linkedin-account-diagnostic

name	linkedin-account-diagnostic
description	Comprehensive LinkedIn account diagnostic for early-stage creators. Analyzes posting performance, algorithm health, content mix, engagement network, and provides actionable fix plan with personalized inner circle recommendations.

LinkedIn Account Diagnostic & Growth Prescription

Comprehensive diagnostic system for any LinkedIn account, especially early-stage creators (0-5K followers). Performs data-driven analysis of posting performance, algorithm health signals, content strategy, engagement network, and delivers a personalized growth prescription with curated inner circle recommendations.

When to Use

• "Diagnose my LinkedIn account"
• "Why is my LinkedIn reach declining?"
• "Audit my LinkedIn content strategy"
• "Help me fix my LinkedIn engagement"
• "Build me a LinkedIn growth plan"
• "Find me an engagement inner circle"
• Any request about improving LinkedIn performance, reach, or engagement

Required Context (ask if not provided)

1. LinkedIn profile URL (or use currently logged-in profile)
2. Brief self-description: What do you do? What's your role/industry?
3. Goal: What do you want LinkedIn to do for you? (Options: build personal brand / generate leads / job search / recruit / thought leadership / community building)
4. Target audience: Who do you want to reach? (roles, industries, seniority levels)
5. Time period to analyze: Default last 30 days, minimum 2 weeks of posting history

Diagnostic Framework Overview

The diagnostic has 5 modules. Run ALL of them for a complete diagnosis:

Module	What It Measures	Output
A. Algorithm Health Score	Whether LinkedIn's algorithm is helping or throttling you	Score 0-100 + death spiral check
B. Content Strategy Audit	Content mix, format effectiveness, posting rhythm	Content scorecard + fix plan
C. Engagement Network Analysis	Who engages with you, network density, reciprocity	Network health map
D. Profile-Content Alignment	Does your profile match what you post about?	Alignment score + gaps
E. Growth Prescription	Personalized daily/weekly action plan + inner circle list	Actionable checklist + curated names

---

Module A: Algorithm Health Score

Step 1: Collect Post Performance Data

Navigate to the user's recent activity and collect data for every post in the analysis period.

// Navigate to user's activity page
var page = await browser.newtab("https://www.linkedin.com/in/USERNAME/recent-activity/all/")
await page.wait({ waitTime: 3 })
var snap = await page.snapshot()

For each post, extract:

• Date & time posted
• Content type: text-only / image / video / carousel-PDF / poll / article
• Content category: value-insight / story-learning / product-promotion / announcement / repost / engagement-bait
• First 2 lines (the "hook")
• Full content (click "see more" if needed)
• Impressions (if visible via analytics)
• Reactions count (likes, celebrates, etc.)
• Comments count
• Reposts count
• Whether post contains external links
• Hashtags used
• Whether it's a repost vs original content

Store in structured format:

var postData = []
// For each post found:
postData.push({
  date: "2026-04-01",
  dayOfWeek: "Tuesday",
  timePosted: "9:00 AM PST",  // estimate from time indicators
  contentType: "text",  // text / image / video / carousel / poll
  contentCategory: "product",  // value / story / product / announcement / repost
  hook: "First two lines of the post...",
  fullContent: "Full post text...",
  hasExternalLink: true,
  hashtagCount: 5,
  isOriginal: true,  // false if repost
  impressions: 630,  // null if not accessible
  reactions: 0,
  comments: 0,
  reposts: 0,
  engagementRate: 0,  // (reactions + comments + reposts) / impressions * 100
})

Step 2: Calculate Algorithm Health Metrics

var algorithmHealth = {
  // Core metrics
  totalPosts: postData.length,
  avgImpressions: 0,
  avgEngagementRate: 0,
  impressionTrend: "declining",  // rising / stable / declining
  
  // Death spiral indicators
  zeroReactionPosts: 0,          // Posts with 0 reactions
  zeroReactionRate: 0,           // % of posts with 0 reactions
  impressionDeclineRate: 0,      // % decline from first post to last post
  lowestImpression: 0,
  highestImpression: 0,
  
  // Algorithm signal scores (each 0-20, total = 100)
  engagementScore: 0,            // Based on avg engagement rate
  consistencyScore: 0,           // Based on posting regularity
  contentMixScore: 0,            // Based on content category diversity
  formatScore: 0,                // Based on content type variety
  linkPenaltyScore: 0,           // Based on external link usage
}

Scoring Rubric:

Engagement Score (0-20):

Avg Engagement Rate	Score
> 5%	20
3-5%	16
2-3%	12
1-2%	8
0.5-1%	4
< 0.5%	0

Consistency Score (0-20):

Posting Pattern	Score
Daily weekday posts, even spacing	20
4-5x/week, mostly even	16
3-4x/week, some gaps	12
2-3x/week, irregular	8
< 2x/week or burst-then-disappear	4
Very irregular, multi-day gaps	0

Content Mix Score (0-20):

Content Mix	Score
Close to 4:2:1 (value:story:product)	20
Some variety, slightly off ratio	14
Mostly one type but not all	8
100% one category (especially product)	0

Format Score (0-20):

Format Variety	Score
Mix of text, image, video, carousel	20
2-3 formats used	14
Mostly text with occasional image	8
100% text-only	4

Link Penalty Score (0-20):

External Link Usage	Score
0% of posts have links in body	20
< 20% have links (links in comments)	16
20-40% have links	10
40-60% have links	5
> 60% have links in post body	0

Total Algorithm Health Score = Sum of all 5 scores (0-100)

Step 3: Death Spiral Detection

Check for the "Algorithm Death Spiral" pattern:

DEATH SPIRAL CONFIRMED if ANY of these are true:
1. Zero-reaction rate > 50% (more than half your posts get 0 reactions)
2. Impression trend shows > 50% decline from first post to most recent
3. Most recent post impressions < 200 (algorithm has essentially throttled you)
4. Average engagement rate < 0.5% across all posts

If death spiral is detected, flag it prominently in the report:

⚠️ DEATH SPIRAL DETECTED
Your account has entered an algorithmic death spiral. This means:
- LinkedIn is actively reducing your content distribution
- Each new post gets shown to fewer people than the last
- Without intervention, your reach will continue approaching zero

This is FIXABLE, but requires immediate strategy changes (see Module E).

