linkedin-analyzer

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

3,891 stars

Best use case

linkedin-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using linkedin-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/linkedin-analyzer/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/behruamm/linkedin-analyzer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/linkedin-analyzer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How linkedin-analyzer Compares

Feature / Agentlinkedin-analyzerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# LinkedIn Content Strategy Analyzer

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).

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