timestamps: true\n    punctuation: true\n    profanity_filter: false\n    \n  output:\n    formats:\n      - txt\n      - srt\n      - vtt\n      - json\n    include_confidence: true\n    include_timestamps: true\n```\n\n## Meeting Transcription\n\n### Meeting Notes Template\n\n```yaml\nmeeting_transcript:\n  metadata:\n    title: \"{{meeting_title}}\"\n    date: \"{{date}}\"\n    duration: \"{{duration}}\"\n    attendees: \"{{speakers}}\"\n    \n  output_template: |\n    # {{title}}\n    \n    **Date:** {{date}}\n    **Duration:** {{duration}}\n    **Attendees:** {{attendees}}\n    \n    ## Summary\n    {{ai_summary}}\n    \n    ## Key Points\n    {{#each key_points}}\n    - {{this}}\n    {{\u002Feach}}\n    \n    ## Action Items\n    {{#each action_items}}\n    - [ ] {{task}} - @{{assignee}} - Due: {{due_date}}\n    {{\u002Feach}}\n    \n    ## Full Transcript\n    {{#each segments}}\n    **[{{timestamp}}] {{speaker}}:** {{text}}\n    \n    {{\u002Feach}}\n```\n\n### Speaker Diarization\n\n```yaml\ndiarization_config:\n  min_speakers: 2\n  max_speakers: 10\n  \n  speaker_labels:\n    - name: \"Speaker 1\"\n      voice_sample: \"sample_1.wav\"  # Optional\n    - name: \"Speaker 2\"\n      voice_sample: \"sample_2.wav\"\n      \n  output_format:\n    speaker_prefix: true\n    speaker_timestamps: true\n    \n  example_output: |\n    [00:00:05] SPEAKER_1: Welcome everyone to today's meeting.\n    [00:00:12] SPEAKER_2: Thanks for having us.\n    [00:00:18] SPEAKER_1: Let's start with the agenda.\n```\n\n## Subtitle Generation\n\n### SRT Format\n\n```yaml\nsubtitle_config:\n  format: srt\n  \n  timing:\n    max_duration: 7  # seconds per subtitle\n    min_gap: 0.1     # seconds between subtitles\n    chars_per_line: 42\n    max_lines: 2\n    \n  style:\n    case: sentence  # sentence, upper, lower\n    numbers: words  # words, digits\n    \n  example_output: |\n    1\n    00:00:05,000 --> 00:00:08,500\n    Welcome to today's presentation\n    about transcription automation.\n    \n    2\n    00:00:09,000 --> 00:00:12,000\n    Let me start by explaining\n    the basic concepts.\n```\n\n### VTT Format\n\n```yaml\nvtt_config:\n  format: vtt\n  \n  features:\n    cue_settings: true\n    styling: true\n    \n  example_output: |\n    WEBVTT\n    \n    00:00:05.000 --> 00:00:08.500 align:center\n    Welcome to today's presentation\n    about transcription automation.\n    \n    00:00:09.000 --> 00:00:12.000 align:center\n    \u003Cv Speaker 1>Let me start by explaining\n    the basic concepts.\n```\n\n## Integration Workflows\n\n### Zoom Integration\n\n```yaml\nzoom_transcription:\n  trigger:\n    event: recording_completed\n    \n  workflow:\n    - step: download_recording\n      source: zoom_cloud\n      \n    - step: transcribe\n      engine: whisper\n      language: auto\n      \n    - step: diarize\n      identify_speakers: true\n      \n    - step: generate_notes\n      template: meeting_notes\n      include_summary: true\n      extract_action_items: true\n      \n    - step: distribute\n      destinations:\n        - notion_page\n        - slack_channel\n        - email_attendees\n```\n\n### YouTube Integration\n\n```yaml\nyoutube_subtitles:\n  trigger:\n    event: video_uploaded\n    \n  workflow:\n    - step: download_audio\n      source: youtube_video\n      \n    - step: transcribe\n      engine: whisper\n      task: transcribe\n      \n    - step: generate_subtitles\n      formats: [srt, vtt]\n      \n    - step: translate\n      target_languages: [es, zh, ja, de, fr]\n      \n    - step: upload_subtitles\n      destination: youtube\n      as_cc: true\n```\n\n### Podcast Processing\n\n```yaml\npodcast_workflow:\n  input:\n    source: rss_feed\n    format: audio\u002Fmp3\n    \n  processing:\n    - transcribe:\n        engine: whisper\n        model: large\n        \n    - generate_chapters:\n        detect_topics: true\n        min_duration: 60  # seconds\n        \n    - create_show_notes:\n        summarize: true\n        extract_links: true\n        highlight_quotes: true\n        \n    - create_searchable_index:\n        full_text: true\n        timestamps: true\n        \n  output:\n    - transcript_txt\n    - chapters_json\n    - show_notes_md\n    - search_index\n```\n\n## Language Support\n\n### Multi-Language Transcription\n\n```yaml\nmultilingual:\n  auto_detect: true\n  \n  supported_languages:\n    - code: en\n      name: English\n      model: large\n      \n    - code: zh\n      name: Chinese\n      model: large\n      \n    - code: es\n      name: Spanish\n      model: large\n      \n    - code: ja\n      name: Japanese\n      model: medium\n      \n  translation:\n    enabled: true\n    target: en\n    preserve_original: true\n```\n\n### Code-Switching\n\n```yaml\ncode_switching:\n  enabled: true\n  primary_language: en\n  secondary_languages: [zh, es]\n  \n  output: |\n    [00:01:23] The next topic is about 人工智能,\n    which has been muy importante in recent years.