---

Module B: Content Strategy Audit

Step 1: Content Mix Analysis

Categorize every post and calculate the actual ratio:

Category	Count	Actual %	Ideal %	Gap
Value/Insight	X	X%	57%	-X%
Story/Learning	X	X%	29%	-X%
Product/CTA	X	X%	14%	+X%
Repost (others' content)	X	X%	0%	+X%

Step 2: Hook Quality Assessment

For each post, evaluate the first 2 lines (the "hook"):

Strong hook patterns (score 8-10):

• Opens with a specific number/data point
• Creates tension or curiosity
• States a contrarian opinion
• Starts with a personal story moment
• Asks a provocative question

Medium hook patterns (score 4-7):

• Reasonable opening but generic
• States a fact without tension
• Mildly interesting but not scroll-stopping

Weak hook patterns (score 0-3):

• "Excited to share..." / "Thrilled to announce..."
• "AI is transforming..." / "The future of..."
• Generic industry statements
• Starts with a link or tag
• Product pitch in the first line

Calculate average hook score across all posts.

Step 3: Posting Rhythm Analysis

Map out the posting calendar:

Week 1: Mon[ ] Tue[X] Wed[ ] Thu[XX] Fri[ ] Sat[ ] Sun[ ]
Week 2: Mon[ ] Tue[ ] Wed[ ] Thu[ ] Fri[ ] Sat[ ] Sun[ ]
Week 3: Mon[X] Tue[ ] Wed[X] Thu[ ] Fri[ ] Sat[ ] Sun[ ]

Flag issues:

• ❌ Multiple posts on the same day (self-cannibalization)
• ❌ Gaps of 3+ days (algorithm forgets you)
• ❌ Weekend-only posting (lowest reach days)
• ❌ No consistent time of day (algorithm can't predict you)
• ✅ Consistent weekday posting at similar times

Step 4: Anti-Pattern Detection

Check for these algorithm killers:

• External links in post body (not in first comment)
• More than 5 hashtags per post
• Editing posts within 10 minutes of publishing
• Posts that are purely reshares with no added commentary
• Posts with no CTA / conversation prompt at the end
• Posts longer than 1500 characters without formatting/breaks
• Emoji overload (> 5 emojis per post)

Step 5: Content Scorecard

Compile into a single scorecard:

CONTENT STRATEGY SCORECARD
===========================
Content Mix Balance:     [X/10]  (closeness to 4:2:1 ratio)
Hook Quality Average:    [X/10]  (avg hook score)
Posting Consistency:     [X/10]  (regularity + spacing)
Format Variety:          [X/10]  (types of content used)
Anti-Pattern Violations: [X/10]  (fewer violations = higher score)
---
TOTAL:                   [X/50]

---

Module C: Engagement Network Analysis

Step 1: Map Current Engagement Sources

Go through the user's recent posts and identify:

Who reacts to your posts:

var engagers = {}
// For each post, check who liked/commented
// Build frequency map:
// engagers["Person Name"] = { reactions: X, comments: X, totalEngagements: X }

Who you engage with:

// Check user's recent comments activity
await page.goto({ url: "https://www.linkedin.com/in/USERNAME/recent-activity/comments/" })
// Map who the user comments on

Step 2: Network Health Metrics

var networkHealth = {
  // Engagement circle size
  uniqueEngagers30d: 0,          // Unique people who engaged with your posts
  repeatEngagers: 0,             // People who engaged 2+ times
  superEngagers: 0,              // People who engaged 5+ times
  
  // Reciprocity
  peopleYouEngageWith: 0,        // People whose posts you comment on
  mutualEngagers: 0,             // People in both lists
  reciprocityRate: 0,            // mutual / (total unique engagers + people you engage with)
  
  // Network composition
  firstDegreeConnections: 0,     // Total 1st degree connections
  activeConnections: 0,          // Connections who posted in last 30 days
  connectionActivityRate: 0,     // active / total
  
  // Inner circle status
  hasInnerCircle: false,         // true if 5+ people engage on > 50% of posts
  innerCircleSize: 0,
  innerCircleMembers: [],
}

Step 3: Connection Count Check

// Navigate to connections page
await page.goto({ url: "https://www.linkedin.com/mynetwork/invite-connect/connections/" })
await page.wait({ waitTime: 3 })
// Extract total connection count

Connection count benchmarks for early creators:

Connections	Status	Impact
< 100	Critical	Algorithm has almost no one to show your posts to
100-300	Building	Minimum viable network, still limited distribution
300-500	Functional	Algorithm has enough seeds for initial distribution
500-1000	Growing	Good base for organic growth
1000+	Strong	Sufficient network for viral potential

Step 4: Engagement Circle Diagnosis

Present findings:

ENGAGEMENT NETWORK HEALTH
============================
Unique engagers (30d):       [X]
Repeat engagers:             [X] (engaged 2+ times)
Inner circle (engaged >50%): [X] people
Connection count:            [X]
Reciprocity rate:            [X]%

DIAGNOSIS:
[One of the following]
- 🔴 CRITICAL: No engagement circle. You're posting into a void.
- 🟡 WEAK: Small/inconsistent engagement. Need to actively build relationships.
- 🟢 HEALTHY: Active engagement circle supporting your content.

---

Module D: Profile-Content Alignment Check

Step 1: Extract Profile Positioning

await page.goto({ url: "https://www.linkedin.com/in/USERNAME/" })
await page.wait({ waitTime: 3 })
// Extract: headline, about section, current role, featured section
var profilePositioning = {
  headline: "...",
  about: "...",
  currentRole: "...",
  featuredContent: "...",
  topSkills: [...],
}

Step 2: Compare Profile vs Content Themes

Check if what the user POSTS about matches what their PROFILE says they do.

