Rank content angles by engagement data, competition level, and platform fit. Data-driven angle selection instead of guesswork. Use this skill when the user has a keyword or product and needs to decide WHAT to create, which angle to take, which format to use, or which platform to target. Triggers on: "what angle should I use", "rank content ideas for [keyword]", "best angle for [product]", "which content idea will perform best", "help me pick an angle", "what should I write about", "content angle for [topic]", "rank my content ideas", "which approach will get the most views", "data-driven content planning", "angle ranker", "content scoring", "which hook should I use", "compare these content ideas", "prioritize my content angles", "what video should I make".
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Rank content angles by engagement data, competition level, and platform fit. Data-driven angle selection instead of guesswork. Use this skill when the user has a keyword or product and needs to decide WHAT to create, which angle to take, which format to use, or which platform to target. Triggers on: "what angle should I use", "rank content ideas for [keyword]", "best angle for [product]", "which content idea will perform best", "help me pick an angle", "what should I write about", "content angle for [topic]", "rank my content ideas", "which approach will get the most views", "data-driven content planning", "angle ranker", "content scoring", "which hook should I use", "compare these content ideas", "prioritize my content angles", "what video should I make".
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
keyword:string# (required if no scout_data) "AI video tools"product:object# (optional) Affiliate product being promotedname:string# "HeyGen"description:string# What it doesurl:string# Product URL or affiliate linkreward_value:string# Commission info — never shown in contentplatform:string# (required) Target platform for content creation# "youtube" | "tiktok" | "linkedin" | "x" | "reddit" | "blog"
# (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:
web_search "[keyword] site:youtube.com" → top 10 videos, note formats and view counts
web_search "[keyword] site:tiktok.com" OR web_search "[keyword] tiktok viral" → top TikTok content
web_search "[keyword] site:reddit.com top" → top Reddit discussions
web_search "[keyword] [platform] best performing" → meta-analysis of what works
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:
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
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
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
User-provided angles (from custom_angles):
Include any angles the user suggested
Score them alongside generated candidates — no bias
For each angle, define:
Angle:title:string# Specific, could be an actual content titleangle:string# Brief description of the angleformat:string# "comparison" | "review" | "tutorial" | "listicle" | "demo" | "story" | "reaction" | "explainer"hook:string# The actual hook/opening linehook_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:
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 no creator_strengths:
Default all angles to 7 (neutral)
**Alternative paths:**
- `tiktok-script-writer` — for short-form video version of Angle #1
- `content-pillar-atomizer` — create a blog post, then atomize across all platforms
- `comparison-post-writer` — if the winning angle is a comparison
Error Handling
No scout data and no keyword: Ask user: "What topic or product are you creating content for? And which platform?"
Only 1 platform specified + limited data: Generate angles anyway using platform-specific defaults. Note: "Limited data available. Scores are based on general platform patterns. Run trending-content-scout first for data-backed scoring."
All angles score similarly (within 0.5 points): Spread them out by double-weighting the most differentiating dimension. Present as: "These angles are closely matched. The tiebreaker is [competition/creator fit/etc.]."
User's custom angles score low: Still include them but be honest: "Your angle '[X]' scored [X.X/10] — competition is high and the format doesn't match platform trends. Consider the #1 angle instead, or combine your angle with a [winning format]."
Time budget too short for any good angle: Recommend the easiest angle regardless of score, and flag: "With [30 min], your best option is [quick angle]. For higher impact, allocate [2 hours] for [best angle]."
Examples
Example 1:
User: "I want to promote HeyGen on TikTok. What angle should I use?"
→ keyword: "HeyGen", platform: "tiktok"
→ Quick scout: web_search "HeyGen tiktok" → mostly demo-first content, 30-45s
→ Generate 10 angles: "HeyGen vs Synthesia comparison", "I made a $2000 video for free with HeyGen",
"POV: your boss asks for a video and you use AI", "HeyGen for real estate agents", etc.
→ Top angle: "I replaced a $2000 video production with HeyGen" — Score: 8.7
(bold_claim hook, demo format, low competition on TikTok, high engagement predicted)
→ Next: tiktok-script-writer with hook_style: bold_claim, duration: 45s
Example 2:
User: "Rank these content ideas for my YouTube channel about email marketing:
ConvertKit vs Mailchimp comparison
How I grew my list to 10K subscribers
Top 5 email marketing mistakes"
→ platform: "youtube", custom_angles provided
→ Scout YouTube for email marketing content
→ Score all 3 + generate 5 additional angles
→ Result: "How I grew to 10K" scores highest (story format + bold_claim = 8.5)
because competition for comparisons is saturated (score: 6.2) and listicles are average (7.1)
→ The user's story angle wins — with a suggested hook: "I went from 0 to 10K subscribers in 6 months. Here's what nobody tells you."
Example 3:
User: "I'm good at storytelling and humor. What should I create about AI writing tools on LinkedIn?"
→ creator_strengths: ["storytelling", "humor"], platform: "linkedin"
→ Generate angles weighted toward story format
→ Top: "I let AI write my LinkedIn posts for a week. My boss noticed." — Score: 9.1
(story format, relatable hook, low competition on LinkedIn for this angle, perfect creator fit)
Feedback & Issue Reporting
When this skill produces unexpected, incomplete, or incorrect output, generate a
skill_feedback block (see shared/references/feedback-protocol.md for full schema).
Skill-specific failure modes:
All angles score within 0.5 points: Scoring not differentiated enough. Report as wrong_output with the scores.
Top angle is generic: "Write a review of X" instead of a specific, titled angle. Report as data_quality.
Downstream skill can't use recommended_skill_params: Schema mismatch. Report as chain_break.
Auto-detect triggers:
Score range (max - min) < 1.0 across all angles
<8 angles generated
recommended_skill_params missing required fields for the suggested next skill
niche-opportunity-finder (S1) — niche context for angle generation
competitor-spy (S1) — competitor gaps to exploit
performance-report (S6) — historical angle performance data
Feedback Loop
S6 performance-report shows which angles actually performed → update scoring weights and format preferences for next run → content strategy improves with every cycle