\n    \n  handling:\n    detect_language_per_segment: true\n    tag_language_switches: true\n```\n\n## Quality Enhancement\n\n### Post-Processing\n\n```yaml\npost_processing:\n  text_cleanup:\n    - remove_filler_words: [\"um\", \"uh\", \"like\"]\n    - fix_common_errors: true\n    - normalize_numbers: true\n    \n  formatting:\n    - add_punctuation: true\n    - capitalize_sentences: true\n    - paragraph_breaks: true\n    \n  speaker_attribution:\n    - merge_short_segments: true\n    - min_segment_duration: 1.0\n    \n  output_enhancement:\n    - add_timestamps: true\n    - highlight_keywords: true\n    - generate_summary: true\n```\n\n### Accuracy Metrics\n\n```\nTRANSCRIPTION QUALITY REPORT\n═══════════════════════════════════════\n\nFile: meeting_2024_01_15.mp3\nDuration: 45:32\nEngine: Whisper Large\n\nMETRICS:\nWord Error Rate (WER):  4.2%\nCharacter Error Rate:   2.8%\nConfidence Score:       0.94\n\nSPEAKER DIARIZATION:\nSpeakers Detected: 4\nDiarization Accuracy: 91%\n\nPROCESSING TIME:\nTotal: 8m 23s\nReal-time Factor: 0.18x\n\nDETECTED ISSUES:\n• Low confidence at 12:34 (background noise)\n• Overlapping speech at 23:45\n• Unknown speaker at 34:12\n```\n\n## API Examples\n\n### OpenAI Whisper\n\n```python\nimport openai\n\n# Transcribe audio\nwith open(\"meeting.mp3\", \"rb\") as audio_file:\n    transcript = openai.Audio.transcribe(\n        model=\"whisper-1\",\n        file=audio_file,\n        response_format=\"verbose_json\",\n        timestamp_granularities=[\"word\", \"segment\"]\n    )\n\n# Access results\nfor segment in transcript.segments:\n    print(f\"[{segment.start:.2f}] {segment.text}\")\n```\n\n### AssemblyAI\n\n```python\nimport assemblyai as aai\n\ntranscriber = aai.Transcriber()\n\nconfig = aai.TranscriptionConfig(\n    speaker_labels=True,\n    auto_chapters=True,\n    entity_detection=True\n)\n\ntranscript = transcriber.transcribe(\n    \"https:\u002F\u002Fexample.com\u002Fmeeting.mp3\",\n    config=config\n)\n\nfor utterance in transcript.utterances:\n    print(f\"Speaker {utterance.speaker}: {utterance.text}\")\n```\n\n## Best Practices\n\n1. **Quality Audio**: Clean input = better output\n2. **Choose Right Model**: Balance speed vs accuracy\n3. **Use Diarization**: Identify speakers clearly\n4. **Post-Process**: Clean up automated output\n5. **Verify Critical Content**: Human review important\n6. **Consider Privacy**: Handle sensitive content\n7. **Store Efficiently**: Compress and index\n8. **Provide Context**: Vocabulary hints help\n",{"mcp":24,"name":5,"tags":31,"input":37,"author":42,"models":43,"output":48,"version":53,"category":54,"languages":55,"department":59,"description":7,"capabilities":60,"related_skills":65},{"tools":25,"server":30},[26,27,28,29],"whisper_transcribe","assembly_ai","deepgram_api","subtitle_generate","media-mcp",[32,33,34,35,36],"transcription","audio","video","meetings","subtitles",[38,39,40,41],"Audio files","Video files","Live streams","Meeting recordings","Claude Office Skills",[44,45,46,47],"claude-3-opus","claude-3-sonnet","gpt-4","whisper",[49,50,51,52],"Text transcripts","SRT\u002FVTT subtitles","Meeting notes","Searchable archives","1.0.0","multimedia",[56,57,58],"en","zh","multi","content",[61,62,63,64],"Speech-to-text conversion","Meeting transcription","Subtitle generation","Speaker diarization",[66,67,68],"youtube-automation","podcast-automation","meeting-notes","main","9c4c7d5cd2813a8936bf2c9fdb174ea883b85a11","transcription-automation",105,"https:\u002F\u002Fgithub.com\u002Fclaude-office-skills\u002Fskills","https:\u002F\u002Fgithub.com\u002Fclaude-office-skills","2026-01-31 22:03:56",{"source":8,"skillName":5,"commands":77,"prompt":87,"zipAvailable":88},[78,81,84],{"tool":79,"command":80},"npx","npx skills add claude-office-skills\u002Fskills --skill \"Transcription Automation\"",{"tool":82,"command":83},"bunx","bunx skills add claude-office-skills\u002Fskills --skill \"Transcription Automation\"",{"tool":85,"command":86},"pnpm","pnpm dlx skills add claude-office-skills\u002Fskills --skill \"Transcription Automation\"","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fclaude-office-skills\u002Fskills\u002Fblob\u002Fmain\u002Ftranscription-automation\u002FSKILL.md",true,[],[91,98,105,112,119,126],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":19},"01M72A0X7X4TWS4ZWTEK8TJEC8","devops-automation","DevOps and IT Ops automation - 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