Predict which LinkedIn content will perform best with your audience using AI-powered segment analysis and calibrated scoring
**Command:** `/accuracy`
**Command:** `/audience`
**Command:** `/deep-dive [segment-name]`
**Command:** `/score`
**Command:** `/setup-audience-simulator`
Deep analysis of a specific segment including roles, topics, and past performance
Display high-level summary of audience segments, sizes, topics, and activity
Score 3 content variations and rank them with comparative analysis
Review calibration metrics, accuracy trends, and model confidence levels
Core skill for scoring a draft post with per-segment predictions and reasoning
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Predict which LinkedIn content will perform best with your audience using AI-powered segment analysis and calibrated scoring.
Part of the 3-plugin LinkedIn ecosystem:
Plugin 3 analyzes your LinkedIn connections to predict audience segments and forecast post performance:
/setup-audience-simulator in Cowork/score to score your first draft| Command | What it does |
|---|---|
/setup-audience-simulator | First-run setup: parse connections, identify segments, initialize database |
/score | Score a draft post and get per-segment predictions |
/audience | View high-level overview of your audience segments, sizes, and engagement patterns |
/deep-dive [segment] | Explore a specific segment in detail (roles, topics, past performance) |
/accuracy | Review prediction calibration metrics and accuracy trends |
When you run /score with a draft post:
Plugin 1 (Feed Tracker)
├─ Collects network posts → feeds.db
├─ Collects your own posts → feeds.db
└─ Tracks connections → feeds.db
Plugin 3 (Audience Simulator)
├─ Parses connection headlines
├─ Creates audience segments (Executives, Technical Leaders, Growth & Sales, General)
├─ Tags posts with topics (AI/ML, Leadership, etc.)
├─ Calculates segment × topic affinities
└─ Stores in connection_profiles, audience_segments, segment_topic_affinity
User scores a draft → /score command
├─ Analyzes draft topics and hook
├─ Queries segment affinities
├─ Applies calibration (if 10+ predictions tracked)
└─ Returns 0-100 score + per-segment breakdown
User posts on LinkedIn → Plugin 1 tracks actual engagement
User reviews performance → /accuracy command
├─ Compares predicted vs actual
├─ Updates calibration coefficients
└─ Improves future predictions
Score formula:
overall_score = weighted_average(segment_scores)
where:
segment_score = (hook_strength + topic_affinity) / 2
weight = segment_size × activity_rate
Confidence levels:
Calibration:
calibrated_score = (predicted_score × slope) + intercept> /setup-audience-simulator
✓ Checking Plugin 1... Found 1,966 connections and 8,234 posts.
✓ Parsed 1,966 connection headlines.
✓ Identified 4 core audience segments:
- Executives (150 people)
- Technical Leaders (420 people)
- Growth & Sales (380 people)
- General Audience (1,016 people)
✓ Audience Simulator initialized!
> /score
[Paste your draft...]
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