SKILL.md
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Data Collection
Path A — With BlackTwist
When BlackTwist tools are available, pull data in this order:
- **
list_posts** — retrieve recent posts to establish the analysis window (default: last 30 days or last 20 posts, whichever is larger)
- **
get_post_analytics** — pull per-post metrics: impressions, likes, comments, reposts, saves, link clicks, profile visits
- **
get_live_metrics** — check current real-time performance for any posts still gaining traction
- **
get_metric_timeseries** — pull engagement rate and impressions over time to identify trends (weekly view recommended)
- **
get_daily_recap** — surface any anomaly days (unusually high or low performance)
- **
get_consistency** — check posting frequency and whether consistency correlates with performance shifts
Collect all data before beginning analysis. Do not present raw numbers to the user — interpret them.
Path B — Without BlackTwist
If BlackTwist is unavailable, ask the user to provide their data. Use this prompt:
"To analyze your performance, I need your post metrics. You can share:
- A screenshot of your analytics dashboard
- A CSV export from your platform
- Manual input using the template below
Data Collection Template:
For each post (last 14–30 days), collect:
Post
Date
Impressions
Likes
Comments
Reposts
Saves
Link Clicks
Profile Visits
The minimum needed for a useful analysis: impressions + likes + comments for at least 5 posts."
Do not attempt analysis with fewer than 5 posts — tell the user why and ask for more.
Metrics Framework
Organize all metrics into three categories before analyzing:
Reach
- Impressions — total times the post appeared in feeds (includes repeats)
- Reach — unique accounts who saw the post
- Profile visits from post — how many viewers clicked through to learn more
Engagement
- Likes — passive positive signal
- Comments — active engagement; higher weight than likes
- Reposts / shares — distribution signal; the most valuable organic action
- Saves — intent to return; strong indicator of lasting value
- Engagement rate — calculate as:
(likes + comments + reposts + saves) / impressions × 100
Conversion
- Link clicks — traffic signal; only relevant when a link is present
- DMs from post — often untracked but worth asking the user about
- Follows from post — net new audience directly attributable to the content
Important: Always compare engagement rate, not raw engagement numbers. A post with 50 likes from 500 impressions (10% ER) outperforms a post with 200 likes from 10,000 impressions (2% ER).
Analysis Outputs
Produce all four outputs below. Do not skip any section.
1. Top Performers
Identify the top 3–5 posts by engagement rate. For each:
- State the engagement rate and the raw numbers behind it
- Diagnose why it worked — be specific across these dimensions:
- Topic: Was it timely, controversial, educational, personal?
- Format: Thread, single post, list, story, data-driven?
- Hook: What did the first line do? Which hook pattern?
- Timing: Day of week, time of day — any pattern?
- Call to action: Did it invite a specific response?
Do not just say "this performed well." Say: "This post's engagement rate of 8.4% was 3x your average. The hook led with a specific number, the topic addressed a pain point your audience frequently comments about, and you posted on Tuesday at 9am — your historically strongest slot."
Example top performer diagnosis:
Post: "7 writing habits that doubled my output" (March 12, 9:14 AM)
ER: 8.4% (vs. 2.8% baseline) — 3x your average
Impressions: 4,200 | Likes: 189 | Comments: 47 | Reposts: 31 | Saves: 86
Why it worked:
- Hook: List preview pattern ("7 habits...") — your strongest hook type
- Topic: Productivity + writing — overlaps two of your top pillars
- Timing: Tuesday morning — your historically strongest slot
- CTA: "Which one surprised you?" — drove 47 comments2. Bottom Performers
Identify the bottom 3–5 posts by engagement rate. For each:
- State the engagement rate
- Diagnose what went wrong — be specific:
- Weak or generic hook?
- Topic misaligned with audience interest?
- Posted at an off-peak time?
- Format mismatch for the platform?
- Too promotional or self-serving?
Frame diagnoses as learnings, not failures.
3. Trend Analysis
Look across the full dataset and answer:
- Engagement trend: Is the average engagement rate going up, down, or flat over the analysis window?
- Impressions trend: Is organic reach growing, shrinking, or holding steady?
- Consistency impact: Does posting frequency correlate with performance? (More posts = more reach, or does quality drop when volume increases?)
- Content type trends: Are certain formats (threads, single posts, lists) consistently outperforming others?
State the trend clearly — "Your engagement rate has declined 22% over the last 3 weeks, while impressions held steady. This suggests your content is reaching people but not resonating." — then explain what it likely means.
Example trend analysis output:
Trend Summary (March 1–31):
- Engagement rate: 2.8% avg (down 22% from February's 3.6%)
- Impressions: 2,100/post avg (stable — no change from February)
- Posting frequency: 4.2x/week (up from 3.1x/week in February)
- Diagnosis: Increased volume diluted quality. Impressions held but
resonance dropped — content is reaching people but not connecting.4. Actionable Insights
Close every analysis with 3–5 specific, prioritized actions based on the findings. Each action must:
- Reference a specific finding from the analysis (not generic advice)
- Be concrete enough to act on this week
- Be ranked by expected impact
Example format:
- Replicate your Tuesday hook pattern — Your top 3 posts all opened with a specific number. Write your next 5 hooks using the statistic/data pattern.
- Stop posting on Fridays — Your Friday posts average 1.8% ER vs. 5.2% on other days. Shift that content to Wednesday.
- Add a save CTA to educational posts — Your how-to content gets high impressions but low saves. End with "Save this for later" and retest.
Benchmarking
Always benchmark against the user's own averages, not platform-wide vanity metrics.
Calculate the user's baseline from the analysis window:
- Average engagement rate across all posts
- Average impressions per post
- Average comments per post
Use these baselines when labeling a post as a "top performer" or "underperformer." A 3% engagement rate may be excellent for one creator and mediocre for another.
Do not cite industry benchmarks ("the average Threads engagement rate is X%") unless the user specifically asks for external comparison. Their history is the only relevant benchmark.
Reporting Format
Deliver findings in this structure — not as a wall of numbers:
## Performance Analysis — [Date Range]
**Posts analyzed:** [N]
**Your baseline engagement rate:** [X%]
**Impressions trend:** [Up / Down / Flat] [X%]
---
### Top Performers
[3–5 posts with diagnosis]
### Bottom Performers
[3–5 posts with diagnosis]
### Trends
[3–5 sentences on directional patterns]
### What to Do Next
[3–5 ranked, specific actions]Keep the report scannable. Use bold for key terms. Avoid tables with more than 5 columns — they are hard to read in most interfaces. Write in active voice throughout.
Boundaries
- Does not track follower growth or audience demographics — see audience-growth-tracker-sms for growth analysis
- Does not detect cross-post content patterns — see content-pattern-analyzer-sms for pattern detection across many posts
- Does not generate a prioritized action plan — see optimization-advisor-sms for concrete next steps
- Does not write or draft content — see post-writer-sms for content creation
- Does not execute code or access external APIs unless BlackTwist MCP is connected
- Does not cite industry benchmarks unless explicitly requested — all comparisons use the user's own averages
Related Skills
- social-media-context-sms — establish niche, voice, and goals before analyzing
- content-pattern-analyzer-sms — go deeper on what content patterns drive performance
- optimization-advisor-sms — translate analysis findings into a concrete improvement plan