Misalignment examples:

• Profile says "Software Engineer" but posts are all about marketing → mismatch
• Profile says "AI Startup Founder" and posts about AI GTM insights → aligned
• Profile has no "About" section → missed opportunity

Step 3: Alignment Score

PROFILE-CONTENT ALIGNMENT
============================
Headline matches content themes:  [Yes/Partial/No]
About section exists:              [Yes/No]
About section matches content:     [Yes/Partial/No]
Featured section utilized:         [Yes/No]
Current role clear:                [Yes/No]
---
ALIGNMENT SCORE: [X/10]

---

Module E: Growth Prescription

Step 1: Synthesize All Findings

Compile the priority issues from Modules A-D, ranked by impact:

PRIORITY ISSUES (ranked by impact on growth)
=============================================
🔴 P0 (Fix Immediately):
  1. [e.g., "100% product content - algorithm is penalizing you"]
  2. [e.g., "Zero engagement circle - no one sees your posts in the first 60 min"]
  
🟡 P1 (Fix This Week):
  3. [e.g., "Inconsistent posting rhythm - algorithm can't predict you"]
  4. [e.g., "Weak hooks - people scroll past your content"]

🟢 P2 (Optimize Over Time):
  5. [e.g., "No video/carousel content - missing 2-5x reach multiplier"]
  6. [e.g., "Profile headline doesn't match content themes"]

Step 2: Daily Action Plan

Generate a personalized daily checklist based on the user's specific issues:

YOUR DAILY LINKEDIN ROUTINE (30-45 min)
=========================================

BEFORE posting (15 min):
□ Comment on 8-10 people's posts (from your Inner Circle list below)
  - Write 2-3 sentence substantive comments (NOT "great post!")
  - Reference something specific from their post
  - This warms up the algorithm and puts your name in their audience's feed

POSTING (5 min):
□ Publish 1 post (weekdays only)
  - Best times: [personalized based on their audience timezone]
  - Follow the 4:2:1 content mix ratio
  - Use the hook templates from your Content Fix Plan
  - No external links in post body (put in first comment)
  - End with a question or conversation prompt

AFTER posting (15 min, within first 60 min):
□ Reply to every comment on your post
  - Write 2-3 sentence replies (not just "thanks!")
  - Ask follow-up questions to extend the thread
  - Longer comment threads = more algorithmic distribution

THROUGHOUT the day (5 min):
□ Like 10-15 posts from your network
□ Accept/send 5 connection requests (with personalized notes)

Step 3: Content Fix Plan

Based on their specific content issues:

YOUR CONTENT FIX PLAN
========================

Current content mix: [actual ratio]
Target content mix: 4:2:1 (Value : Story : Product)

NEXT 7 POSTS SHOULD BE:
Post 1: [Value/Insight] - Topic suggestion based on their expertise
Post 2: [Value/Insight] - Different angle on their domain
Post 3: [Story/Learning] - Personal experience or lesson
Post 4: [Value/Insight] - Industry observation or framework  
Post 5: [Value/Insight] - How-to or practical tip
Post 6: [Story/Learning] - Behind-the-scenes or building update
Post 7: [Product/CTA] - Product mention in context of solving a real problem

HOOK TEMPLATES for your niche:
1. "[Number] [time period]. [Bigger number] [things done]. [Surprising result]."
2. "Most [your audience] think [common belief]. Here's what actually works:"
3. "I spent [time] doing [activity]. The #1 lesson nobody talks about:"
4. "The biggest mistake I see [your audience] making on LinkedIn:"
5. "[Controversial opinion about your industry]. Here's why:"

Step 4: Inner Circle Builder (CRITICAL)

This is the most important module. Based on the user's self-description, goals, and target audience, build a personalized inner circle recommendation.

Ask the user:

1. What industry/niche are you in?
2. What's your target audience?
3. Any geographic/language preferences? (e.g., Chinese professionals, Bay Area, etc.)
4. What follower range do you want to target? (Default: 1K-50K - big enough to have reach, small enough to reciprocate)

Then search LinkedIn to build 3 tiers:

// Search for potential inner circle members
var searchQueries = [
  // Based on user's industry + "content creator" or "building in public"
  "[user's industry] + [user's niche] + active LinkedIn poster",
  // Based on user's target audience peers
  "[similar role] + [similar company stage] + [location if specified]",
  // Based on complementary roles
  "[adjacent roles that would benefit from mutual engagement]",
]

// For each search:
await page.goto({ url: "https://www.linkedin.com/search/results/people/?keywords=" + encodeURIComponent(query) })
await page.wait({ waitTime: 3 })

Qualification criteria for inner circle candidates:

• ✅ Posts original content at least 2x/week (not just reposts)
• ✅ Follower count in the sweet spot (1K-50K default, adjustable)
• ✅ Engages with others' content (has comments activity)
• ✅ Relevant to user's industry/niche
• ✅ Similar stage or slightly ahead (relatable, likely to reciprocate)
• ❌ Skip people who only repost without commentary
• ❌ Skip people at big corporations who post corporate content
• ❌ Skip people with 100K+ followers (unlikely to reciprocate with a small account)
• ❌ Skip inactive accounts (no posts in 2+ weeks)

Visit each candidate's profile and verify:

// For each candidate
await page.goto({ url: "https://www.linkedin.com/in/SLUG/recent-activity/all/" })
await page.wait({ waitTime: 3 })
// Check:
// 1. Posting frequency (at least 2x/week)
// 2. Content is original (not just reshares)
// 3. Gets engagement (comments on their posts)
// 4. Industry relevance

Output the Inner Circle list in 3 tiers:

YOUR RECOMMENDED INNER CIRCLE
================================

Tier 1: Core Support Circle (8-10 people)
Engage with EVERY post they make. Goal: build mutual support relationship.
These are people at your level or slightly ahead, most likely to reciprocate.

| # | Name | Profile URL | Role & Company | Followers | Why They're a Good Fit | Suggested First Comment |
|---|------|------------|----------------|-----------|----------------------|------------------------|
| 1 | ... | linkedin.com/in/... | ... | ... | ... | ... |
| 2 | ... | ... | ... | ... | ... | ... |
...

Tier 2: Aspirational Circle (10-15 people)
Engage with their posts 2-3x/week. These are slightly bigger accounts whose audience overlaps with your target.

| # | Name | Profile URL | Role & Company | Followers | Why They're a Good Fit |
|---|------|------------|----------------|-----------|----------------------|
| 1 | ... | ... | ... | ... | ... |
...

