Best for
- B2B founders or executives posting consistently on LinkedIn
- Social media managers trying to improve content quality with real performance data
- Marketing teams managing founder-led or executive thought leadership programs
Turn LinkedIn post performance into a practical learning system that improves future hooks, formats, topics, and founder-led content decisions.
A recurring LinkedIn learning loop with post tags, performance analysis, content rules, and a next-month posting plan.
Quick view
Check the audience, core tools, and access before you start the detailed steps.
Best for
Core tools
5 tools used across the workflow.
Access
Preview the open steps, then unlock the remaining implementation details and prompts.
Why this works
Most LinkedIn analysis stops at vanity metrics and generic advice. This workflow connects performance to specific post traits: hook type, format, topic, CTA, narrative structure, and audience response. Over time, the team builds a practical content playbook based on what their actual audience rewards.
Expected results
Posts analyzed
30-90 posts
This range is enough to identify recurring patterns without overfitting to one week of performance.
Content rules created
8-15 rules
A practical ruleset should be small enough for writers to remember and specific enough to guide drafting.
Review time saved
3-5 hours per month
Structured exports, tags, and AI-assisted pattern analysis reduce manual spreadsheet review and subjective debate.
Content quality
Performance-backed planning
The next calendar is built from actual audience response, not generic LinkedIn best practices.
Step-by-step workflow
The first 2 steps are open. Pro unlocks the remaining steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and implementation details.
30 min
30 min
Export 30-90 days of post performance from Shield or your LinkedIn analytics tool. Include post text, publish date, impressions, reactions, comments, reposts, profile views, follower growth, clicks if available, and any inbound conversations created. Stage the data in Google Sheets.
A clean LinkedIn performance dataset with post text and engagement metrics.
Capture business-relevant notes manually. A post with modest likes but two target-account comments may be more valuable than a viral post with irrelevant engagement.
45 min
45 min
Use Claude to classify each post by hook type, format, topic, point of view strength, CTA, story use, and audience target. Store the tags in Airtable so future content reviews do not start from scratch. Keep the taxonomy lightweight enough to use every month.
Tagged LinkedIn post library organized by content traits.
Tag 'why it worked' separately from 'what it was about.' Topic and format are not the same thing.
Classify these LinkedIn posts into a reusable performance taxonomy.
Posts and performance data:
{{linkedin_posts_with_metrics}}
Business goals:
{{business_goals}}
For each post, tag:
1. Hook type
2. Format
3. Topic
4. Audience segment
5. Point of view strength
6. Story or example used
7. CTA type
8. Likely reason it performed or underperformed
Return the tagged dataset and a short definition for each tag.Pro workflow preview
Previewing 2 of 7 steps
Get the remaining 5 steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and ongoing workflow and prompt releases.
$9/month
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