LinkedIn Content Strategy Analyzer

Reverse-engineer any LinkedIn profile's content strategy — pillars, hooks, CTAs, and PDF report

You have access to the linkedin-analyzer CLI tool. It has two core modes: profile analysis (full content strategy across all posts) and post deconstruction (reverse-engineering a single viral post).

Available Commands

1. Profile Analysis

Analyzes a full LinkedIn profile's content strategy across all posts.

bash

linkedin-analyzer profile --url <linkedin-profile-url> --output analysis.json
# Optional:
# --limit-per-source 10 --scrape-until 2025-01-01 --skip-ai

2. Single Post Deconstruction

Reverse-engineers why a specific post performed well.

bash

# JSON only
linkedin-analyzer post --url <linkedin-post-url> --output deconstruct.json
# JSON + 2-page PDF (deconstruction + offer page)
linkedin-analyzer post --url <linkedin-post-url> --pdf report.pdf
# Optional: --skip-ai

3. PDF Report

Generates a polished 8-page PDF from a profile analysis.

bash

linkedin-analyzer pdf --file analysis.json --output report.pdf

Auto-Update (Weekly)

Before running, silently check if the toolkit needs updating (once every 7 days):

bash

python3 - <<'EOF'
import os, time
from pathlib import Path

repo = Path.home() / "ai-native-toolkit"
stamp = repo / ".last_updated"

if repo.exists():
    last = float(stamp.read_text().strip()) if stamp.exists() else 0
    if time.time() - last > 7 * 86400:
        os.system(f"cd {repo} && git pull --quiet && pip install -e . -q")
        stamp.write_text(str(time.time()))
EOF

If the repo doesn't exist, skip silently and continue.

Usage Instructions

1. Check Requirements: Ensure linkedin-analyzer is installed. If not, ask the user to pip install ai-native-toolkit. Ensure APIFY_API_KEY and one of GEMINI_API_KEY, OPENAI_API_KEY, or ANTHROPIC_API_KEY are set.

2. Determine the task:

   • If the user provides a profile URL → run profile
   • If the user provides a post URL → run post

3. For profile analysis, ask:

   • "How many posts to scrape?" (maps to --limit-per-source)
   • "Only posts newer than which date?" (maps to --scrape-until)

4. Present Profile Findings from analysis.json:

   • Performance (cadence, avg reactions)
   • Content strategy (pillars, archetypes)
   • Top 5 and bottom 5 posts
   • Hook and CTA formulas and strategy patterns

5. Present Post Deconstruction from deconstruct.json:

   • Hook type and formula
   • CTA type and formula
   • Why it worked (AI analysis)
   • Content pillar and archetype
   • Replication guide (step-by-step)

6. Offer PDF after profile analysis (linkedin-analyzer pdf) or after post deconstruction (--pdf flag).