text_tokens.py•4.81 kB
# src/chuk_mcp_linkedin/tokens/text_tokens.py
"""
Text formatting tokens for LinkedIn posts.
Based on 2025 performance data analysis of 1M+ posts.
"""
from typing import Any, Dict, Tuple
class TextTokens:
"""Design tokens for text formatting on LinkedIn"""
# LinkedIn character limits
MAX_LENGTH = 3000
TRUNCATION_POINT = 210 # "...see more" appears here
# Ideal length ranges for different post types
IDEAL_LENGTH: Dict[str, Tuple[int, int]] = {
"micro": (50, 150), # Quick update/question
"short": (150, 300), # Standard engagement post
"medium": (300, 800), # Thought leadership
"long": (800, 1500), # Deep dive analysis
"story": (1000, 3000), # Long-form narrative
}
# Line break styles for scannability
LINE_BREAKS: Dict[str, int] = {
"dense": 1, # Traditional paragraph
"readable": 2, # Standard spacing
"scannable": 3, # Easy to scan (recommended)
"dramatic": 5, # High visual impact
"extreme": 7, # Maximum white space
}
# Paragraph length guidelines
PARAGRAPH_LENGTH: Dict[str, Tuple[int, int]] = {
"tight": (1, 2), # 1-2 sentences
"standard": (2, 4), # 2-4 sentences
"loose": (4, 6), # 4-6 sentences
}
# Emoji usage formulas (emojis per word ratio)
EMOJI: Dict[str, float] = {
"none": 0.0,
"minimal": 0.01, # 1 per 100 words (~1-2 per post)
"moderate": 0.05, # 1 per 20 words (~3-5 per post)
"expressive": 0.1, # 1 per 10 words (~5-10 per post)
"heavy": 0.15, # 1 per 7 words (influencer style)
}
# Hashtag best practices
HASHTAGS: Dict[str, Any] = {
"count": {
"minimal": (1, 2),
"optimal": (3, 5), # Sweet spot based on 2025 data
"maximum": (5, 7),
"over_limit": 8, # Diminishing returns
},
"placement": {
"inline": "Within the text flow",
"mid": "After main content, before CTA",
"end": "At the very end (most common)",
"first_comment": "In first comment (keeps post clean)",
},
"strategy": {
"branded": "Company/personal brand tags",
"trending": "Current trending topics",
"niche": "Industry-specific hashtags",
"mixed": "Blend of all three (recommended)",
},
}
# Visual formatting symbols
SYMBOLS: Dict[str, str] = {
"arrow": "→",
"bullet": "•",
"checkmark": "✓",
"cross": "✗",
"lightning": "⚡",
"bulb": "💡",
"lightbulb": "💡",
"target": "🎯",
"pin": "📌",
"quote": "💬",
"calendar": "📅",
"checklist": "✓",
"checkbox": "☐",
"transformation": "🔄",
"poll": "📊",
"features": "✨",
"list": "📝",
}
# Chart-specific tokens
CHART_EMOJIS: Dict[str, str] = {
"bar": "📊",
"metrics": "📈",
"comparison": "⚖️",
"progress": "📊",
"ranking": "🏆",
"time": "⏱️",
"stats": "📊",
}
# Bar chart colors (emoji squares for LinkedIn compatibility)
BAR_COLORS: list[str] = ["🟦", "🟩", "🟨", "🟧", "🟥", "🟪", "🟫"]
# Indicators
INDICATORS: Dict[str, str] = {
"positive": "✅",
"negative": "❌",
"neutral": "⚪",
"warning": "⚠️",
"gold_medal": "🥇",
"silver_medal": "🥈",
"bronze_medal": "🥉",
}
# Progress bar characters
PROGRESS_BARS: Dict[str, str] = {
"filled": "█",
"empty": "░",
"partial": "▓",
}
# Separators for visual breaks
SEPARATORS: Dict[str, str] = {"line": "---", "dots": "• • •", "wave": "~", "heavy": "━━━"}
@classmethod
def get_length_range(cls, length_type: str) -> Tuple[int, int]:
"""Get min/max length for a post type"""
return cls.IDEAL_LENGTH.get(length_type, cls.IDEAL_LENGTH["medium"])
@classmethod
def get_line_break_count(cls, style: str) -> int:
"""Get number of line breaks for a style"""
return cls.LINE_BREAKS.get(style, 2)
@classmethod
def calculate_emoji_count(cls, word_count: int, level: str) -> int:
"""Calculate recommended emoji count based on word count"""
ratio = cls.EMOJI.get(level, 0.05)
return int(word_count * ratio)
@classmethod
def get_hashtag_count(cls, strategy: str) -> Tuple[int, int]:
"""Get recommended hashtag count range"""
result: Tuple[int, int] = cls.HASHTAGS["count"].get(
strategy, cls.HASHTAGS["count"]["optimal"]
)
return result