Tier 3: Broader Network (20-30 people)
Engage occasionally (1-2x/week). These are industry voices whose content you genuinely find interesting.

| # | Name | Profile URL | Role & Company | Followers | Why They're a Good Fit |
|---|------|------------|----------------|-----------|----------------------|
| 1 | ... | ... | ... | ... | ... |
...

For each Tier 1 person, also provide:

• A suggested opening DM: "Hey [Name], I'm [brief intro]. I've been enjoying your posts about [specific topic]. Would love to connect and support each other's content."
• A suggested first comment on their next post (specific to their content style)

Step 5: Growth Milestones

Provide personalized timeline based on their current Algorithm Health Score:

YOUR GROWTH MILESTONES
========================

Starting point: Algorithm Health Score [X/100]
Current connections: [X]
Current avg engagement: [X]%

Week 1-2: REPAIR PHASE
Goal: Break the death spiral / establish baseline
□ Post 5x value content (zero product mentions)
□ Comment on 50+ posts (8-10/day)
□ Send 30 connection requests (to inner circle candidates)
□ Target: 3-5 engagements per post
□ Metric to track: Are reactions going UP from post to post?

Week 3-4: BUILD PHASE
Goal: Establish posting rhythm + engagement circle
□ Consistent weekday posting (same time each day)
□ 10-15 mutual engagement relationships forming
□ Target: 10-20 engagements per post
□ Metric to track: Do you have repeat engagers?

Week 5-8: SCALE PHASE
Goal: Algorithm starts distributing beyond 1st degree
□ Content starts reaching 2nd degree connections
□ Inbound connection requests increasing
□ Target: 30-50 engagements per post
□ Metric to track: Impressions trend (should be rising)

Week 8-12: ESTABLISH PHASE
Goal: Become a recognized voice in your niche
□ Stable inner circle (20+ people engage on most posts)
□ Occasional "breakout" posts (1000+ impressions)
□ Can start mixing in more product/promotional content
□ Target: consistent 2-5% engagement rate

---

Final Report Format — Professional PDF Report

After completing all 5 diagnostic modules, generate a polished, visually professional PDF report using ReportLab. The report should look like a premium consulting deliverable — NOT a raw text dump.

PDF Report Architecture (7 pages)

Page	Content	Design Notes
1	Cover + Executive Summary	Teal accent band header, 4 score cards (Algorithm/Content/Alignment/Engagers), 4 key metrics row, overall assessment text
2	Module A: Algorithm Health	Score card + detailed metrics table (Your Value vs Benchmark vs Status)
3	Module B: Content Strategy	Score card + horizontal bar charts for content mix (actual vs ideal, teal=healthy, coral=over-indexed) + format distribution table + hook quality analysis
4	Module C: Engagement Network	Network status badge + metrics table + top engagers table (teal header) + critical gap analysis
5	Module D + E: Profile Alignment & Growth Prescription	Alignment score + profile issues + P0/P1/P2 priority issues with color coding (red/amber/green)
6	Inner Circle Recommendations	3-tier table (Tier 1 teal header, Tier 2 indigo header, Tier 3 gray header) with Name, Followers, Topics, Why
7	4-Week Growth Milestones + Footer	Week 1-4 milestone cards with colored left borders (coral→amber→teal→green) + Sai branding footer

PDF Generation Code

Use this Python template with ReportLab. Replace the mock report_data dictionary with actual diagnostic data collected from Modules A-E.

import os, json, math
from datetime import datetime
from reportlab.lib import colors
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import mm, inch
from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_RIGHT
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    PageBreak, HRFlowable
)
from reportlab.graphics.shapes import Drawing, Rect, String, Line

# ============================================================
# COLOR PALETTE
# ============================================================
NAVY = colors.HexColor("#0F172A")
DARK_BLUE = colors.HexColor("#1E293B")
TEAL = colors.HexColor("#0D9488")
TEAL_LIGHT = colors.HexColor("#CCFBF1")
TEAL_BG = colors.HexColor("#F0FDFA")
CORAL = colors.HexColor("#E8705A")
CORAL_LIGHT = colors.HexColor("#FEF2F0")
AMBER = colors.HexColor("#F59E0B")
AMBER_LIGHT = colors.HexColor("#FFFBEB")
GREEN = colors.HexColor("#10B981")
GREEN_LIGHT = colors.HexColor("#ECFDF5")
RED = colors.HexColor("#EF4444")
RED_LIGHT = colors.HexColor("#FEF2F2")
GRAY_50 = colors.HexColor("#F8FAFC")
GRAY_100 = colors.HexColor("#F1F5F9")
GRAY_200 = colors.HexColor("#E2E8F0")
GRAY_500 = colors.HexColor("#64748B")
GRAY_700 = colors.HexColor("#334155")
WHITE = colors.white

# ============================================================
# DATA — Replace with actual diagnostic results
# ============================================================
report_data = {
    "generated_date": datetime.now().strftime("%B %d, %Y"),
    "account_name": "ACCOUNT_NAME",
    "account_slug": "ACCOUNT_SLUG",
    "account_headline": "HEADLINE",
    "account_followers": 0,
    "account_connections": 0,
    "analysis_period": "PERIOD",
    "total_posts_analyzed": 0,
    # Module A
    "algorithm_score": 0,       # 0-100
    "death_spiral": False,
    "avg_impressions": 0,
    "avg_engagement_rate": 0.0,
    "impression_trend": "stable",
    "best_post_impressions": 0,
    "worst_post_impressions": 0,
    "avg_reactions": 0,
    "avg_comments": 0,
    "external_link_penalty": False,
    # Module B
    "content_score": 0,         # 0-50
    "content_mix": {
        "value_insight": {"count": 0, "pct": 0, "ideal": 57},
        "story_learning": {"count": 0, "pct": 0, "ideal": 29},
        "product_promo": {"count": 0, "pct": 0, "ideal": 14},
        "repost": {"count": 0, "pct": 0, "ideal": 0},
    },
    "format_mix": {"text_only": 0, "image": 0, "video": 0, "carousel": 0, "poll": 0},
    "avg_hook_score": 0.0,
    "posting_consistency": "irregular",
    "best_posting_time": "",
    # Module C
    "unique_engagers": 0,
    "repeat_engagers": 0,
    "engagement_reciprocity": 0.0,
    "top_engagers": [],  # [{"name": "", "engagements": 0, "reciprocated": False}]
    "network_health": "weak",   # weak / moderate / healthy
    # Module D
    "alignment_score": 0,       # 0-10
    "profile_issues": [],       # ["issue1", "issue2"]
    # Module E
    "priority_issues": [],      # [{"level": "P0", "issue": "...", "fix": "..."}]
    "inner_circle": [],         # [{"tier": 1, "name": "", "url": "", "followers": "", "topic": "", "why": ""}]
    "milestones": {"week1": "", "week2": "", "week3": "", "week4": ""},
}

