#linkedin #analytics #engagement #reddit

Phy Post Forensics

Analyzes your social media posts' linguistic and structural features to identify patterns that drive your highest engagement and generate a personalized cont...

Lucius Pang@phy041

Install

openclaw skills install @phy041/phy-post-forensics

phy-post-forensics — "Why Did This Post Work?"

You post 50 times. 3 go viral. 47 die. Analytics tells you "what happened" (likes, views). This tool tells you why — which structural elements in your content drove engagement.

The Problem

Every analytics tool shows lagging indicators: impressions, likes, comments. None of them tell you:

Which hook type correlates with your best posts?
Do your top posts have more specific numbers?
Does sentence rhythm (mixing short + long) predict engagement?
Are your worst posts missing personal voice (first-person pronouns)?

This tool extracts 12 structural features per post, groups by performance tier, and outputs: "Your top posts share X, Y, Z. Your bottom posts all lack them."

Quick Start

bash

# Analyze posts from JSON file
python3 ~/.claude/skills/phy-post-forensics/scripts/post_forensics.py --file my_posts.json

# Pipe from stdin
cat posts.json | python3 ~/.claude/skills/phy-post-forensics/scripts/post_forensics.py

# JSON output (for pipelines)
python3 ~/.claude/skills/phy-post-forensics/scripts/post_forensics.py --file posts.json --format json

Input Format

json

[
  {
    "text": "I built 101 OpenClaw skills in 30 days. Here's what happened...",
    "platform": "linkedin",
    "engagement_rate": 8.5,
    "impressions": 15000,
    "date": "2026-03-18"
  },
  {
    "text": "Another post...",
    "platform": "reddit",
    "engagement_rate": 1.2,
    "impressions": 500
  }
]

Required: text, engagement_rate. Optional: platform, impressions, date.

The 12 Features Extracted

#	Feature	What It Measures	Why It Matters
1	Hook Type	First line classification: number_lead, question, contrarian, story_open, challenge, statement	Hook determines "stop scrolling" moment
2	Word Count	Total words	Platform sweet spots vary (LinkedIn ~100-200, Twitter ~50-100)
3	Sentence Count	Number of sentences	More sentences ≠ better, but structure matters
4	Sentence Length CV	Coefficient of variation of sentence lengths	High CV = mixed rhythm (human). Low CV = monotone (AI)
5	Question Count	Questions in body text	Questions drive comments and dwell time
6	Specific Numbers	Count of data points (%, $, metrics, years)	Posts with data get 3-4x more reach (LinkedIn 2026 data)
7	Personal Pronoun Density	I/my/we per 100 words	Personal voice = trust and engagement
8	List Formatting	Bullets or numbered lists present	Scannability drives dwell time
9	Paragraph Count	Number of visual breaks	Short paragraphs = mobile-friendly
10	CTA Type	Call-to-action classification: question, action, share, comment, none	CTAs drive comments, comments drive distribution
11	Sentiment	Positive / neutral / negative keyword analysis	Positive content drives more shares
12	Specificity Score	Proper nouns + numbers + tool names per 100 words	Specific > generic (3-4x reach difference)

How Analysis Works

Extract 12 features from every post
Tier posts by engagement: top 25%, middle 50%, bottom 25%
Compare feature distributions across tiers
Generate insights: which features differentiate top from bottom
Output blueprint: actionable content template based on YOUR data

Example Output

text

==================================================================
  phy-post-forensics — Content Forensics Report
==================================================================
  Posts analyzed : 8
  Top tier       : 2 posts (avg 11.05% engagement)
  Bottom tier    : 2 posts (avg 0.40% engagement)
  Spread         : Top posts get 27.6x more engagement
==================================================================

🔍  Key Insights (8 patterns found):

  1. 🔴 [HIGH] Specific Data Points
     Top posts: 5.5 numbers/post
     Bottom:    0.0 numbers/post
     → Include specific numbers — posts with data get 3-4x reach

  2. 🔴 [HIGH] Specificity Score
     Top posts: 18.9/100w
     Bottom:    2.6/100w
     → Name specific tools, companies, projects — not generic advice

  3. 🔴 [HIGH] Hook Type
     Top posts: Mostly 'contrarian' (50%)
     Bottom:    Mostly 'challenge' (50%)
     → Lead with contrarian hooks — they correlate with your best posts

📋 YOUR CONTENT BLUEPRINT:
  HOOK: Challenge conventional wisdom: 'Stop doing X. Here's why.'
  LENGTH: ~74 words, 14 sentences
  DATA: Include ~5 specific numbers/metrics
  VOICE: Personal — aim for 2+ first-person pronouns per 100 words
  CTA: End with a clear next step the reader can take

Research Basis

Source	Key Finding	How We Use It
Buffer 52M+ posts (2026)	Dwell time > likes; specificity = 3-4x reach	Specificity scoring + feature correlation
LinkedIn 360Brew data	Document posts = 596% more engagement than text; first 150 chars critical	Hook type classification
Reddit 1000-post study	Posting time = 730% diff; question titles underperform by 16%	Context notes in output (distribution vs content)
Stanford CS229	Text features + sentiment predict Reddit post popularity	12-feature extraction framework
LinkedIn 2026 benchmarks	6.60% engagement for docs, 2-4% for text; 3-5 weekly optimal	Platform-aware recommendations

Collecting Your Post Data

Export from LinkedIn Analytics → Content tab → CSV export, then convert to JSON format.

bash

# Use Reddit user history API
curl "https://www.reddit.com/user/USERNAME/submitted.json?limit=100" | python3 -c "
import json, sys
data = json.load(sys.stdin)
posts = [{'text': p['data']['title'] + ' ' + p['data'].get('selftext', ''),
          'platform': 'reddit',
          'engagement_rate': p['data']['upvote_ratio'] * 100,
          'impressions': p['data']['score']}
         for p in data['data']['children']]
json.dump(posts, sys.stdout, indent=2)
"

Manual

Create a JSON file with your posts and approximate engagement rates.

Technical Notes

Zero external dependencies — pure Python 3.7+ stdlib
Minimum 3 posts for meaningful analysis (8+ recommended)
Tier grouping: top 25%, middle 50%, bottom 25% by engagement_rate
Insight generation: only surfaces patterns with meaningful deltas (not noise)
JSON output: --format json for pipeline integration

Companion Skills

Skill	Relationship
`phy-content-humanizer-audit`	Checks AI signature before posting (this tool = analyzes after posting)
`phy-platform-rules-engine`	Checks platform-specific rules (this tool = analyzes content quality)
`phy-content-compound`	Builds content atom library (this tool = informs which atoms work best)

Author

Canlah AI — Run performance marketing without breaking your brand.

GitHub: github.com/PHY041
All Skills: clawhub.ai/PHY041