Metadata
- name
- Platform Rules Engine
- description
- Social media pre-flight checker. Scans any draft post against 30+ platform-specific invisible rules and outputs PASS/WARN/FAIL per rule with exact fix suggestions. Like a linter for content. Covers Reddit (90/10 self-promo ratio, shadowban triggers, link-to-comment tracking), LinkedIn (360Brew AI detection, 60% external link penalty, engagement bait NLP filter, engagement pod detection), Twitter/X (150x author-reply multiplier, 30-min velocity window, link depression since March 2026, bookmark 10x signal), and HackerNews (Show HN format rules, tutorial downrank, clickbait title editing by dang). Research-backed with specific algorithm data. Zero external dependencies.
- license
- Apache-2.0
- homepage
- https://canlah.ai
- metadata
- {"author":"Canlah AI","version":"1.0.3"}
- tags
- ["social-media","content","reddit","linkedin","twitter","hackernews","pre-publish","content-quality"]
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) |
Author
Canlah AI — Run performance marketing without breaking your brand.