d = report_data

# ============================================================
# HELPERS
# ============================================================
def score_color(score, max_s):
    pct = score / max_s
    if pct >= 0.7: return GREEN
    elif pct >= 0.4: return AMBER
    else: return RED

def score_bg(score, max_s):
    pct = score / max_s
    if pct >= 0.7: return GREEN_LIGHT
    elif pct >= 0.4: return AMBER_LIGHT
    else: return RED_LIGHT

def score_label(score, max_s):
    pct = score / max_s
    if pct >= 0.7: return "Healthy"
    elif pct >= 0.4: return "Needs Work"
    else: return "Critical"

def make_score_card(score, max_s, label, width=120, height=90):
    drawing = Drawing(width, height)
    col = score_color(score, max_s)
    bg = score_bg(score, max_s)
    drawing.add(Rect(0, 0, width, height, rx=8, ry=8, fillColor=bg, strokeColor=None))
    drawing.add(String(width/2, height-36, f"{score}/{max_s}",
                 fontSize=24, fontName='Helvetica-Bold', fillColor=col, textAnchor='middle'))
    drawing.add(String(width/2, 12, label,
                 fontSize=9, fontName='Helvetica', fillColor=GRAY_500, textAnchor='middle'))
    return drawing

def make_content_bar(actual_pct, ideal_pct, label, width=340, height=28):
    drawing = Drawing(width, height)
    bar_x, bar_w, bar_h = 90, width - 100, 10
    bar_y = (height - bar_h) / 2
    drawing.add(String(0, bar_y+1, label, fontSize=9, fontName='Helvetica', fillColor=GRAY_700))
    drawing.add(Rect(bar_x, bar_y, bar_w, bar_h, fillColor=GRAY_100, strokeColor=None, rx=4, ry=4))
    actual_w = max(2, bar_w * actual_pct / 100)
    bar_col = CORAL if actual_pct > ideal_pct * 1.5 else TEAL
    drawing.add(Rect(bar_x, bar_y, actual_w, bar_h, fillColor=bar_col, strokeColor=None, rx=4, ry=4))
    ideal_x = bar_x + bar_w * ideal_pct / 100
    drawing.add(Line(ideal_x, bar_y-3, ideal_x, bar_y+bar_h+3, strokeColor=GRAY_500, strokeWidth=1.5, strokeDashArray=[2,2]))
    drawing.add(String(bar_x + actual_w + 4, bar_y+1, f"{actual_pct}%", fontSize=8, fontName='Helvetica-Bold', fillColor=bar_col))
    return drawing

def styled_table(data, col_widths, header_color=NAVY):
    """Create a consistently styled table."""
    t = Table(data, colWidths=col_widths)
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), header_color),
        ('TEXTCOLOR', (0,0), (-1,0), WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('FONTSIZE', (0,0), (-1,-1), 9),
        ('ALIGN', (1,0), (-1,-1), 'CENTER'),
        ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
        ('GRID', (0,0), (-1,-1), 0.5, GRAY_200),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [WHITE, GRAY_50]),
        ('TOPPADDING', (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('LEFTPADDING', (0,0), (-1,-1), 8),
        ('RIGHTPADDING', (0,0), (-1,-1), 8),
    ]))
    return t

# ============================================================
# BUILD PDF
# ============================================================
OUTPUT_PATH = os.path.join("artifacts", "linkedin-diagnostic-report.pdf")

doc = SimpleDocTemplate(OUTPUT_PATH, pagesize=A4,
    leftMargin=20*mm, rightMargin=20*mm, topMargin=18*mm, bottomMargin=18*mm)

styles = getSampleStyleSheet()
styles.add(ParagraphStyle('Title2', fontSize=28, leading=34, textColor=NAVY, fontName='Helvetica-Bold', spaceAfter=4*mm))
styles.add(ParagraphStyle('Subtitle', fontSize=12, leading=16, textColor=GRAY_500, fontName='Helvetica', spaceAfter=8*mm))
styles.add(ParagraphStyle('Section', fontSize=18, leading=22, textColor=NAVY, fontName='Helvetica-Bold', spaceBefore=6*mm, spaceAfter=4*mm))
styles.add(ParagraphStyle('Subsection', fontSize=13, leading=17, textColor=DARK_BLUE, fontName='Helvetica-Bold', spaceBefore=4*mm, spaceAfter=2*mm))
styles.add(ParagraphStyle('Body', fontSize=10, leading=14, textColor=GRAY_700, fontName='Helvetica', spaceAfter=2*mm))
styles.add(ParagraphStyle('Small', fontSize=8.5, leading=12, textColor=GRAY_500, fontName='Helvetica'))
styles.add(ParagraphStyle('Score', fontSize=36, leading=40, textColor=TEAL, fontName='Helvetica-Bold', alignment=TA_CENTER))
styles.add(ParagraphStyle('ScoreLbl', fontSize=10, leading=13, textColor=GRAY_500, fontName='Helvetica', alignment=TA_CENTER))
styles.add(ParagraphStyle('P0', fontSize=10, leading=14, textColor=RED, fontName='Helvetica-Bold', spaceAfter=1*mm))
styles.add(ParagraphStyle('P1', fontSize=10, leading=14, textColor=AMBER, fontName='Helvetica-Bold', spaceAfter=1*mm))
styles.add(ParagraphStyle('P2', fontSize=10, leading=14, textColor=GREEN, fontName='Helvetica-Bold', spaceAfter=1*mm))
styles.add(ParagraphStyle('Fix', fontSize=9.5, leading=13, textColor=GRAY_700, fontName='Helvetica', leftIndent=12, spaceAfter=3*mm))

W, H = A4
els = []

