interview-ingest

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