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#

bash

# 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)#

IDRuleWhat It CatchesSeverityR001Self-promo in openingProduct mention in first paragraph → instant downvotesHIGHR002Self-promotion ratio>10% promotional content violates 90/10 ruleHIGHR003External link in bodyReddit tracks link-to-comment ratio; >10% → spam flagMEDIUMR004Post length<50 words = low effort; >500 = TL;DR neededLOWR005No discussion promptNo question = no comments = no algorithm boostMEDIUMR006Clickbait languageCommunity actively downvotes hype/clickbaitMEDIUMR007Cross-posting signal"Also posted on LinkedIn" → mass-distribution flagHIGH

LinkedIn (8 rules)#

IDRuleWhat It CatchesSeverityL001External link penaltyLinks in body → 60% less reach (LinkedIn 2026)HIGHL002Engagement bait"Comment YES", "Tag a friend" → NLP filter penalizesHIGHL003AI content signals37 AI-flagged words → 360Brew 30% less reachHIGHL004Hashtag count>5 hashtags looks spammy; 0 = no categorizationMEDIUML005Hook strengthFirst 150 chars determine "See more" clicks → dwell timeHIGHL006Post length<30 words = low dwell timeMEDIUML007Long paragraphs>40 words/paragraph = poor mobile readabilityMEDIUML008Engagement pod signalsPod references → aggressive detection in 2026HIGH

Twitter/X (6 rules)#

IDRuleWhat It CatchesSeverityX001External link depressionLinks → near-zero engagement for non-Premium (since March 2026)HIGHX002Thread hook strengthWeak thread hook = rest of thread never distributedHIGHX003Character count>280 chars = truncated (non-Premium)HIGHX004Link placement strategyTip: author reply with link = 150x multiplierHIGHX005Bookmark potentialNo save-worthy content = misses 10x bookmark boostMEDIUMX006No engagement promptNo question = no replies = misses 27x reply multiplierMEDIUM

HackerNews (7 rules)#

IDRuleWhat It CatchesSeverityHN001Clickbait titleModerator (dang) will edit your titleHIGHHN002ALL CAPSExcessive capitalization → title editMEDIUMHN003Show HN missing URLShow HN requires a URLHIGHHN004Show HN content typeShow HN is for runnable things, not blog postsHIGHHN005Tutorial contentTutorials explicitly downranked by moderatorsMEDIUMHN006Self-promotionSelf-promo outside Show HN → flaggedHIGHHN007Technical depthLow technical depth → poor reception on HNMEDIUM

Algorithm Data Behind the Rules#

Platform SignalSourceDataLinkedIn external link penaltyLinkedIn 360Brew 2026~60% less reachLinkedIn AI content penaltyLinkedIn 360Brew 202630% less reach, 55% less engagementTwitter/X reply multiplierX algorithm source codeReply = 27x, Author reply = 150x, Bookmark = 10x, Like = 1xTwitter/X link penaltyX algorithm March 2026Non-Premium: near-zero engagement with linksTwitter/X half-lifeX algorithm analysisTweet loses 50% visibility every 6 hoursReddit timing impactUpvote.net 1000-post study730% difference based on posting time aloneReddit self-promoReddit sitewide rules90/10 ratio across total account historyHN tutorial downrankHN moderator policyTutorials "gratify intellectual curiosity less"

Example Output#

Bad Reddit post (HIGH RISK)#

text

  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)#

text

  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)#

text

  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#

SkillRelationshipphy-content-humanizer-auditDeep 8-dimension AI signature analysis (this tool = quick rule check)phy-post-forensicsPost-publish analysis (this tool = pre-publish prevention)phy-content-compoundContent atom library (use rules engine before publishing atom combinations)

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Author#

Canlah AI — Run performance marketing without breaking your brand.

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