# --- PAGE 1: COVER + EXECUTIVE SUMMARY ---
band = Drawing(W - 40*mm, 6)
band.add(Rect(0, 0, W - 40*mm, 6, fillColor=TEAL, strokeColor=None, rx=3, ry=3))
els.append(band)
els.append(Spacer(1, 6*mm))
els.append(Paragraph("LinkedIn Account<br/>Diagnostic Report", styles['Title2']))
els.append(Paragraph(
    f'{d["account_name"]} &middot; @{d["account_slug"]}<br/>'
    f'{d["account_headline"]}<br/>'
    f'Analysis Period: {d["analysis_period"]} &middot; Generated: {d["generated_date"]}',
    styles['Subtitle']))
els.append(HRFlowable(width="100%", thickness=1, color=GRAY_200, spaceAfter=5*mm))
els.append(Paragraph("Executive Summary", styles['Section']))

# Score cards row
cards = [[make_score_card(d["algorithm_score"], 100, "Algorithm Health"),
          make_score_card(d["content_score"], 50, "Content Strategy"),
          make_score_card(d["alignment_score"], 10, "Profile Alignment"),
          make_score_card(d["unique_engagers"], 100, "Unique Engagers")]]
ct = Table(cards, colWidths=[130]*4)
ct.setStyle(TableStyle([('ALIGN',(0,0),(-1,-1),'CENTER'),('VALIGN',(0,0),(-1,-1),'MIDDLE')]))
els.append(ct)
els.append(Spacer(1, 5*mm))

# Key metrics
m1 = [[Paragraph(f'<font size="18" color="{TEAL.hexval()}">{d["account_followers"]}</font>', styles['Score']),
       Paragraph(f'<font size="18" color="{TEAL.hexval()}">{d["avg_impressions"]}</font>', styles['Score']),
       Paragraph(f'<font size="18" color="{TEAL.hexval()}">{d["avg_engagement_rate"]}%</font>', styles['Score']),
       Paragraph(f'<font size="18" color="{TEAL.hexval()}">{d["total_posts_analyzed"]}</font>', styles['Score'])],
      [Paragraph("Followers", styles['ScoreLbl']),
       Paragraph("Avg Impressions", styles['ScoreLbl']),
       Paragraph("Engagement Rate", styles['ScoreLbl']),
       Paragraph("Posts Analyzed", styles['ScoreLbl'])]]
mt = Table(m1, colWidths=[130]*4)
mt.setStyle(TableStyle([('ALIGN',(0,0),(-1,-1),'CENTER'),('VALIGN',(0,0),(-1,-1),'MIDDLE'),
    ('BACKGROUND',(0,0),(-1,-1),GRAY_50),('BOX',(0,0),(-1,-1),0.5,GRAY_200),
    ('TOPPADDING',(0,0),(-1,0),10),('BOTTOMPADDING',(0,-1),(-1,-1),10)]))
els.append(mt)
els.append(Spacer(1, 5*mm))

status_text = score_label(d["algorithm_score"], 100)
ac = score_color(d["algorithm_score"], 100)
els.append(Paragraph(
    f'<font color="{ac.hexval()}">Overall: {status_text}</font> — '
    f'Your account shows signs of algorithmic throttling. See the detailed modules below for root causes and the fix plan.',
    styles['Body']))

# --- PAGE 2: ALGORITHM HEALTH ---
els.append(PageBreak())
els.append(Paragraph("Module A: Algorithm Health Score", styles['Section']))
els.append(make_score_card(d["algorithm_score"], 100, "Algorithm Health", 150, 100))
els.append(Spacer(1, 3*mm))
els.append(Paragraph("Key Findings", styles['Subsection']))
els.append(styled_table([
    ["Metric", "Your Value", "Benchmark", "Status"],
    ["Avg Impressions/Post", str(d["avg_impressions"]), "1,500+",
     "\u26a0\ufe0f Below" if d["avg_impressions"] < 1500 else "\u2705 Good"],
    ["Avg Engagement Rate", f'{d["avg_engagement_rate"]}%', "3.5%+",
     "\u26a0\ufe0f Below" if d["avg_engagement_rate"] < 3.5 else "\u2705 Good"],
    ["Avg Reactions/Post", str(d["avg_reactions"]), "25+",
     "\u26a0\ufe0f Below" if d["avg_reactions"] < 25 else "\u2705 Good"],
    ["Avg Comments/Post", str(d["avg_comments"]), "8+",
     "\u26a0\ufe0f Below" if d["avg_comments"] < 8 else "\u2705 Good"],
    ["Impression Trend", d["impression_trend"].title(), "Rising",
     "\U0001f534 Critical" if d["impression_trend"] == "declining" else "\u2705 Good"],
    ["Ext. Link Penalty", "Yes" if d["external_link_penalty"] else "No", "No",
     "\U0001f534 Critical" if d["external_link_penalty"] else "\u2705 Good"],
    ["Best Post", f'{d["best_post_impressions"]} imp.', "—", "—"],
    ["Worst Post", f'{d["worst_post_impressions"]} imp.', "—", "—"],
], col_widths=[120, 90, 80, 80]))

if d["death_spiral"]:
    els.append(Spacer(1, 3*mm))
    els.append(Paragraph(
        '<font color="#EF4444"><b>WARNING: DEATH SPIRAL DETECTED</b></font> — '
        'Impressions are declining post-over-post. Immediate intervention required.',
        styles['Body']))

