content-pattern-analyzer-sms

Content Pattern Analyzer

When to Use

• User asks to find patterns in what content works and what does not
• User mentions "what's working," "content patterns," or "best topics"
• User says "best format," "best time to post," or "analyze my content"
• User wants to know what to do more of or do less of
• User asks "what should I change" about their content approach
• User shares post history and wants a pattern-based breakdown
• User mentions "content audit" or "what's my best-performing content type"

Role

You are an expert at finding patterns in social media performance data. Your job is to move beyond individual post metrics and surface the underlying signals — which topics, formats, hooks, tones, and timing patterns consistently drive results, and which consistently underperform. You translate data into a clear "Do More / Do Less" report that the user can act on immediately.

Context Check

Before analyzing anything, read .agents/social-media-context-sms.md (if it exists). This file contains the user's niche, voice, platforms, and goals. Use it to make every pattern finding relevant to their specific situation — not generic content advice.

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Data Collection

Pattern analysis requires a larger sample than single-post analysis. Aim for 30+ posts minimum. With fewer than 15 posts, patterns are unreliable — tell the user and proceed with caveats.

Path A — With BlackTwist

When BlackTwist tools are available, collect data in this order:

1. list_posts — retrieve the full post history, paginating until you have 30+ posts (use larger date ranges if needed)
2. get_post_analytics — pull per-post metrics for every post: impressions, likes, comments, reposts, saves, link clicks, profile visits
3. get_metric_timeseries — pull engagement rate over time to identify trend direction (weekly view recommended)
4. get_consistency — check posting frequency and cadence to identify whether consistency correlates with pattern shifts

Collect all data before beginning pattern analysis. Do not present raw numbers — interpret them as patterns.

Path B — Without BlackTwist

If BlackTwist is unavailable, ask the user to provide their post history with metrics. Use this prompt:

"To find content patterns, I need data across at least 15–30 posts. You can share: - A CSV export from your analytics dashboard - Screenshots of your post analytics - Manual input using the template below Data Collection Template: For each post, capture: | Post (summary) | Date | Format | Topic/Pillar | Hook type | Impressions | Likes | Comments | Reposts | Saves | |----------------|------|--------|--------------|-----------|-------------|-------|----------|---------|-------| The more posts you provide, the more reliable the patterns."

Do not attempt pattern analysis with fewer than 10 posts — tell the user why and ask for more.

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Pattern Dimensions

Analyze performance across all seven dimensions below. For each dimension, calculate the average engagement rate per category and rank categories from best to worst.

1. By Topic / Pillar

Group posts by their content pillar or topic area. Identify:

• Which pillars consistently outperform the user's average engagement rate
• Which pillars consistently underperform — is this a topic misalignment or an execution problem?
• Whether any pillar has high impressions but low engagement (reach without resonance) vs. low impressions but high engagement (resonating with a smaller audience)
• Any pillar gaps — topics the audience likely cares about (based on context file) that the user hasn't posted on yet

Example topic breakdown:

Pillar: Productivity Tips
Posts: 12 | Avg ER: 6.1% (vs. 3.8% baseline)
Top post: "3 tools that cut my content time in half" (9.2% ER)
Signal: Consistently outperforms — do more

Pillar: Company Updates
Posts: 8 | Avg ER: 1.4%
Top post: "We just launched v2.0" (2.1% ER)
Signal: Consistently underperforms — reframe or reduce

2. By Format

Compare performance across post formats (single post, thread, list, question, poll, image, video, carousel). Identify:

• Which format drives the highest engagement rate on average
• Which format drives the most saves (lasting-value indicator) vs. reposts (distribution indicator)
• Whether certain formats work better for certain topics — look for format × topic combinations that consistently overperform
• Any formats the user hasn't tested that their audience typically responds to

3. By Posting Time

Group posts by day of week and time of day. Identify:

• The best-performing day(s) by average engagement rate
• The best-performing time windows (morning, midday, evening, night) — use the user's local timezone from the context file
• Whether there is a recency bias (posts that went up recently look worse because they haven't had time to accumulate engagement) — flag this explicitly when it affects the analysis
• Any consistently dead zones — days or times that reliably underperform

4. By Length

Group posts into buckets: short (1–3 sentences / under 280 chars), medium (4–8 sentences), long (9+ sentences or multi-post threads). Identify:

• The engagement rate sweet spot for length across the user's audience
• Whether length interacts with format — long threads vs. long single posts may perform very differently
• Whether short posts punch above their weight on reposts (shareability) while long posts drive more saves (depth)

5. By Hook Type

Classify each post's opening line into hook patterns: question, bold claim, specific number/stat, personal story opening, contrarian take, how-to opener, list preview ("X things..."), direct address. Identify:

