LinkedIn Marketing Skills for Claude Code and Codex
Claude skills for LinkedIn. 12 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)
- Open claude.ai and click Customize in the sidebar
- Open the Plugins tab
- Click Add
- Choose Add marketplace → Add from a repository
- Paste
sergebulaev/linkedin-skills there and sync
- Find the plugin under Discover, then click Add
- Done. The skills activate automatically when you ask about LinkedIn.
Note: Skills/Plugins require a paid Claude plan (Pro, Max, Team, or Enterprise) with code execution enabled.
Claude Desktop (Mac / Windows)
- Open Claude Desktop
- Click Customize in the left sidebar, then open the Plugins tab
- Click the Add dropdown at the top right and choose Add marketplace
- Select Add from a repository, paste
sergebulaev/linkedin-skills, and sync
- Switch to the Discover tab and find the plugin in the list
- Click the + on the plugin card to install it
- Switch back to Yours to confirm it is listed and enabled
- Done. Start a new conversation and ask Claude to write a LinkedIn post.
The tab switch in steps 5 and 7 is the part that trips people: syncing a marketplace puts the plugin in the catalog (Discover), not in your installed list (Yours). The + in step 6 sits on the plugin card itself, not beside a section heading.
OpenClaw
- Open your OpenClaw working directory
- Clone the skills into it:
git clone https://github.com/sergebulaev/linkedin-skills.git
- 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.
- 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 — the skills activate with no plugin install, which is the route to use where /plugin is unavailable:
git clone https://github.com/sergebulaev/linkedin-skills.git
cd linkedin-skills
The repo ships a .claude/skills/ mirror of symlinks, so Claude Code finds all 12 skills on its own.
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 12 skills
| Skill |
What it does |
| Post Writer |
Drafts viral-ready posts using 20 proven 2026 hook formulas (anaphora, R.I.P. obituary, year-over-year pivot, curiosity gap, emotional cold-open, controlled A/B, false-binary, and 13 more) plus a founders-edition angle library, picked by engagement goal |
| Comment Drafter |
Drafts a comment on any LinkedIn post from its URL |
| Reply Handler |
Drafts a reply to any comment, correctly handling LinkedIn's 2-level thread flattening. Or give it just a post URL and it sweeps the whole thread — every top-level comment and reply — filters out low-value ones, and drafts the rest in one batch |
| Post Audit |
Checks your draft against 2026 algorithm rules and AI-detection patterns before you publish |
| Humanizer |
Removes the AI tells human readers and LinkedIn's slop filter react to: 2026 AI vocabulary scored by paragraph density, reveal bridges, staccato fragment stacks, stacked triads, performed sincerity; caps em dashes instead of banning them. Does not promise to beat detectors (no edit reliably does). Bundles three sub-tools: AI-emoji density scorer, multi-detector spread tester (GPTZero, Originality.ai, ZeroGPT, Sapling, Copyleaks) that documents how much they disagree, and a rule-explainer reference for defending stylistic choices. |
| Hook Extractor |
Reverse-engineers the hook formula from any viral post. Returns a blank template you can fill with your own topic |
| Content Planner |
Creates a 7-day plan with daily post topics, formats, hooks, posting times, and comment targets |
| Engagement Monitor |
Two read-side workflows: (1) tracks your comment threads for author replies and drafts follow-ups in the 6-24h window; (2) pulls likers and commenters on any post and groups them by ICP fit (peer / aspirational / prospect). |
| Profile Optimizer |
Rewrites your headline, About section, Featured section, and Experience for 2026 conversion patterns |
| Employee Advocacy |
Plans a team LinkedIn program: 14-day launch, posting cadence, brand governance, ROI tracking |
| Repurposer |
Turns content from another platform (tweet, thread, YouTube video, blog, newsletter) into a native LinkedIn post: re-hooks for the fold, expands to the 900-1300 char sweet spot, moves links to the first comment, runs the humanizer |
| Interviewer |
Interviews you and keeps the answers in a Story Bank: roles, receipts with real numbers, turning points, scars, positions you would defend. Every other skill reads it, so drafts stop asking you for a specific number mid-request. Also runs a focused interview that turns one topic into a post spine. The only skill that works when you have never posted before, since it needs a career rather than an archive |
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.
Standalone skills built by other people on this bundle's conventions (same voice rules, same approval-card flow, same Not for X (use Y) disambiguation). They live in their authors' repos, so the core stays at 12 skills and one read/write pipeline. Install them next to this bundle the same way.
- linkedin-outreach by @smfardeen7 - drafts 300-character connection-request notes (10 scenario templates) and post-accept follow-up sequences with day offsets and stop rules. Draft-only: LinkedIn has no invite or DM API, you paste and send.
Built one? Open a PR that adds a single line here.
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:
| Use case |
Actor |
Cost |
| Post body by URL |
supreme_coder/linkedin-post |
$1 / 1,000 |
| Comments + replies on a post |
apimaestro/linkedin-post-comments-replies-engagements-scraper-no-cookies |
$5 / 1,000 |
| Your own recent comments |
apimaestro/linkedin-profile-comments |
$5 / 1,000 |
| Likers + commenters on any post |
scraping_solutions/linkedin-posts-engagers-likers-and-commenters-no-cookies |
$5 / 1,000 |
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.
Publora also ships official MCP skills (npx skills add publora/skills): one skill per platform, covering the publish side only. This bundle is the layer above them, adding the reading, the writing craft and the approval flow.
