youtube-script-master

What this skill does

# YouTube Script Master

Unified skill for creating **data-driven**, evidence-based cardiology YouTube scripts in Hinglish.

**This skill CONSUMES data from the research-engine Python pipeline.** It does NOT replace that pipeline with manual web searches.

---

## CRITICAL: Run Research Pipeline First

Before writing ANY script, the research-engine should have been run to generate:
- Content calendar with prioritized topics
- Demand analysis (what people want)
- Gap analysis (where opportunities are)
- Narrative analysis (what misinformation to address)

```bash
cd "/Users/shaileshsingh/cowriting system/research-engine"
python run_pipeline.py --quick    # Quick mode (~10 min)
python run_pipeline.py            # Full mode (~30 min)
```

---

## Complete Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│           PHASE 1: DATA COLLECTION (Weekly - Python Pipeline)   │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  channel_scraper.py ──► Scrapes 35+ channels (no API needed)   │
│                         Competition, inspiration, belief-seeders│
│                                                                  │
│  comment_scraper.py ──► Downloads comments from top videos      │
│                         Extracts questions and pain points      │
│                                                                  │
│  OUTPUT: /data/scraped/latest_scrape.json                       │
│          /data/scraped/latest_comments.json                     │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│           PHASE 2: ANALYSIS (Python Pipeline)                    │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  demand_signals.py ──► What topics get views/engagement         │
│                        Question themes, demand scoring           │
│                                                                  │
│  narrative_monitor.py ──► Tracks 8 dangerous narratives:        │
│                           1. LDL skepticism                      │
│                           2. Statin fear                         │
│                           3. Insulin primacy                     │
│                           4. Fasting absolutism                  │
│                           5. Supplement superiority              │
│                           6. Seed oil villain                    │
│                           7. Exercise compensation               │
│                           8. Fear mongering                      │
│                                                                  │
│  gap_finder.py ──► Content opportunities                        │
│                    CORRECTION_OPPORTUNITY (misinformation)       │
│                    LANGUAGE_GAP (English→Hindi needed)           │
│                    DEMAND_GAP (questions but no videos)          │
│                    PROVEN_TOPIC (high views in English)          │
│                                                                  │
│  view_predictor.py ──► ML prediction of video performance       │
│                        Ridge regression + TF-IDF on title        │
│                                                                  │
│  OUTPUT: /output/demand_analysis_*.json                          │
│          /output/narrative_analysis_*.json                       │
│          /output/content_gaps_*.json                             │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│           PHASE 3: PLANNING (Python Pipeline)                    │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  idea_combinator.py ──► Seed ideas (300+) × Modifiers (215+)   │
│                         Filters by pillar, archetype, compat    │
│                         Prioritizes by demand + gap scores       │
│                                                                  │
│  calendar_generator.py ──► 100-day content calendar             │
│                            Mon/Wed/Fri schedule                  │
│                            Balanced by pillar and audience       │
│                                                                  │
│  OUTPUT: /output/calendar.json                                   │
│          /output/100-day-calendar.md (Obsidian-ready)           │
│          /output/idea-briefs/*.md (per-video briefs)            │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│           PHASE 4: KNOWLEDGE BUILDING (Per Video)                │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  knowledge_pipeline.py ──► RAG + PubMed in parallel             │
│    ├─► RAG: Your textbooks/guidelines (AstraDB)                 │
│    └─► PubMed: Latest research (NCBI API)                       │
│                                                                  │
│  OUTPUT: Knowledge brief with citations                          │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│           PHASE 5: SCRIPT WRITING (This Skill - Opus)            │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  INPUTS:                                                         │
│  - calendar.json (which topic, why now)                         │
│  - content_gaps.json (opportunity type)                         │
│  - narrative_analysis.json (if debunk: which narrative)         │
│  - knowledge_brief (evidence for claims)                        │
│                                                                  │
│  APPLY:                                                          │
│  - Hinglish rules (70% Hindi / 30% English)                     │
│  - Script structure (hook → body → CTA)                         │
│  - Debunk protocol (if correction opportunity)                  │
│  - 6-point voice check                                          │
│                                                                  │
│  OUTPUT: Complete 15-30 min script in Hinglish                  │
└─────────────────────────────────────────────────────────────────┘
```

---

## Using Research Engine Outputs

### Step 1: Check the Content Calendar

```bash
# See next 5 topics to create
python calendar_generator.py --show-next 5

# Or read directly
cat /output/calendar.json | head -100
```

Each calendar entry includes:
- `seed_idea` - The topic
- `modifier` - The angle
- `gap_score` - Why this is an opportunity
- `recommended_date` - When to publish

### Step 2: Check If Debunk Needed

```bash
# Get threat ranking of narratives
python analyzer/narrative_monitor.py --threats

# Generate debunk ideas
python analyzer/narrative_monitor.py --debunk

# Get response video ideas for high-reach misinformation
python analyzer/narrative_monitor.py --response
```

**Output includes:**
- Which channels are promoting which narratives
- View counts of misinformation videos
- Pre-generated Hinglish hooks for debunk content
- Matched seed ideas for counter-content

### Step 3: Check Correction Opportunities

```bash
python analyzer/gap_finder.py --corrections
```

Ret