Review

Post-Publish Performance Review

Analyze how published posts performed against Phoenix scoring predictions and extract actionable lessons for future content.

Input

The user provides one or more of:

• Post URL(s) — to analyze directly
• Engagement data — impressions, replies, reposts, likes, bookmarks, link clicks
• Analytics screenshot — from X Premium analytics dashboard
• General request — "review my recent posts" or "what's working?"

Process

Step 1 — Gather Performance Data

If the user provides a URL: Use WebFetch to read the post text and any publicly visible metrics. WebFetch can extract the post content, author, and basic engagement signals visible on the page.

For full authenticated metrics (detailed impression counts, bookmark counts, analytics dashboard data), suggest running /analyze [url] which uses chrome browser automation for logged-in access to richer data.

If the user provides raw data, use that directly.

Key metrics to capture:

• Impressions — total eyeballs
• Engagement rate — (all engagements / impressions) × 100
• Reply count — most important engagement metric
• Reposts — distribution amplifier
• Bookmarks — quality signal
• Likes — baseline (least important positive)
• Profile visits — discovery signal
• Follower change — growth impact
• Video views / completion % (if applicable)
• Link clicks (if applicable)

Step 2 — Score Against Phoenix Hierarchy

Map actual performance to algorithm signals:

Metric	Value	Algorithm Interpretation
Author reply threads	?	75–150× weight — did the author create conversation threads?
Replies	?	13–27× weight — [assessment]
Reposts	?	~20× weight — [assessment]
Bookmarks	?	~10× weight — [assessment]
Impressions vs followers	?	Distribution multiplier — [assessment]
Likes	?	1× baseline — [assessment]
Engagement rate	?	Overall quality signal — [assessment]

Engagement rate benchmarks:

• < 1%: Below average — content or timing issue
• 1-3%: Average
• 3-5%: Good
• 5-10%: Excellent
• 10%+: Exceptional (viral territory)

Reply-to-like ratio (key health metric):

• < 0.05: Low conversation — hook or CTA needs work
• 0.05-0.15: Normal
• 0.15-0.30: Good conversation driver
• 0.30+: Excellent — algorithm heavily rewards this

Step 3 — Diagnose Performance

If high impressions + low engagement:

• Content reached people but didn't resonate
• Likely issue: weak hook, wrong audience timing, or content quality
• The algorithm showed it but people didn't engage → future posts may get reduced distribution

If low impressions + high engagement:

• Content resonated but wasn't distributed widely
• Likely issue: timing, small follower base, or posting frequency penalty
• The algorithm may expand distribution on future similar content

If high replies specifically:

• Excellent — this is the highest-weight signal
• Analyze what in the post triggered replies
• Replicate this pattern

If high bookmarks:

• Content was save-worthy — strong quality signal
• This content works well as a thread or series
• Consider expanding into related topics

Step 4 — Extract Lessons

For each post reviewed, identify:

1. What worked — specific elements to replicate
2. What underperformed — specific elements to change
3. Algorithm diagnosis — why the algo distributed (or didn't) as it did
4. Actionable next step — one specific thing to try next post

Step 5 — Output

Present:

1. Performance summary — Key metrics in a clear table
2. Phoenix score analysis — How each metric maps to algorithm signals
3. Diagnosis — Why the post performed as it did
4. Lessons learned — Specific, actionable takeaways
5. Recommended next post — Based on what worked, suggest the next content piece
6. Trend over time — If reviewing multiple posts, identify patterns (improving, declining, inconsistent)

If Using OpenTweet MCP

Query analytics directly:

• "Show me my best performing posts this week"
• "What posting time got the best engagement?"
• Use this data to refine the schedule skill recommendations