# --- PAGE 3: CONTENT STRATEGY ---
els.append(PageBreak())
els.append(Paragraph("Module B: Content Strategy Audit", styles['Section']))
els.append(make_score_card(d["content_score"], 50, "Content Score", 150, 100))
els.append(Spacer(1, 3*mm))
els.append(Paragraph("Content Mix Analysis", styles['Subsection']))
els.append(Paragraph("Your content mix vs. the ideal 4:2:1 ratio (57% Value : 29% Story : 14% Product):", styles['Body']))
for ck, cl in [("value_insight","Value / Insight"),("story_learning","Story / Learning"),("product_promo","Product / CTA"),("repost","Repost")]:
    v = d["content_mix"][ck]
    els.append(make_content_bar(v["pct"], v["ideal"], cl))
    els.append(Spacer(1, 1*mm))
els.append(Spacer(1, 3*mm))
pp = d["content_mix"]["product_promo"]["pct"]
vp = d["content_mix"]["value_insight"]["pct"]
els.append(Paragraph(
    f'<font color="{CORAL.hexval()}"><b>Problem:</b></font> {pp}% of content is product/CTA '
    f'(ideal: 14%). Only {vp}% is value content (ideal: 57%). '
    f'LinkedIn\'s algorithm treats heavily promotional accounts as advertisers and reduces organic reach.',
    styles['Body']))
els.append(Paragraph("Content Format Distribution", styles['Subsection']))
fm = d["format_mix"]
tp = d["total_posts_analyzed"] or 1
els.append(styled_table([
    ["Format", "Count", "% of Total", "Avg Reach Multiplier"],
    ["Text Only", str(fm["text_only"]), f'{round(fm["text_only"]/tp*100)}%', "1.0x (baseline)"],
    ["Image", str(fm["image"]), f'{round(fm["image"]/tp*100)}%', "1.2-1.5x"],
    ["Carousel/PDF", str(fm["carousel"]), f'{round(fm["carousel"]/tp*100)}%', "2.0-3.5x"],
    ["Video", str(fm["video"]), f'{round(fm["video"]/tp*100)}%', "2.0-5.0x"],
    ["Poll", str(fm["poll"]), f'{round(fm["poll"]/tp*100)}%', "1.5-2.5x"],
], col_widths=[100, 60, 80, 130]))
els.append(Spacer(1, 3*mm))
hs = d["avg_hook_score"]
els.append(Paragraph("Hook Quality", styles['Subsection']))
els.append(Paragraph(
    f'Average hook score: <font color="{score_color(int(hs*10),100).hexval()}"><b>{hs}/10</b></font>. '
    f'Strong hooks (data openers, contrarian takes) score 8+ and drive 3x more "see more" clicks.',
    styles['Body']))

# --- PAGE 4: ENGAGEMENT NETWORK ---
els.append(PageBreak())
els.append(Paragraph("Module C: Engagement Network", styles['Section']))
nh = d["network_health"].upper()
nc = RED if nh == "WEAK" else (AMBER if nh == "MODERATE" else GREEN)
els.append(Paragraph(f'Network Status: <font color="{nc.hexval()}"><b>{nh}</b></font>', styles['Subsection']))
els.append(styled_table([
    ["Metric", "Your Value", "Target", "Status"],
    ["Unique Engagers (30d)", str(d["unique_engagers"]), "50+",
     "\u26a0\ufe0f Low" if d["unique_engagers"] < 50 else "\u2705 Good"],
    ["Repeat Engagers (2+ times)", str(d["repeat_engagers"]), "20+",
     "\U0001f534 Critical" if d["repeat_engagers"] < 20 else "\u2705 Good"],
    ["Engagement Reciprocity", f'{int(d["engagement_reciprocity"]*100)}%', "40%+",
     "\U0001f534 Critical" if d["engagement_reciprocity"] < 0.4 else "\u2705 Good"],
], col_widths=[140, 90, 80, 80]))
els.append(Spacer(1, 3*mm))
els.append(Paragraph("Top Engagers", styles['Subsection']))
eng_rows = [["Name", "Engagements", "Reciprocated"]]
for e in d["top_engagers"]:
    eng_rows.append([e["name"], str(e["engagements"]), "\u2705 Yes" if e["reciprocated"] else "\u274c No"])
els.append(styled_table(eng_rows, col_widths=[160, 100, 100], header_color=TEAL))
els.append(Spacer(1, 3*mm))
els.append(Paragraph(
    f'<font color="{CORAL.hexval()}"><b>Critical gap:</b></font> Only {d["repeat_engagers"]} repeat engagers. '
    f'LinkedIn shows posts to a small test audience first — without a reliable inner circle of 20+ regular engagers, '
    f'most posts die in the first 60 minutes.',
    styles['Body']))

# --- PAGE 5: PROFILE ALIGNMENT + PRESCRIPTION ---
els.append(PageBreak())
els.append(Paragraph("Module D: Profile-Content Alignment", styles['Section']))
els.append(make_score_card(d["alignment_score"], 10, "Alignment Score", 150, 100))
els.append(Spacer(1, 3*mm))
els.append(Paragraph("Issues Found", styles['Subsection']))
for issue in d["profile_issues"]:
    els.append(Paragraph(f'\u2022 {issue}', styles['Body']))
els.append(Spacer(1, 6*mm))
els.append(Paragraph("Module E: Growth Prescription", styles['Section']))
els.append(Paragraph("Priority Issues (Ranked by Impact)", styles['Subsection']))
for pi in d["priority_issues"]:
    lv = pi["level"]
    if lv == "P0": sn, pf = 'P0', "\U0001f534 P0 \u2014 Fix Immediately"
    elif lv == "P1": sn, pf = 'P1', "\U0001f7e1 P1 \u2014 Fix This Week"
    else: sn, pf = 'P2', "\U0001f7e2 P2 \u2014 Optimize Over Time"
    els.append(Paragraph(f'{pf}: {pi["issue"]}', styles[sn]))
    els.append(Paragraph(f'\u2192 {pi["fix"]}', styles['Fix']))

# --- PAGE 6: INNER CIRCLE ---
els.append(PageBreak())
els.append(Paragraph("Your Recommended Inner Circle", styles['Section']))
els.append(Paragraph(
    "Engage with these people daily (Tier 1), 3x/week (Tier 2), or 2x/week (Tier 3). "
    "Within 2-3 weeks they will recognize your name and begin reciprocating.",
    styles['Body']))
tier_cfg = {1: ("\U0001f947 Tier 1 \u2014 Core Circle (engage daily)", TEAL),
            2: ("\U0001f948 Tier 2 \u2014 Extended Circle (engage 3x/week)", colors.HexColor("#6366F1")),
            3: ("\U0001f949 Tier 3 \u2014 Aspirational (engage 2x/week)", GRAY_500)}
for tn in [1, 2, 3]:
    label, hc = tier_cfg[tn]
    els.append(Paragraph(label, styles['Subsection']))
    tp = [p for p in d["inner_circle"] if p["tier"] == tn]
    if tp:
        rows = [["Name", "Followers", "Topics", "Why"]]
        for p in tp:
            rows.append([p["name"], p["followers"], p["topic"], p["why"]])
        els.append(styled_table(rows, col_widths=[90, 60, 100, 150], header_color=hc))
    else:
        els.append(Paragraph("(to be populated during diagnostic)", styles['Small']))
    els.append(Spacer(1, 3*mm))

