Communication DNA

Analyze speech and meeting transcriptions to build communication profiles — vocabulary fingerprints, filler word detection, speech patterns, commitment extraction, sentiment arcs, topic detection, and speaker comparison. Use when asked to analyze transcripts, profile speakers, compare communication styles, ingest meeting recordings/transcriptions, find commitments/promises in meetings, or understand someone's speaking patterns. Supports TXT, SRT, VTT, and JSON transcript formats. Integrates with Personal CRM and Knowledge Base.

Analyze transcriptions to extract communication intelligence — how people speak, what they commit to, how they compare.

Setup

The project lives at communication-dna/ in the workspace. On first use, initialize the DB:

cd <skill-dir>/scripts
python3 db.py  # Creates communication_dna.db with all tables + FTS5

Core Workflow

1. Ingest Transcriptions

python3 dna.py ingest <file> --title "Meeting Name" --date 2026-02-23 --context meeting
python3 dna.py ingest-dir <directory>  # Batch ingest

Supported formats:

• TXT — Auto-detects speaker labels ("John:", "[Alice]", "Speaker 1:")
• SRT — SubRip subtitles with timestamps
• VTT — WebVTT with timestamps
• JSON — Whisper/Otter.ai exports with segments

Speaker matching is fuzzy — reuses existing speakers by name.

2. Analyze Speakers

python3 dna.py analyze <speaker_id>    # Full report
python3 dna.py analyze-all             # All speakers
python3 dna.py fingerprint <speaker_id> # Vocabulary deep dive
python3 dna.py fillers <speaker_id>     # Filler word report
python3 dna.py patterns <speaker_id>    # Speech patterns
python3 dna.py commitments             # All extracted commitments
python3 dna.py sentiment <trans_id>    # Sentiment arc
python3 dna.py topics                  # Top topics
python3 dna.py compare <id1> <id2>    # Side-by-side comparison

3. Speaker Profiles

Auto-generated style tags based on analysis:

• Formal/Casual, Assertive/Cautious, Inquisitive, Filler-heavy/Articulate, Optimistic/Critical, Diverse vocabulary/Repetitive

4. Cross-System Integration

python3 dna.py link-crm               # Auto-link speakers → CRM contacts
python3 dna.py link-kb                 # Cross-reference with Knowledge Base
python3 dna.py push-to-kb <trans_id>  # Push transcription to KB
python3 dna.py cross-search "query"   # Search DNA + KB together

CRM path: ../personal-crm/crm.db | KB path: ../knowledge-base/knowledge.db

5. Web UI

python3 app.py  # Starts on port 5053

Pages: Dashboard, Speakers, Speaker Profile, Transcriptions, Transcription Detail, Compare, Search, Ingest (drag & drop), Integrations.

API endpoints: /api/speakers, /api/speaker/<id>, /api/search, /api/ingest, /api/link-crm, /api/link-kb, /api/cross-search, /api/push-to-kb/<id>

Analysis Capabilities

Analysis	What it extracts
Vocabulary Fingerprint	Word frequency, type-token ratio, sentence length, formality score, unique words
Filler Detection	Rate per 100 words, filler distribution, cross-speaker comparison
Speech Patterns	Bigram/trigram phrases, question rate, hedging vs assertiveness scores
Commitments	"I'll do X", decisions, action items — stored in extractions table
Sentiment	Per-segment scoring (-1 to +1), arcs over time, speaker averages
Topics	TF-IDF extraction per transcription and per speaker

File Reference

All source files are in scripts/:

• db.py — Schema + DB initialization
• ingest.py — Format parsers + speaker detection
• analyze.py — Analysis engine (6 functions + caching)
• wordlists.py — Stop words, fillers, sentiment words, formal/informal lists
• profiles.py — Speaker profile generator + comparison engine
• integrations.py — CRM + KB connectors
• dna.py — CLI (argparse, 17 subcommands)
• app.py — Flask web UI
• templates/ — Jinja2 templates (dark theme, Tailwind CSS)

Dependencies

• Python 3 stdlib (no pip installs for core)
• Flask (for web UI only)
• SQLite FTS5 (built into Python's sqlite3)