Case 00938

phy-platform-rules-engine — Social Media Pre-Flight Checker

Each social platform has invisible rules. Break them and you get shadowbanned, de-ranked, or ignored — without knowing why. This tool scans your draft post against 30+ platform-specific rules before you publish.

Like a linter, but for social media posts.

Quick Start

# Check a Reddit post
echo "Your post text" | python3 ~/.claude/skills/phy-platform-rules-engine/scripts/platform_rules.py --platform reddit

# Check a LinkedIn post from file
python3 ~/.claude/skills/phy-platform-rules-engine/scripts/platform_rules.py --file draft.txt --platform linkedin

# Check a Twitter/X thread
python3 ~/.claude/skills/phy-platform-rules-engine/scripts/platform_rules.py --text "My tweet..." --platform twitter

# Check a HackerNews submission
python3 ~/.claude/skills/phy-platform-rules-engine/scripts/platform_rules.py --text "Show HN: ..." --platform hackernews

# JSON output
python3 ~/.claude/skills/phy-platform-rules-engine/scripts/platform_rules.py --file draft.txt --platform linkedin --format json

Rules by Platform

Reddit (7 rules)

ID	Rule	What It Catches	Severity
R001	Self-promo in opening	Product mention in first paragraph → instant downvotes	HIGH
R002	Self-promotion ratio	>10% promotional content violates 90/10 rule	HIGH
R003	External link in body	Reddit tracks link-to-comment ratio; >10% → spam flag	MEDIUM
R004	Post length	<50 words = low effort; >500 = TL;DR needed	LOW
R005	No discussion prompt	No question = no comments = no algorithm boost	MEDIUM
R006	Clickbait language	Community actively downvotes hype/clickbait	MEDIUM
R007	Cross-posting signal	"Also posted on LinkedIn" → mass-distribution flag	HIGH

LinkedIn (8 rules)

ID	Rule	What It Catches	Severity
L001	External link penalty	Links in body → 60% less reach (LinkedIn 2026)	HIGH
L002	Engagement bait	"Comment YES", "Tag a friend" → NLP filter penalizes	HIGH
L003	AI content signals	37 AI-flagged words → 360Brew 30% less reach	HIGH
L004	Hashtag count	>5 hashtags looks spammy; 0 = no categorization	MEDIUM
L005	Hook strength	First 150 chars determine "See more" clicks → dwell time	HIGH
L006	Post length	<30 words = low dwell time	MEDIUM
L007	Long paragraphs	>40 words/paragraph = poor mobile readability	MEDIUM
L008	Engagement pod signals	Pod references → aggressive detection in 2026	HIGH

Twitter/X (6 rules)

ID	Rule	What It Catches	Severity
X001	External link depression	Links → near-zero engagement for non-Premium (since March 2026)	HIGH
X002	Thread hook strength	Weak thread hook = rest of thread never distributed	HIGH
X003	Character count	>280 chars = truncated (non-Premium)	HIGH
X004	Link placement strategy	Tip: author reply with link = 150x multiplier	HIGH
X005	Bookmark potential	No save-worthy content = misses 10x bookmark boost	MEDIUM
X006	No engagement prompt	No question = no replies = misses 27x reply multiplier	MEDIUM

HackerNews (7 rules)

ID	Rule	What It Catches	Severity
HN001	Clickbait title	Moderator (dang) will edit your title	HIGH
HN002	ALL CAPS	Excessive capitalization → title edit	MEDIUM
HN003	Show HN missing URL	Show HN requires a URL	HIGH
HN004	Show HN content type	Show HN is for runnable things, not blog posts	HIGH
HN005	Tutorial content	Tutorials explicitly downranked by moderators	MEDIUM
HN006	Self-promotion	Self-promo outside Show HN → flagged	HIGH
HN007	Technical depth	Low technical depth → poor reception on HN	MEDIUM

Algorithm Data Behind the Rules

Platform Signal	Source	Data
LinkedIn external link penalty	LinkedIn 360Brew 2026	~60% less reach
LinkedIn AI content penalty	LinkedIn 360Brew 2026	30% less reach, 55% less engagement
Twitter/X reply multiplier	X algorithm source code	Reply = 27x, Author reply = 150x, Bookmark = 10x, Like = 1x
Twitter/X link penalty	X algorithm March 2026	Non-Premium: near-zero engagement with links
Twitter/X half-life	X algorithm analysis	Tweet loses 50% visibility every 6 hours
Reddit timing impact	Upvote.net 1000-post study	730% difference based on posting time alone
Reddit self-promo	Reddit sitewide rules	90/10 ratio across total account history
HN tutorial downrank	HN moderator policy	Tutorials "gratify intellectual curiosity less"

Example Output

Bad Reddit post (HIGH RISK)

  Score    : 20/100 🔴 HIGH RISK
  Rules    : 2 PASS, 3 WARN, 2 FAIL

  🔴 [R001] Self-promo in opening — FAIL
     → Move product mention to a reply comment. Lead with value first.
  🔴 [R007] Cross-posting signal — FAIL
     → Remove cross-posting references. Each platform should feel native.

Bad LinkedIn post (HIGH RISK)

  Score    : 5/100 🔴 HIGH RISK
  Rules    : 3 PASS, 2 WARN, 3 FAIL

  🔴 [L001] External link penalty — FAIL (60% less reach)
  🔴 [L002] Engagement bait detection — FAIL (NLP filter)
  🔴 [L003] AI content signals — FAIL (8 AI words found)

Clean LinkedIn post (CLEAR)

  Score    : 100/100 ✅ CLEAR
  Rules    : 8 PASS, 0 WARN, 0 FAIL

Technical Notes

• Zero external dependencies — pure Python 3.7+ stdlib
• Exit codes: 0 (CLEAR), 1 (warnings only), 2 (has failures)
• JSON output: --format json for pipeline integration
• 37 AI-flagged words: same database as phy-content-humanizer-audit
• 10 engagement bait patterns: regex-based, matches LinkedIn's NLP filter targets

Companion Skills

Skill	Relationship
phy-content-humanizer-audit	Deep 8-dimension AI signature analysis (this tool = quick rule check)
phy-post-forensics	Post-publish analysis (this tool = pre-publish prevention)
phy-content-compound	Content atom library (use rules engine before publishing atom combinations)