Setup (2 minutes)
Before or after any of it, one command tells you where you stand:
python3 scripts/selftest.py # install, accounts, tests, and which skills work right now
python3 scripts/selftest.py --fresh # clone to a temp dir and check a genuinely clean install
It reports each of Apify, Publora and Pixfaro separately, using free endpoints that verify a key without doing any work, and it names what is missing rather than only that something is. Skills that need a layer you have not connected still work, by drafting for you to paste, and the report says which ones those are.
Two ways to connect, pick either
A connector, if you are on claude.ai or Claude Code. Publora and Pixfaro both publish one. Authorize it once in your connector settings and the skills use it: no key on disk, no .env, nothing to rotate. Publora's connector also carries post_stats and profile_stats, which the REST path below does not have.
An API key, if you are anywhere else — a plain terminal, CI, a script, or you would rather the credential lived in a file you control. That is the seven steps below.
They are not exclusive and neither is second-class. One caveat worth knowing: scripts/check_config.py and scripts/selftest.py read .env and the shell, so a connector is invisible to them. If they say "manual" while your posts are going out, the connector is doing the work and nothing is wrong.
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: sign up at api.pixfaro.com/signup, create a key (name it linkedin, scope Generate), and put PIXFARO_TOKEN=pf_live_... in .env at the root of the linkedin-skills folder (next to this README; keys are shown once). Then python3 scripts/check_config.py calls Pixfaro's GET /v1/key and prints the key's name and scope when it is right. 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.
For text-led visuals (a quote-card of your hook), the skills skip the image model entirely and use Pixfaro's design templates: lib.quote_card("<hook>", handle="@you", style="brand") typesets the card server-side (POST /v1/renders), so the line is crisp at any length — same hosted-URL flow. lib.available_templates() lists templates and live prices. A brand logo can be uploaded once with lib.brand_logo("logo.png") (full-scope key); the returned logo_id goes into Voice & Brand Profile §6 and every overlay from then on stamps the real mark.
Voice rules
Every skill follows these rules automatically:
- Em dashes capped at about 1 per 100 words. The character stopped being a tell in 2026; the density is.
- Capitalize names. Always. Lowercase reads as disrespectful.
- No AI vocabulary: "leverage", "fundamentally", "streamline", "harness", "delve", "unlock", "foster".
- Specific numbers beat adjectives. "$14,200" beats "significant savings".
- One sharp insight per comment beats three vague ones.
- 200-350 chars for comments, 900-1,300 chars for posts.
Troubleshooting
| Problem |
Fix |
| Skills don't activate when I ask about LinkedIn |
Make sure you installed via the Skills panel, /plugin install, or codex plugin add. Try starting a new conversation. |
| "Publora API key not provided" |
Your .env file is missing or in the wrong folder. It should be in the linkedin-skills/ root. |
| "401 Unauthorized" from Publora |
Your API key expired. Go to Publora Settings > API > Create a new key. |
| Image skills keep saying "No Pixfaro key set" although you added one |
The key was not loaded: .env must be at the linkedin-skills/ root and python-dotenv installed. python3 scripts/check_config.py now says exactly which — and, with a key, whether Pixfaro accepts it (GET /v1/key). |
| "401" from Pixfaro |
The key was copied short or revoked. Keys are shown once — mint a new one in the Pixfaro dashboard and paste the whole pf_live_... string. |
| "404 on comment/post" |
Your LINKEDIN_PLATFORM_ID is wrong. Go to Publora Channels and copy the full linkedin-... string. |
| "400 reactionType" error |
Known Publora quirk. The skills handle this automatically. If you're calling the API manually, use PRAISE (not CELEBRATE), INTEREST (not INSIGHTFUL). |
pip install fails |
Use a virtual environment: python -m venv venv && source venv/bin/activate && pip install requests python-dotenv |
Cross-cutting references
For developers: runtime compatibility, URL parsing, and internals
Runtime compatibility
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
| Runtime |
Auto-discovers skills? |
Setup |
| Claude Code (CLI, Desktop, Web, IDE) |
Yes |
Install via plugin or clone. Skills activate on matching prompts. |
| Codex CLI |
Yes |
Install via codex plugin marketplace add sergebulaev/linkedin-skills and codex plugin add linkedin-skills@linkedin-skills. |
Anthropic Managed Agents (/v1/agents) |
Yes |
Pass skill files in the agent context. |
| OpenClaw |
Manual |
Mount the repo, add system prompt pointing to skills/*/SKILL.md. |
| Cursor / Cline / Aider |
Manual |
Read SKILL.md files as prompt context; import lib/ as Python. |
| Manus |
No |
Upload references/ as knowledge base. Call Publora API directly. |
| LangChain / AutoGen |
No |
Use lib/ as a package; feed references/ as prompt context. |
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 for URL parsing,
# lib/apify_client.py for reading posts / comments / engagers,
# lib/publora_client.py for publishing."
Generic Python agent quickstart
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"]])
URL handling
LinkedIn has three post URN types. The lib/url_parser.py handles all of them:
| URL fragment |
URN |
/posts/slug-activity-7448... |
urn:li:activity:7448... |
/posts/slug-share-7449... |
urn:li:share:7449... |
/feed/update/urn:li:ugcPost:7447... |
urn:li:ugcPost:7447... |
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/<author-handle>_activity-<id>"
References
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.
Part of a family of AI social-media marketing skill bundles for Claude Code and Codex:
Also: Anthropic Skills repo, the awesome-claude-skills directory.
这是一套面向 LinkedIn 的 12 个 Claude Code 和 Codex 技能合集：它们能模仿你的语气起草帖子、评论和回复，在发布前审查草稿，并帮你建立固定的发布节奏——全部在终端里完成。