LinkedIn Marketing Skills for Claude

10 skills that help Claude write LinkedIn posts, comments, and replies in your voice. They draft content, strip AI tells, and wait for your approval before anything gets published. No coding required.

Install

Pick whichever way you use Claude:

claude.ai (web)

1. Open https://claude.ai/code
2. Go to Skills in the sidebar
3. Click Add from GitHub
4. Paste: sergebulaev/linkedin-skills
5. Done. The skills activate automatically when you ask about LinkedIn.

Claude Desktop (Mac / Windows)

1. Open Claude Desktop
2. Open Settings (gear icon)
3. Go to Skills
4. Click Add from GitHub
5. Paste: sergebulaev/linkedin-skills
6. Done. Start a new conversation and ask Claude to write a LinkedIn post.

OpenClaw

1. Open your OpenClaw working directory
2. Clone the skills into it:

   git clone https://github.com/sergebulaev/linkedin-skills.git
   

3. In OpenClaw settings, add this to your system prompt:

   You have LinkedIn marketing skills in ./linkedin-skills/.
   For any LinkedIn task, read the relevant skills/*/SKILL.md first.
   Use lib/url_parser.py and lib/publora_client.py for actions.
   

4. Done. Ask OpenClaw to write a LinkedIn post or comment.

Claude Code (CLI / VS Code / JetBrains)

/plugin marketplace add sergebulaev/linkedin-skills
/plugin install linkedin-skills@linkedin-skills

Or clone the repo and open it as your working directory:

git clone https://github.com/sergebulaev/linkedin-skills.git
cd linkedin-skills

What you can do

Once installed, just talk to Claude. The right skill activates automatically.

Write a post:

"Write me a LinkedIn post about why AI agencies are replacing traditional ones. Make it viral."

Comment on someone's post:

"Comment on this post: https://linkedin.com/posts/... — I want to add a thoughtful take."

Check a draft before publishing:

"Audit this post draft for AI tells and algorithm issues: [paste your text]"

Reverse-engineer a viral post:

"What hook formula does this post use? https://linkedin.com/posts/..."

Plan your week:

"Create a 7-day LinkedIn content plan. I'm a B2B SaaS founder targeting VPs of Marketing."

Rewrite your profile:

"Optimize my LinkedIn profile for inbound leads: https://linkedin.com/in/yourname"

Remove AI tells from any text:

"Humanize this text: [paste AI-generated draft]"

Every skill shows you a draft first and waits for your OK before doing anything. Nothing gets posted without your approval.

The 10 skills

Optional: auto-post with Publora

By default, skills draft content for you to copy-paste into LinkedIn. If you want Claude to publish directly to your LinkedIn (and optionally to X, Threads, Instagram), connect Publora. It takes about 2 minutes.

What is Publora?

Publora is a publishing API that handles LinkedIn's quirks (3 different URL formats, reaction type mismatches, thread flattening bugs). The free tier gives you 15 posts/month.

Setup (2 minutes)

Step 1. Sign up at https://app.publora.com/signup (free)

Step 2. Connect LinkedIn: click Channels in the left sidebar, then Add Channel, pick LinkedIn, authorize.

Step 3. Find your Platform ID: go to Channels, click your LinkedIn account. The ID looks like linkedin-ABC123DEF. Copy the whole thing including linkedin-.

Step 4. Get your API key: click Settings (gear icon, bottom-left), then API, then Create Key. Copy the sk_... string.

Step 5. Create a file called .env in the linkedin-skills folder:

PUBLORA_API_KEY=sk_paste_your_key_here
LINKEDIN_PLATFORM_ID=linkedin-paste_your_id_here

If you cloned the repo, you can copy the template instead:

cp .env.example .env

Then open .env and replace the placeholders with your real values.

Step 6. Install two small Python packages:

pip install requests python-dotenv

Step 7. Test it. Ask Claude:

"List my connected Publora accounts."

If you see your LinkedIn account, you're set. If not, check Troubleshooting.

Voice rules

Every skill follows these rules automatically:

1. No em dashes. Biggest AI tell in 2026.
2. Capitalize names. Always. Lowercase reads as disrespectful.
3. No AI vocabulary: "leverage", "fundamentally", "streamline", "harness", "delve", "unlock", "foster".
4. Specific numbers beat adjectives. "$14,200" beats "significant savings".
5. One sharp insight per comment beats three vague ones.
6. 200-350 chars for comments, 900-1,300 chars for posts.

Troubleshooting

Cross-cutting references

• references/industry-benchmarks.md — engagement rates, time-per-post, reach multipliers across industries
• references/engagement-metrics-taxonomy.md — what to measure at post / account / team / business level

Runtime compatibility

linkedin-skills/
├── skills/          ← SKILL.md frontmatter; native to Claude Code, others read as markdown
├── lib/             ← pure Python, works in any agent runtime
├── references/      ← pure markdown, works anywhere
└── scripts/         ← pure Python CLI, works anywhere

OpenClaw quickstart

git clone [email protected]:sergebulaev/linkedin-skills.git

# Add to OpenClaw system prompt:
# "You have LinkedIn marketing skills in ./linkedin-skills/.
#  Read the relevant skills/*/SKILL.md before any LinkedIn task.
#  Use lib/url_parser.py and lib/publora_client.py for actions."

Generic Python agent quickstart

import sys; sys.path.insert(0, "path/to/linkedin-skills")
from lib import parse_linkedin_url, PubloraClient

parsed = parse_linkedin_url("https://www.linkedin.com/posts/slug-activity-7448808898326654978-iW20")
print(parsed["post_urn"])  # urn:li:activity:7448808898326654978

client = PubloraClient()  # reads PUBLORA_API_KEY from env
client.create_comment(post_urn=parsed["post_urn"], message="draft", platform_id="linkedin-xxx")

URL handling

LinkedIn has three post URN types. The lib/url_parser.py handles all of them:

Comment URLs include a commentUrn query param. The parser extracts both post_urn and comment_id.

Thread flattening

LinkedIn flattens reply threads to 2 levels. When replying to a reply, parentComment must point to the top-level comment URN, not the reply's URN. The linkedin-reply-handler skill handles this correctly.

Testing the parser

python lib/url_parser.py "https://www.linkedin.com/posts/dharmesh_activity-7448808898326654978-iW20"

References

• Publora API docs — endpoint reference
• 360Brew paper — LinkedIn's ranking foundation model
• AuthoredUp 2026 reach data — format-level reach benchmarks

License

MIT. Powered by Publora.

Related

• Anthropic Skills repo
• awesome-claude-skills directory