content-angle-ranker

What this skill does

# Content Angle Ranker

You have a keyword. You know the niche. But what specific content should you create?
Which angle, format, and hook will actually perform? This skill answers that question
with data — not gut feeling.

It takes engagement data (from `trending-content-scout` or live research) and ranks
8-12 content angle candidates by a weighted score combining platform fit, competition
level, engagement prediction, and creator fit. The output is a prioritized list with
a clear recommendation and direct handoff to content creation skills.

Think of it as `/plan-ceo-review` from gstack, but for content strategy: "What is
the 10-star version of this content?" — except the answer is backed by engagement data.

## Stage

This skill belongs to Stage S1: Research — but it bridges directly into S2: Content Creation.

## When to Use

- After `trending-content-scout` ran — use its data to pick the best angle
- User has a product/keyword but doesn't know what content to create
- User has multiple content ideas and wants to prioritize by data
- User wants to know: "If I only have time for ONE piece of content, what should it be?"
- Before running any S2 content skill (viral-post-writer, tiktok-script-writer, etc.)

## Input Schema

```yaml
keyword: string               # (required if no scout_data) "AI video tools"
product: object                # (optional) Affiliate product being promoted
  name: string                 # "HeyGen"
  description: string          # What it does
  url: string                  # Product URL or affiliate link
  reward_value: string         # Commission info — never shown in content
platform: string               # (required) Target platform for content creation
                               # "youtube" | "tiktok" | "linkedin" | "x" | "reddit" | "blog"
creator_strengths: string[]    # (optional) What the user is good at
                               # "storytelling" | "technical" | "humor" | "authority" |
                               # "visual" | "data" | "personal_experience"
audience: string               # (optional) Target audience — "beginners", "developers", "small business owners"
time_budget: string            # (optional) "30min" | "2hours" | "1day" — affects difficulty filter
custom_angles: string[]        # (optional) User's own angle ideas to include in ranking
```

**Auto-detection:** If `trending-content-scout` ran earlier in the conversation,
its output is automatically used as the data foundation. No need to pass it explicitly.

## Workflow

### Step 1: Gather Engagement Data

**If `trending-content-scout` output exists in context:**
- Use `pattern_analysis` (winning formats, hooks, engagement benchmarks)
- Use `content_gaps` as angle candidates
- Use `top_content` for competition assessment
- Skip to Step 2

**If no scout data:**
Run a quick scout internally:
1. `web_search "[keyword] site:youtube.com"` → top 10 videos, note formats and view counts
2. `web_search "[keyword] site:tiktok.com"` OR `web_search "[keyword] tiktok viral"` → top TikTok content
3. `web_search "[keyword] site:reddit.com top"` → top Reddit discussions
4. `web_search "[keyword] [platform] best performing"` → meta-analysis of what works
5. Extract: dominant formats, popular hooks, view count ranges, gaps

This takes 30-60 seconds and provides enough signal for angle scoring.

### Step 2: Generate Angle Candidates (8-12)

Generate 8-12 specific content angle candidates. Each angle must be concrete enough
to become a title — not vague ("write about HeyGen") but specific ("HeyGen vs Synthesia:
I tested both for 30 days — honest comparison for solo creators").

**Sources for angles:**

1. **Gap-based angles (from scout data or web_search):**
   - Content gaps: topics nobody has covered well
   - Format gaps: popular topic but missing in a specific format (e.g., comparison exists on YouTube but not TikTok)
   - Audience gaps: existing content targets general audience, specific audience underserved
   - Recency gaps: existing content is outdated, fresh version needed

2. **Pattern-based angles (from winning formats):**
   - Take the winning format and apply it to the keyword
   - Combine the best hook type with the topic
   - Replicate the structure of the highest-engagement content with a fresh perspective

3. **Contrarian angles:**
   - If all content is positive → honest cons angle
   - If all content targets beginners → advanced user angle
   - If all content is listicles → deep single-product dive

4. **User-provided angles (from custom_angles):**
   - Include any angles the user suggested
   - Score them alongside generated candidates — no bias

For each angle, define:

```yaml
Angle:
  title: string               # Specific, could be an actual content title
  angle: string               # Brief description of the angle
  format: string              # "comparison" | "review" | "tutorial" | "listicle" | "demo" | "story" | "reaction" | "explainer"
  hook: string                # The actual hook/opening line
  hook_type: string           # "question" | "shock" | "bold_claim" | "demo_first" | "relatable" | "contrarian"
  source: string              # "gap" | "pattern" | "contrarian" | "user_provided"
```

### Step 3: Score Each Angle

Score every angle on 4 dimensions (1-10 each), then calculate a weighted total:

```
angle_score = (platform_fit × 0.25) + (competition_level × 0.30) +
              (engagement_prediction × 0.30) + (creator_fit × 0.15)
```

**Dimension 1: Platform Fit (weight: 25%)**

How well does this format/hook work on the target platform?

| Format | YouTube | TikTok | LinkedIn | X | Reddit | Blog |
|--------|---------|--------|----------|---|--------|------|
| comparison | 9 | 8 | 7 | 5 | 8 | 9 |
| review | 8 | 6 | 5 | 4 | 9 | 9 |
| tutorial | 9 | 7 | 6 | 3 | 7 | 10 |
| listicle | 7 | 8 | 9 | 8 | 6 | 8 |
| demo | 8 | 10 | 5 | 4 | 3 | 5 |
| story | 6 | 9 | 10 | 8 | 7 | 7 |
| reaction | 7 | 10 | 4 | 6 | 5 | 3 |
| explainer | 8 | 5 | 8 | 6 | 8 | 9 |

Adjust based on actual scout data if available (if comparisons outperform on a platform
where they usually don't, use the real data instead of the default table).

**Dimension 2: Competition Level (weight: 30%)**

How many similar content pieces already exist? Higher score = LESS competition.

```
IF scout data available:
  Count how many top_content pieces match this angle's format + similar topic
  10 = zero similar content found (blue ocean)
  7-9 = 1-3 similar pieces (low competition)
  4-6 = 4-10 similar pieces (moderate competition)
  1-3 = 10+ similar pieces (saturated)
  
IF no scout data:
  web_search for the exact angle title → count results
  Fewer results with exact match = higher score
```

**Dimension 3: Engagement Prediction (weight: 30%)**

How likely is this angle to get high engagement based on data?

```
IF scout data available:
  Look at engagement scores of similar formats and hooks in top_content
  If this angle's format has avg_engagement > median → higher score
  If this angle's hook_type has avg_engagement > median → higher score
  Combine: angle uses top format + top hook → 9-10
  Angle uses average format + average hook → 5-6
  Angle uses underperforming format → 3-4

IF no scout data:
  Use platform defaults and general engagement patterns
  Comparisons generally outperform reviews → 8 vs 6
  Bold claim hooks generally outperform questions → 8 vs 6
```

**Dimension 4: Creator Fit (weight: 15%)**

How well does this angle match the creator's strengths?

```
IF creator_strengths provided:
  "storytelling" → story format, relatable hooks → high fit
  "technical" → tutorial format, demo hooks → high fit
  "humor" → reaction format, relatable hooks → high fit
  "authority" → review format, bold claim hooks → high fit
  "visual" → demo format, demo_first hooks → high fit
  "data" → comparison format, explainer → high fit
  "personal_experience" → story format, reaction → high fit
  
  Match count: 2+ matches → 9-10, 1 match → 6-7, 0 matches → 4-5

IF n