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name: interview-ingest description: "This skill should be used when users have audio interview recordings to transcribe, need to convert PDF documents, mentions 'import data', 'transcribe', 'convert', or is starting data preparation for Stage 1 or Stage 2."

interview-ingest

Audio transcription and document conversion for qualitative data import. Converts interview recordings, PDFs, and other formats into analyzable markdown.

When to Use

Use this skill when:

• User has audio interview recordings to transcribe
• User needs to convert PDF documents
• User mentions "import data", "transcribe", "convert"
• Starting data preparation for Stage 1 or Stage 2

MCP Dependencies

This skill operates at two capability tiers:

Tier 1: Best (Requires MinerU API key)

• PDFs: MinerU VLM-powered parsing (90%+ accuracy)
• Tables/Images: Excellent extraction
• Audio: External transcription services recommended
• Best for: Complex academic papers, documents with tables/figures

Tier 2: Manual (No API key required)

• PDFs: Manual conversion (Adobe Acrobat, Google Docs OCR)
• Audio: External transcription services (Otter.ai, Rev.com, YouTube captions)
• Tables/Images: Manual cleanup after conversion
• Best for: Simple documents, researchers without API keys

Checking Tier Availability

# Check for MinerU
[ -n "$MINERU_API_KEY" ] && echo "MinerU available (Tier 1)" || echo "Manual conversion (Tier 2)"

Workflow by Format

Audio Interviews

Recommended transcription services:

• Otter.ai - AI-powered transcription, good for interviews
• Rev.com - Professional human transcription
• YouTube - Upload as unlisted video for auto-captions
• Whisper - Open source, run locally

Best practices:

• Use high-quality recordings when possible
• Review transcripts for accuracy
• Add speaker labels: "Interviewer:" and "Participant:"
• Note timestamps for key passages
• Mark unclear passages with [unclear] or [inaudible]

PDF Documents

Tier 1 (MinerU - recommended for complex PDFs):

# Parse PDF with VLM mode for tables/images
mineru_parse({
  url: "file:///path/to/paper.pdf",
  model: "vlm",
  formula: true,
  table: true
})

Tier 2 (Manual conversion):

• Adobe Acrobat - Export to Word/text
• Google Docs - Open PDF for auto-OCR
• Tesseract OCR - Command-line tool for batch processing

Other Formats

Format	Conversion Method	Notes
DOCX	Copy/paste or Pandoc	Good formatting
PPTX	Export to text	Manual extraction
XLSX	Export to CSV/text	Tables preserved
Images	OCR tools	Tesseract, Google Lens
YouTube	Download captions	Auto-generated transcripts
Web pages	WebFetch or Jina	Full content extraction

Scripts

process-audio.js

Batch process interview recordings.

node skills/interview-ingest/scripts/process-audio.js \
  --project-path /path/to/project \
  --input-dir /path/to/recordings \
  --output-dir stage1-foundation/manual-codes

Output Organization

stage1-foundation/
├── manual-codes/
│   ├── P001-interview.md    # Transcribed interviews
│   ├── P002-interview.md
│   └── ...
├── raw-data/                 # Original files (optional)
│   ├── P001-recording.mp3
│   └── ...
└── data-inventory.json       # Tracks all data sources

data-inventory.json

{
  "documents": [
    {
      "id": "P001",
      "original_file": "P001-recording.mp3",
      "converted_file": "P001-interview.md",
      "format": "audio",
      "conversion_tool": "otter.ai",
      "conversion_date": "2025-01-15",
      "duration_minutes": 45,
      "notes": "Good audio quality"
    }
  ]
}

Quality Considerations

Audio Transcription

• Review all transcripts - AI transcription has errors
• Add speaker labels - "Interviewer:" and "Participant:"
• Note unclear passages - Mark with [unclear] or [inaudible]
• Include timestamps - For later reference to original

PDF Conversion

• Check table accuracy - Complex tables may need manual fixes
• Verify figures - May need manual description
• Review formatting - Headers, lists, emphasis

Integration with Stages

Stage 1 Preparation

1. Transcribe/convert all data sources
2. Organize in stage1-foundation/
3. Create data-inventory.json
4. Begin manual coding on converted files

Stage 2 Processing

1. @dialogical-coder works with markdown files
2. Quotes reference line numbers in converted files
3. Audit trail links back to original sources

Fallback Guidance

If automated transcription unavailable:

Audio Options:

• Otter.ai - Good transcription service
• Rev.com - Professional transcription
• YouTube auto-captions - Upload as unlisted video
• Manual transcription - Time-intensive but accurate

PDF Options:

• Adobe Acrobat - Export to Word/text
• Google Docs - Open PDF, auto-OCR
• Manual copy/paste - For short documents

Related

• MCPs: MinerU (optional), Jina (optional for web content)
• Skills: document-conversion for detailed PDF handling
• Commands: Data import commands