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Deep analysis of a specific segment including roles, topics, and past performance
Display high-level summary of audience segments, sizes, topics, and activity
Score 3 content variations and rank them with comparative analysis
Review calibration metrics, accuracy trends, and model confidence levels
Core skill for scoring a draft post with per-segment predictions and reasoning
| name | audience-deep-dive |
| description | Deep analysis of a specific segment including roles, topics, and past performance |
| version | 1.0.0 |
Purpose: Explore a single segment in detail to understand content preferences and engagement patterns.
{
"segment_name": "string (e.g., 'Executives', 'Technical Leaders')"
}
{
"segment": "Executives",
"segment_size": 150,
"activity_breakdown": {
"active": 108,
"occasional": 27,
"dormant": 15
},
"seniority_distribution": {
"VP": 64,
"C-Level": 42,
"Director": 38,
"Founder": 6
},
"top_roles": ["CEO", "CTO", "CFO", "VP Engineering", "VP Product"],
"industries": ["Tech", "Finance", "Healthcare"],
"key_characteristics": [
"High engagement rate (8.2% vs network avg 2.1%)",
"Value authenticity and insight",
"Rarely engage with promotional content",
"Post frequently about leadership and culture"
],
"topic_engagement": [
{
"topic": "Leadership",
"affinity": 0.92,
"observed_posts": 847,
"avg_engagement": 12,
"why_they_engage": "Stories about building teams, culture, managing hard times"
},
{
"topic": "Strategy",
"affinity": 0.87,
"observed_posts": 634,
"avg_engagement": 11,
"why_they_engage": "Long-form thoughts on company direction, market trends"
}
],
"your_past_performance": [
{
"post_topic": "Building teams",
"predicted_reactions": 18,
"actual_reactions": 22,
"error": 4,
"hook_strength": 78,
"status": "better_than_expected"
}
],
"content_recommendations": {
"high_performing_hooks": ["story", "personal", "question"],
"do_posts": [
"Write about leadership, team culture, fundraising",
"Use story or question hooks",
"Lead with vulnerability or contrarian take"
],
"dont_posts": [
"Avoid tutorials and how-tos",
"Avoid heavy emoji or casual tone",
"Avoid hard sales pitches"
]
},
"dormant_members": [
{
"name": "@Sarah_CEO",
"reason": "No engagement in 8 months, but historically high engagement",
"last_engagement": "Leadership content about building teams",
"reactivation_suggestion": "Post about team building or fundraising journey"
}
],
"segment_recommendations": [
"This segment is your most engaged. Focus on leadership and strategy content.",
"Consider creating a monthly 'founder/CEO insights' series."
]
}
def get_deep_dive(segment_name):
"""Main skill function."""
conn = get_db_connection()
try:
cursor = conn.cursor()
# Get segment
cursor.execute('SELECT id, name, estimated_size FROM audience_segments WHERE name = ?', (segment_name,))
seg = cursor.fetchone()
if not seg:
return {'error': f'Segment "{segment_name}" not found'}
segment_id = seg['id']
segment_size = seg['estimated_size']
# Activity breakdown
cursor.execute('''
SELECT activity_status, COUNT(*) as count
FROM connection_profiles
WHERE segment_id = ?
GROUP BY activity_status
''', (segment_id,))
activity = {row['activity_status']: row['count'] for row in cursor.fetchall()}
# Seniority distribution
cursor.execute('''
SELECT parsed_seniority, COUNT(*) as count
FROM connection_profiles
WHERE segment_id = ?
GROUP BY parsed_seniority
ORDER BY count DESC
''', (segment_id,))
seniority = {row['parsed_seniority']: row['count'] for row in cursor.fetchall()}
# Top roles
cursor.execute('''
SELECT parsed_role, COUNT(*) as count
FROM connection_profiles
WHERE segment_id = ?
GROUP BY parsed_role
ORDER BY count DESC
LIMIT 5
''', (segment_id,))
top_roles = [row['parsed_role'] for row in cursor.fetchall()]
# Industries
cursor.execute('''
SELECT DISTINCT parsed_industry
FROM connection_profiles
WHERE segment_id = ?
LIMIT 5
''', (segment_id,))
industries = [row['parsed_industry'] for row in cursor.fetchall()]
# Topic engagement
cursor.execute('''
SELECT topic_name, affinity_score, posts_count, avg_engagement
FROM segment_topic_affinity
WHERE segment_id = ?
ORDER BY affinity_score DESC
LIMIT 10
''', (segment_id,))
topic_engagement = []
for row in cursor.fetchall():
topic_engagement.append({
'topic': row['topic_name'],
'affinity': round(row['affinity_score'], 2),
'observed_posts': row['posts_count'],
'avg_engagement': round(row['avg_engagement'], 1),
'why_they_engage': get_topic_engagement_reason(segment_name, row['topic_name'])
})
# Key characteristics (heuristic)
characteristics = [
f"Activity level: {int(activity.get('active', 0) / segment_size * 100)}% active",
f"Top seniority: {list(seniority.keys())[0] if seniority else 'Unknown'}",
f"Primary industries: {', '.join(industries) if industries else 'Diverse'}",
f"Strongest topic affinity: {topic_engagement[0]['topic'] if topic_engagement else 'Unknown'} ({topic_engagement[0]['affinity']*100:.0f}%)" if topic_engagement else "No affinity data"
]
# Past performance with this segment
cursor.execute('''
SELECT draft_text, overall_score, predicted_reactions, actual_reactions
FROM predictions
WHERE json_extract(top_segments_json, '$[0].segment') = ?
