8e post performance tracker

Skill anandan-digital-marketer/seo-agent-skills/skills/8e-post-performance-tracker

29 SEO agent skills for Claude Code — technical auditing, LLM visibility, GEO optimization, content strategy

Install

npx -y skills add anandan-digital-marketer/seo-agent-skills --skill 8e-post-performance-tracker

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What its author says it does

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Pulls LinkedIn post engagement data via the LinkedIn MCP (Task 38, already live) after publishing. Tracks views, likes, comments, reposts, and follower growth per post. Stores history to identify what content formats and topics drive the most engagement for this audience. Feeds future content decisions (8D).

SKILL.md

5.7 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

8E — Post Performance Tracker

You are a social media analytics analyst. Data from past posts is the most reliable guide for what to write next.

Most LinkedIn creators post and forget. This agent closes that loop — every post generates learning that improves the next one.

Step 1 — Pull Post Data via LinkedIn MCP

Using the LinkedIn MCP (linkedin_get_my_posts tool):

Pull all posts from the last 30 days
For each post: content preview, post date, engagement metrics

LinkedIn MCP tools available (Task 38):

linkedin_get_my_posts — recent posts by authenticated user (Anandan N)
linkedin_token_status — verify token is valid before pulling

If token is expired (>59 days from May 13, 2026):

TOKEN EXPIRED. Run: python automation/linkedin/mcp-linkedin/get_linkedin_token.py to refresh. Then retry.

Step 2 — Extract Metrics Per Post

For each post, extract:

Metric	What It Measures
Impressions / Views	How many people saw it
Likes (reactions)	Positive resonance
Comments	Strongest engagement signal — algorithm rewards
Reposts/Shares	Viral coefficient — most valuable metric
CTR (if link in comment)	Did anyone actually click through?
Follower growth	Did this post attract new followers?

Engagement rate formula:

Engagement Rate = (Likes + Comments + Reposts) / Impressions × 100
Target: >2% is good, >5% is excellent for LinkedIn

Step 3 — Post History Store

Append to automation/linkedin-posts/post-performance-history.json:

{
  "posts": [
    {
      "post_id": "[id]",
      "published": "YYYY-MM-DD",
      "format": "text|carousel|image|video",
      "topic": "[topic label]",
      "hook_type": "number|claim|question|story",
      "word_count": N,
      "impressions": N,
      "likes": N,
      "comments": N,
      "reposts": N,
      "engagement_rate": X.X,
      "cta_keyword": "[keyword or null]",
      "cta_responses": N,
      "best_performing_time": "Tue 9am"
    }
  ]
}

Step 4 — Format Performance Analysis

Compare post types against each other:

Format	Avg Impressions	Avg Engagement Rate	Comments	Reposts
Text post
Carousel
Text + image
Video

Winner: [which format drives most engagement for this account]

Step 5 — Topic Performance Analysis

Group posts by topic and compare:

Topic	Posts	Avg Engagement Rate	Best Hook Type
SEO automation
LLM visibility
AI marketing tools
Technical setup/how-to
Data/research findings

Top performer: [topic + why it resonates] Underperformer: [topic to retire or reframe]

Step 6 — Posting Time Analysis

For posts published at different times, compare performance:

Day	Time	Avg Impressions	Notes
Monday	9am
Tuesday	9am
Wednesday	9am
Thursday	9am
Friday	9am

Best time for this account: [day + time]

LinkedIn's general recommendation: Tue-Thu 8-10am. But this account's audience may differ — data overrides convention.

Step 7 — Next Post Recommendations

Based on performance data, recommend:

Best format to use next: [format with highest avg engagement]
Best topic area: [topic that consistently outperforms]
Hook to try: [underused hook type that others in similar niches use successfully]
Optimal posting time: [from Step 6 data]
CTA to test: [if CTAs haven't been tested yet, suggest one]

LinkedIn Posts Pipeline Status (Task 44)

After pulling performance data, update the pipeline status:

Post 01 — AI SEO Engine: PUBLISHED [March 2026]
  Performance: [metrics if available]

Post 02 — AI Content Pipeline: [PUBLISHED or READY TO PUBLISH]
  Performance: [metrics or "not yet published"]

Post 03 — Blog Intelligence: [STATUS]
  Next step: [action]

Output Format

LINKEDIN POST PERFORMANCE REPORT
==================================
Date: [YYYY-MM-DD]
Posts analysed: [N]
Token status: [Valid until YYYY-MM-DD]

POST-BY-POST METRICS:
[table: date | format | topic | impressions | likes | comments | reposts | rate%]

FORMAT PERFORMANCE:
  Best: [format] — avg X% engagement
  Worst: [format] — avg X% engagement

TOPIC PERFORMANCE:
  Best: [topic] — avg X% engagement
  Next post should cover: [recommendation]

BEST POSTING TIME: [day + time]

PIPELINE STATUS:
  Post 01: [status + metrics]
  Post 02: [status + metrics]
  Post 03: [status]

NEXT POST RECOMMENDATIONS:
  Format: [recommendation]
  Topic: [recommendation]
  Hook: [example first line]
  Time: [day + time]
  CTA: [keyword + expected responses]

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.