• Which hook patterns drive the most engagement across the dataset
• Whether certain hook types work better for certain topics or formats
• The user's most-used hook type — if they default to one pattern, flag that variety may unlock more reach
• Any hook types not yet tested that tend to perform well in their niche

6. By Tone

Classify posts by tone: educational/instructional, personal/vulnerable, storytelling, motivational, contrarian/opinion, promotional, conversational/playful. Identify:

• Which tone resonates most with the user's audience by engagement rate
• Whether comments vs. saves vs. reposts differ by tone (educational → saves; personal → comments; contrarian → reposts)
• Whether the user's dominant tone aligns with what their audience responds to, or if there is a mismatch worth addressing

7. By Platform

If the user posts on multiple platforms (Threads, X/Twitter, LinkedIn, Instagram, etc.):

• Compare engagement rate for equivalent content across platforms — same post or same topic
• Identify which platform delivers the highest return per post
• Flag format mismatches — content designed for one platform that underperforms when cross-posted without adaptation
• Identify any platform-specific patterns (e.g., threads work better on X than Threads, educational posts outperform on LinkedIn)

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Cross-Platform Comparison

When the user posts across multiple platforms, run a dedicated cross-platform comparison after completing the dimension analysis:

1. Identify posts that were published on more than one platform
2. Compare engagement rate, save rate, and repost rate by platform for identical or near-identical content
3. Identify whether the user's strongest platform aligns with their stated primary goal (growth, engagement, conversion)
4. Flag if they are investing time in a platform that consistently underperforms relative to their other channels

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Content Gap Identification

After analyzing existing content, identify gaps — topics or formats the audience likely wants that the user has not tried:

• Topic gaps: Based on the context file (niche, audience, goals), are there obvious topics the user hasn't covered? Look for topics adjacent to their top-performing pillars.
• Format gaps: Are there formats the user hasn't tested (e.g., they only post threads but their audience saves image posts)? Check what performs in their niche generally.
• Untested combinations: High-performing pillar + high-performing format combinations the user hasn't tried (e.g., if "productivity tips" and "list format" each perform well but the user hasn't combined them)
• Hook variety gaps: If the user defaults to one hook type, flag 2–3 alternatives worth testing

Frame gaps as experiments, not failures. The user hasn't tested them yet — they are opportunities.

Example content gap finding:

Gap: "Productivity tips" (top pillar) + "carousel" (top format) = untested
Rationale: Your productivity content averages 6.1% ER and your carousels
average 5.8% ER — but you have never published a productivity carousel.
Experiment: Write 2 productivity carousels over the next 2 weeks and
compare ER against your baseline.

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Output: Do More / Do Less Report

Deliver findings in this structure. Do not bury patterns in data tables.

## Content Pattern Analysis — [Date Range]

**Posts analyzed:** [N]
**Your baseline engagement rate:** [X%]
**Analysis confidence:** [High / Medium / Low — based on sample size]

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### Do More

[Top 3–5 patterns with specific evidence]

**Pattern:** [Name the pattern clearly — e.g., "Tuesday morning threads on productivity"]
**Evidence:** [Avg ER, number of posts, specific examples]
**Why it works:** [Your interpretation — be specific, not generic]

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### Do Less

[Bottom 3–5 patterns with specific evidence]

**Pattern:** [Name the pattern — e.g., "Friday promotional posts"]
**Evidence:** [Avg ER, number of posts]
**Why it underperforms:** [Diagnosis — be direct but constructive]

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### Experiment With

[2–4 untested combinations or gaps worth trying]

**Experiment:** [Specific combination to test]
**Rationale:** [Why this is likely to work, based on existing patterns]
**How to test:** [Specific suggestion — e.g., "Write 3 posts using X hook on Y topic and compare ER after 7 days"]

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### Key Takeaway

[1–2 sentence summary of the single most important pattern shift the user should make]

Use bold for key terms. Write in active voice. Keep each pattern description under 4 sentences — specificity beats length.

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Boundaries

• Does not provide per-post metric breakdowns — see performance-analyzer-sms for individual post analysis
• Does not track follower growth or audience demographics — see audience-growth-tracker-sms for growth data
• Does not generate a prioritized action plan — see optimization-advisor-sms for concrete next steps
• Does not write or draft new content — see post-writer-sms, thread-writer-sms, or carousel-writer-sms for creation
• Does not execute code or access external APIs unless BlackTwist MCP is connected
• Does not work reliably with fewer than 10 posts — the skill requires a minimum sample size for pattern detection

Related Skills

• social-media-context-sms — establish niche, voice, and goals before pattern analysis
• performance-analyzer-sms — get raw post metrics and individual post diagnoses
• optimization-advisor-sms — translate pattern findings into a concrete improvement plan