LinkedIn Marketing Skills for Claude Code and Codex

Claude skills for LinkedIn. 11 Claude Code and Codex skills that 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.

On another platform too? The same team ships matching marketing skill bundles for X (Twitter) · Instagram · YouTube · TikTok · Threads · Facebook. Same voice engine, same approve-before-publish flow.

Install

Pick whichever way you use Claude Code or Codex:

Codex CLI

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

To test a local clone before publishing changes:

git clone https://github.com/sergebulaev/linkedin-skills.git
cd linkedin-skills
codex plugin marketplace add .
codex plugin add linkedin-skills@linkedin-skills

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. Click Customize
3. Click the + next to Personal plugins → Create plugin → Add marketplace
4. Choose Add from a repository and paste: sergebulaev/linkedin-skills
5. Install the plugin
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 for URL parsing,
       lib/apify_client.py for reading posts / comments / engagers,
       lib/publora_client.py for publishing 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

Hermes Agent

Hermes Agent (Nous Research) follows the agentskills.io open standard and loads skills/*/SKILL.md directly. Clone the bundle into your Hermes skills folder:

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

Coming from OpenClaw? hermes claw migrate imports these skills automatically. Then call /<skill-name> from any of your Hermes chat surfaces.

Any agent (skills CLI)

One command that works across Claude Code, Codex, Cursor, and any other agent that reads SKILL.md files:

npx skills add sergebulaev/linkedin-skills

Found this useful? Star the repo. Curated Claude Code and Codex directories rank and gate by star count, so a star is what makes these skills findable for the next person. It is the only thing we ask. No signup, no email.

What you can do

Once installed, just ask Claude Code or Codex for help with LinkedIn. 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 11 skills

Built for founders

If you are a founder, the bundle ships a dedicated founder layer. Your real constraint is rarely reach. It is a small number of high-stakes readers: the next investor, the next hire, the design partner who becomes a case study. The founder layer optimizes for trust with that narrow audience instead of impressions.

• 10 founder angles (references/founder-topics.md) as fill-in templates: reprice the category, content-to-pipeline, audience of one, the scarce-shots math, the unglamorous bet, the limit of delegation, designed serendipity, the evasive-sentence test, the delegation line, the learning gate. Each maps to an engagement goal and a hook formula.
• 4 structural hook formulas (F17-F20) that shape a post's logic: controlled A/B anecdote, false-binary dissolve, anecdote-meets-evidence bridge, diverging-curves close.
• A founders-edition content plan (Conviction / Building in public / The math / Proof) in the Content Planner.

Just tell the Post Writer you are a founder, or ask the Content Planner for a "founder plan," and the skills reach for these first.

Optional: read LinkedIn data with Apify

Four of the skills (Comment Drafter, Reply Handler, Hook Extractor, Engagement Monitor) can read post bodies, comment threads, your own recent comments, and the people who liked or commented on any post. Without an Apify token they fall back to asking you to paste the relevant text. With one, they fetch automatically.

Apify free tier ships with $5/month of credit, which goes a long way at $1-$5 per 1,000 results. The skills use four no-cookies actors:

Setup: drop APIFY_TOKEN=apify_api_... into your .env. The thin client at lib/apify_client.py exposes fetch_post, fetch_post_comments, fetch_user_recent_comments, and fetch_post_engagers.

A typical creator running daily comment ops + a weekly engager-analytics sweep stays under $2/month, well inside the free tier.

Optional: auto-post with Publora

By default, skills draft content for you to copy-paste into LinkedIn. If you want Claude Code or Codex 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 Code or Codex:

"Schedule a test LinkedIn post via Publora 24 hours from now: 'testing the API connection — will cancel in dashboard'."

If Publora returns a scheduled-post ID, you're set. Cancel the post in the Publora dashboard before the scheduled time. If you get HTTP 401, your API key is wrong. If you get HTTP 400 about a missing platformId, your LINKEDIN_PLATFORM_ID isn't set. See Troubleshooting.

Optional: generate illustrations with Pixfaro

Posts with a visual get more dwell time. The Post Writer can generate an illustration for a draft (a feed image, a carousel slide, or a quote-card of your hook) and attach it automatically when publishing. Without a key it drafts the image prompt and asks you to generate it yourself, so nothing breaks.

Pixfaro is a single image API over multiple models (from flux-schnell at $0.004 to gpt-5-image). It composites your handle, brand color, or logo onto the image as a pixel-exact overlay, so a cheap base model still renders crisp text on a quote-card or thumbnail. Pull those brand fields from your Voice & Brand Profile (section 6) and every asset stays on-brand.

Setup: drop PIXFARO_TOKEN=pf_live_... into your .env. The thin client at lib/pixfaro_client.py and the wrappers lib.illustrate(prompt, kind=...) / lib.refine(image_id, instruction) return a hosted URL that flows straight into lib.publish(..., media_urls=[url]). refine edits a prior image by its id (cheaper than regenerating); results carry cost, balance_after, and a premium flag so the skills never quietly spend on a pricey model.

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

linkedin-skills/
├── skills/          ← SKILL.md frontmatter; native to Claude Code and Codex, others read as markdown
├── .codex-marketplace/ ← generated nested Codex package (run scripts/sync_codex_marketplace.py)
├── lib/             ← pure Python, works in any agent runtime
├── references/      ← pure markdown, works anywhere
└── scripts/         ← pure Python CLI, works anywhere

git clone git@github.com: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 for URL parsing,
#      lib/apify_client.py for reading posts / comments / engagers,
#      lib/publora_client.py for publishing."

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

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

# Read side (Apify)
apify = ApifyClient()  # reads APIFY_TOKEN from env
post = apify.fetch_post(post_url="https://www.linkedin.com/posts/...")
engagers = apify.fetch_post_engagers(post_url="https://www.linkedin.com/posts/...", max_items=50)

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

# Image side (Pixfaro) — optional, reads PIXFARO_TOKEN from env
from lib import illustrate
img = illustrate("Minimal flat-vector lighthouse, calm blue palette", kind="wide")
# img["url"] -> pass to publish(..., media_urls=[img["url"]])

python lib/url_parser.py "https://www.linkedin.com/posts/<author-handle>_activity-<id>"

References

• Publora API docs — endpoint reference for the publishing layer
• Apify console — manage actors, tokens, and usage for the read layer
• 360Brew paper — LinkedIn's ranking foundation model
• AuthoredUp 2026 reach data — format-level reach benchmarks

Who builds this

These skills come out of Creative Content Crafts, an engineering company. We build the machinery underneath a company's public voice: ICP parsing, engagement systems, content guardrails, and posting infrastructure. We do not sell the words themselves.

We call that layer content engineering. Writing collapsed to the price of a chat subscription. What stayed valuable is everything below it: pulling every post your market wrote this week, keeping a live list of the people who matter, engaging on it daily with judgment in the loop, and catching the risky drafts before the platform does.

On LinkedIn specifically, that is the whole job. We are engineers of LinkedIn growth, not a ghostwriting agency.

This repo is the thin top layer of that stack, open-sourced. The engine underneath is what we build for clients.

License

MIT. Powered by Publora.

Related open-source skill bundles

Part of a family of AI social-media marketing skill bundles for Claude Code and Codex:

• linkedin-skills - LinkedIn (this repo)
• x-skills - X (Twitter)
• instagram-skills - Instagram
• youtube-skills - YouTube
• threads-skills - Threads
• tiktok-skills - TikTok
• facebook-skills - Facebook Pages

Also: Anthropic Skills repo, the awesome-claude-skills directory.