What is content-metrics?

This Claude skill serves as a comprehensive framework for measuring, analyzing, and optimizing digital content performance. It provides data-driven benchmarks for platforms like LinkedIn and X (Twitter), advanced analysis frameworks like the 80/20 rule and Triple-F (Format, Focus, Framing), and structured reporting templates to transform raw engagement data into actionable growth strategies.

When should I use content-metrics?

content-metrics is useful in the following scenarios: • Benchmarking social media engagement, comment, and share rates against industry standards for LinkedIn and X (Twitter). • Conducting 80/20 analysis to identify the top 20% of content driving 80% of results and uncovering successful patterns. • Optimizing content strategy through systematic A/B testing of hooks, content lengths, and posting frequencies. • Generating structured weekly, monthly, and quarterly performance reports to track audience growth and content resonance. • Distinguishing between vanity metrics and value metrics to focus on data that drives actual conversions and inbound opportunities.

name	content-metrics
description	Knowledge base for measuring, analyzing, and optimizing content performance. Use when tracking metrics, analyzing patterns, or making data-driven content decisions.

Content Metrics Skill

Framework for measuring and optimizing content performance across platforms.

Metrics Hierarchy

Vanity vs Value Metrics

Vanity Metrics (Nice to have):

Follower count
Total likes
Impressions

Value Metrics (Actually matter):

Engagement rate
Comment quality
Profile visits → follows
Inbound opportunities
Conversion to action

The Metrics That Matter

Metric	What It Measures	Why It Matters
Engagement Rate	Likes+Comments+Shares / Impressions	True content resonance
Comment Count	Number of comments	Content sparked discussion
Save/Bookmark Rate	Saves / Impressions	Content worth keeping
Share Rate	Shares / Impressions	Content worth spreading
Profile Visits	Clicks to profile	Interest in author
Follow Rate	New followers / Impressions	Converting readers to audience

Platform-Specific Benchmarks

Metric	Poor	Average	Good	Great
Engagement Rate	<1%	1-2%	2-4%	>4%
Comment Rate	<0.1%	0.1-0.3%	0.3-0.5%	>0.5%
Impressions	<500	500-2K	2K-10K	>10K

Twitter/X

Metric	Poor	Average	Good	Great
Engagement Rate	<0.5%	0.5-1%	1-2%	>2%
Retweet Rate	<0.1%	0.1-0.5%	0.5-1%	>1%
Reply Rate	<0.1%	0.1-0.2%	0.2-0.5%	>0.5%

Performance Categories

Content Type Performance

Track performance by type:

Personal stories
Frameworks/how-tos
Contrarian takes
Industry commentary
Curated content
Questions/polls

Topic Performance

Track by topic area:

Leadership
Technical
Career advice
Industry trends
Personal brand
Company/product

Format Performance

Track by format:

Text only
Text + image
Carousel
Video
Thread
Poll

Timing Performance

Track by:

Day of week
Time of day
Posting frequency

Analysis Frameworks

The 80/20 Analysis

List all content from period
Rank by key metric (engagement)
Identify top 20%
Analyze: What do they share?
Identify bottom 20%
Analyze: What patterns appear?

The Triple-F Framework

Format: What format performed best? Focus: What topics resonated? Framing: How was content positioned?

Trend Analysis

1. Baseline: What's your average?
2. Compare: Each post vs average
3. Trend: Is average improving?
4. Outliers: What caused spikes?
5. Patterns: What's consistent?

Data Collection

What to Track

For each post, record:

Date/time posted
Platform
Content type/format
Topic
Hook type used
Length
Key metrics (24h, 48h, 7d)
Notable outcomes

Memory Storage Format

Category: ["content-published", "[platform]"]
Content: "Post: [hook/summary]
Published: [date]
Platform: [platform]
Type: [content type]
Topic: [topic]
Metrics (7d): [likes], [comments], [shares], [impressions]
Notable: [any notable outcomes]"

Optimization Framework

The Testing Loop

1. Hypothesis: "Story hooks get more engagement"
2. Test: Publish 5 story-hook posts
3. Measure: Compare to baseline
4. Learn: Confirm or reject hypothesis
5. Apply: Adjust strategy
6. Repeat

Variables to Test

Hook Types:

Story tease
Question
Contrarian statement
List promise
Direct statement

Content Length:

Short (<100 words)
Medium (100-300 words)
Long (300+ words)

Posting Time:

Morning (7-10 AM)
Midday (11 AM-2 PM)
Afternoon (3-6 PM)
Evening (7-10 PM)

Frequency:

Daily
Every other day
2-3x per week
Weekly

Pattern Recognition

Signals of Good Content

High comment-to-like ratio (people talking, not just clicking)
Save/bookmark rate above average
Comments asking questions (engagement)
Shares with commentary added
Profile visits spike after post

Signals of Weak Content

High impressions, low engagement (reached people, didn't resonate)
No comments (didn't spark thought)
Quick engagement drop-off (didn't hold attention)
No profile visits (didn't build interest in author)

Reporting Templates

Weekly Check-in

This Week:
- Posts: X
- Avg Engagement: X (vs Y last week)
- Best: [Post summary] - [why it worked]
- Learning: [One insight]

Monthly Review

This Month:
- Total Posts: X
- Engagement Trend: [up/down/stable]
- Top 3: [List with metrics]
- Pattern: [Key pattern identified]
- Next Month Focus: [Priority]

Quarterly Analysis

Quarter Overview:
- Content Volume: X posts
- Audience Growth: X → Y followers
- Engagement Trend: [graph or summary]
- Top Performers: [Top 10 with analysis]
- Patterns Confirmed: [List]
- Strategy Adjustments: [Changes to make]

Converting Insights to Action

From Data to Decision

Finding	Implication	Action
Story posts perform 2x	Audience likes narrative	Lead with stories more
Tuesday posts best	Audience active mid-week	Prioritize Tue-Thu
Long posts underperform	Audience prefers concise	Tighten editing

Priority Matrix

	High Impact	Low Impact
Easy	Do first	Do if time
Hard	Plan for	Don't do

Focus optimization efforts on high-impact, easy changes first.

content-metrics

When & Why to Use This Skill

Use Cases