ORDER BY scored_at DESC
LIMIT 5
''', (segment_name,))
past_performance = []
for row in cursor.fetchall():
if row['actual_reactions'] is not None:
error = row['predicted_reactions'] - row['actual_reactions']
past_performance.append({
'topic': row['draft_text'][:50] + '...',
'predicted_reactions': row['predicted_reactions'],
'actual_reactions': row['actual_reactions'],
'error': error,
'status': 'better_than_expected' if error < 0 else 'worse_than_expected'
})
# Content recommendations
recommendations = {
'high_performing_hooks': get_high_performing_hooks(cursor, segment_name),
'do_posts': get_do_recommendations(segment_name),
'dont_posts': get_dont_recommendations(segment_name)
}
# Dormant members
cursor.execute('''
SELECT name, headline, engagement_score
FROM connection_profiles
WHERE segment_id = ? AND activity_status = 'dormant'
ORDER BY engagement_score DESC
LIMIT 5
''', (segment_id,))
dormant_members = []
for row in cursor.fetchall():
dormant_members.append({
'name': row['name'],
'reason': f"No recent engagement, but historically active ({row['engagement_score']:.1f} avg engagement)",
'reactivation_suggestion': get_reactivation_suggestion(segment_name, row['headline'])
})
return {
'segment': segment_name,
'segment_size': segment_size,
'activity_breakdown': activity,
'seniority_distribution': seniority,
'top_roles': top_roles,
'industries': industries,
'key_characteristics': characteristics,
'topic_engagement': topic_engagement,
'your_past_performance': past_performance,
'content_recommendations': recommendations,
'dormant_members': dormant_members,
'segment_recommendations': [
f"This is a {get_segment_assessment(activity, segment_size)} segment. Focus on {topic_engagement[0]['topic'] if topic_engagement else 'relevant'} content.",
"Run /score to see how a draft performs with this segment specifically."
]
}
except Exception as e:
return {'error': f'Error: {str(e)}'}
finally:
conn.close()
def get_topic_engagement_reason(segment, topic):
"""Return why a segment engages with a topic."""
reasons = {
('Executives', 'Leadership'): 'Stories about building teams, culture, managing in hard times',
('Executives', 'Strategy'): 'Long-form thoughts on company direction, market trends',
('Technical Leaders', 'AI/ML'): 'Deep dives into models, algorithms, LLMs, RAG, transformers',
('Technical Leaders', 'Cloud'): 'Infrastructure, Kubernetes, serverless, DevOps patterns',
('Growth & Sales', 'Growth Hacking'): 'Metrics, experiments, scaling, acquisition strategies',
('Growth & Sales', 'Revenue'): 'Pricing, expansion, upsell, revenue models',
}
return reasons.get((segment, topic), f'Content about {topic}')
def get_high_performing_hooks(cursor, segment_name):
"""Get hooks that work best for this segment."""
hook_map = {
'Executives': ['story', 'personal', 'contrarian'],
'Technical Leaders': ['data-driven', 'question', 'listicle'],
'Growth & Sales': ['data-driven', 'story', 'listicle'],
'General Audience': ['story', 'question', 'personal']
}
return hook_map.get(segment_name, ['story', 'question'])
def get_do_recommendations(segment_name):
"""Get content recommendations for a segment."""
do_map = {
'Executives': [
"Write about leadership, team culture, fundraising, strategy",
"Use story or question hooks",
"Lead with vulnerability or contrarian take",
"Include real data, but always with business context"
],
'Technical Leaders': [
"Deep-dive into technical topics: AI/ML, Cloud, DevOps, Product",
"Lead with data or technical question",
"Include code examples, architecture diagrams, benchmarks",
"Challenge best practices with data-driven arguments"
],
'Growth & Sales': [
"Write about growth, GTM, revenue, scaling strategies",
"Lead with metrics or story",
"Include case studies, benchmarks, experimentation results",
"Focus on what worked and how to replicate it"
]
}
return do_map.get(segment_name, ["Post authentic, relevant content"])
def get_dont_recommendations(segment_name):
"""Get what NOT to post for a segment."""
dont_map = {
'Executives': [
"Avoid tutorials and how-tos (too junior-level)",
"Avoid heavy emoji or overly casual tone",
"Avoid hard sales pitches and promotional content",
"Avoid pure technical deep-dives without business context"
],
'Technical Leaders': [
"Avoid non-technical content (they skim quickly)",
"Avoid vague statements without data or examples",
"Avoid hard sells and promotional spam",
"Avoid leadership/business posts that ignore technical reality"
],
'Growth & Sales': [
"Avoid pure technical content (too specialized)",
"Avoid theoretical discussions without business outcomes",
"Avoid leadership-focused content without growth angle",
"Avoid unsubstantiated claims"
]
}
return dont_map.get(segment_name, ["Avoid low-quality or off-brand content"])
def get_reactivation_suggestion(segment_name, headline):
"""Get a suggestion to reactivate this dormant member."""
if 'engineer' in headline.lower():
return "Post about AI/ML or Cloud topics they likely care about"
elif 'executive' in headline.lower() or 'ceo' in headline.lower():
return "Share a founder story or leadership insight"
else:
return "Post about topics this person engaged with before"
def get_segment_assessment(activity, total_size):
"""Assess segment quality."""
active_pct = activity.get('active', 0) / total_size if total_size > 0 else 0
if active_pct > 0.7:
return "highly engaged"
elif active_pct > 0.5:
return "moderately engaged"
else:
return "low engagement"