Skill: artofcoding-by-hanif/communication-dna (version 1.0.1)

--- name: communication-dna description: 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. --- # Communication DNA 🧬 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: ```bash cd <skill-dir>/scripts python3 db.py # Creates communication_dna.db with all tables + FTS5 ``` ## Core Workflow ### 1. Ingest Transcriptions ```bash 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 ```bash 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 ```bash 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 ```bash 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)


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