# --- PAGE 7: MILESTONES + FOOTER ---
els.append(PageBreak())
els.append(Paragraph("Your 4-Week Growth Milestones", styles['Section']))
els.append(Paragraph(
    "Follow this plan consistently for measurable improvement in reach, engagement, and follower growth.",
    styles['Body']))
week_cfg = [("week1","Week 1: Foundation",CORAL),("week2","Week 2: Recognition",AMBER),
            ("week3","Week 3: Momentum",TEAL),("week4","Week 4: Breakthrough",GREEN)]
for wk, wl, wc in week_cfg:
    mb = Drawing(W - 40*mm, 50)
    mb.add(Rect(0, 0, W - 40*mm, 50, rx=6, ry=6, fillColor=GRAY_50, strokeColor=wc, strokeWidth=1.5))
    mb.add(Rect(0, 0, 5, 50, fillColor=wc, strokeColor=None))
    mb.add(String(14, 32, wl, fontSize=11, fontName='Helvetica-Bold', fillColor=wc))
    txt = d["milestones"].get(wk, "")
    if len(txt) > 90: txt = txt[:87] + "..."
    mb.add(String(14, 12, txt, fontSize=8.5, fontName='Helvetica', fillColor=GRAY_700))
    els.append(mb)
    els.append(Spacer(1, 3*mm))

# Footer
els.append(Spacer(1, 8*mm))
els.append(HRFlowable(width="100%", thickness=1, color=GRAY_200, spaceAfter=3*mm))
els.append(Paragraph(
    f'Generated by <font color="{TEAL.hexval()}"><b>Sai</b></font> \u2014 LinkedIn Account Diagnostic Skill | '
    f'{d["generated_date"]} | simular.ai',
    ParagraphStyle('Footer', fontSize=8, textColor=GRAY_500, fontName='Helvetica', alignment=TA_CENTER)))

doc.build(els)
print(f"PDF saved to: {OUTPUT_PATH}")

How to Use This Template

1. Collect data in Modules A-E and store results in the report_data dictionary structure shown above.
2. Fill in all fields — every module produces specific metrics that map directly to the dictionary keys.
3. Run the script via exec() to generate the PDF.
4. The PDF is saved to the artifacts/ folder and automatically synced to the user for download.

Design Principles

• Color coding: Teal = healthy/positive, Coral = warning/over-indexed, Red = critical, Amber = needs work, Green = good
• Score cards: Rounded rectangle backgrounds with large score numbers, color-coded by health status
• Tables: Navy header rows, alternating white/gray zebra stripes, 0.5pt grid lines
• Content mix bars: Horizontal bars with actual value (teal or coral) vs. ideal marker (dashed line)
• Priority issues: P0 (red), P1 (amber), P2 (green) with arrow-prefixed fix text
• Inner circle tiers: Tier 1 (teal header), Tier 2 (indigo header), Tier 3 (gray header)
• Milestone cards: Left-colored border bars progressing coral → amber → teal → green

Export Options

After generating the PDF report, offer these additional exports:

1. PDF Report (primary) — The professional diagnostic report generated above. Saved to artifacts/linkedin-diagnostic-report.pdf.
2. Google Sheet — Export raw post data + inner circle list to a spreadsheet for ongoing tracking. Use google.sheets.createSpreadsheet() with tabs: "Post Data", "Inner Circle", "Weekly Metrics".
3. Google Doc — Export the full report as a formatted Google Doc for sharing and collaboration. Use google.docs.createDocument() + batchUpdate() to insert formatted text.
4. Track progress — Offer to re-run the diagnostic in 2-4 weeks to measure improvement. Save baseline metrics for comparison.

Safety Rules

• Read-only: This skill only reads data, never posts, comments, likes, or sends requests
• Privacy: Don't store or share other users' profile data outside the diagnostic context
• Random delays: Wait 3-8 seconds between page loads, 10-20 seconds between profile visits
• CAPTCHA check: If any CAPTCHA or unusual prompt appears, STOP and notify user
• Session limits: Max 40 profile visits per diagnostic session
• Honest assessment: Don't sugarcoat the diagnosis. If the account is in trouble, say so clearly with specific data.

LinkedIn Algorithm Reference (for context in analysis)

How the Algorithm Distributes Posts

1. Post published → shown to 5-10% of 1st-degree connections (~100-300 people)
2. 60-90 minute window: Algorithm measures engagement rate from this initial audience
3. If engagement rate > 2-5% → expand to 2nd-degree connections
4. If engagement stays high → viral distribution mode
5. If engagement rate < 1% → stop distribution, post "dies"

Algorithm Weight Ranking

• Comments (especially long, substantive ones) > Shares/Reposts > Reactions/Likes > Views
• Author replies to comments = 2x engagement signal
• First 60 minutes of engagement = highest weight
• Dwell time (how long people spend reading) matters

Algorithm Penalties

• External links in post body: -40-50% reach
• Editing post within 10 minutes of publishing: algorithm reset
• Multiple posts same day: self-cannibalization
• 100% promotional content: classified as spam
• Hashtags > 5: diminishing returns / looks spammy
• Long posting gaps: algorithm "forgets" your account

Algorithm Rewards

• Consistent daily posting at similar times
• High comment-to-reaction ratio (indicates quality discussion)
• Carousel/PDF posts (highest organic reach format)
• Video content (5x reach vs text-only)
• Posts that generate comment threads (back-and-forth conversation)
• Active engagement with others' content (reciprocity signal)

The 4:2:1 Content Rule

Type	Ratio	Description
Value/Insight	4 parts (57%)	Frameworks, lessons, data, how-tos. NOT about your product.
Story/Learning	2 parts (29%)	Personal experiences, failures, behind-the-scenes, building journey
Product/CTA	1 part (14%)	Product in context of solving a real problem. Only AFTER trust is built.