LinkedIn Marketing
Plan, draft, audit, and publish LinkedIn posts and comments with algorithm optimization.
Installation
- Make sure Claude is on your device and in your terminal.
Skills load from
~/.claude/skills/when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- Paste into Claude Code or into your terminal.
This copies the whole skill folder into
~/.claude/skills/linkedin-marketing-sergebulaev/— the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.Faster alternative (instruction-only skills)
Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.
Quick install (SKILL.md only)Sign up to copy - Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from
~/.claude/skills/). New skills are picked up on startup. - Just ask Claude.
Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.
Prefer to read the source first? Open on GitHub.
When Claude uses it
Plan, draft, audit, and publish LinkedIn posts and comments. Use when the user wants to write a viral LinkedIn post, draft a comment or reply on any LinkedIn post URL, audit a draft against 2026 algorithm heuristics, remove AI tells, extract hook formulas from viral posts, or plan a week of content. Powered by the Publora API for publishing. User provides post/comment URLs, skill drafts content, user approves, then publishes.
What this skill does
LinkedIn Marketing Skills
A bundle of 11 focused skills for LinkedIn content ops in 2026, built for Claude Code and Codex. Each skill is single-purpose, follows the draft → approval → publish pattern, and uses the Publora API for posting.
When to use this bundle
- Writing a viral post → use
linkedin-post-writer - Commenting on someone else's post → use
linkedin-comment-drafter - Replying to a comment (yours or someone else's) → use
linkedin-reply-handler - Reviewing a draft before publishing, removing AI tells, scoring AI emoji density, defending a flagged rule, or running 5 AI detectors in parallel → use
linkedin-humanizer(rewrite +--mode auditpre-publish review; folds in the former post-audit, emoji-detector, rules-explainer, and detector-tester sub-tools) - Extracting a hook formula from a viral post → use
linkedin-hook-extractor - Planning a week of LinkedIn content → use
linkedin-content-planner - Tracking which of your comments got author replies → use
linkedin-thread-monitor - Analyzing who liked / commented on any post (audience segmentation) → use
linkedin-engager-analytics - Auditing / rewriting a LinkedIn profile → use
linkedin-profile-optimizer - Running an employee advocacy program across a marketing team → use
linkedin-employee-advocacy - Adapting content from another platform (tweet, video, blog) into a native LinkedIn post → use
linkedin-repurposer
Core pattern
Every action-taking skill follows three steps:
- Parse the input. User provides a LinkedIn URL (post or comment). The skill uses
lib/url_parser.pyto extract the post URN and any comment ID. - Draft the content. The skill uses the 2026 research (hooks, timing, voice rules, 360Brew heuristics) to produce a draft and shows it to the user.
- Wait for approval. The user replies with "post", "yes", or suggests edits. Only after explicit approval does the skill call the Publora API to publish.
Prerequisites
Three tiers — pick one.
🟢 Tier 0 — Draft only (default, no setup)
The skills work out of the box. No API keys, no signup. Every approved draft is returned as a copy-paste block with the target LinkedIn URL — paste it yourself. Great for trying the skills before committing to any backend.
🔵 Tier 1 — Publora auto-post (recommended, ~2 min)
On approval, skills auto-publish to LinkedIn (and optionally X, Threads) via the Publora API. Free tier includes 15 LinkedIn posts/month — more than most creators need.
- Sign up free: https://app.publora.com/signup
- Connect your LinkedIn account in Publora (Channels → Add Channel)
- Copy your API key from Publora's API panel
- Drop into
.env:PUBLORA_API_KEY=sk_... LINKEDIN_PLATFORM_ID=linkedin-... - Run
pip install -r requirements.txt
Why Publora: LinkedIn has three URN types (activity/share/ugcPost), a reaction-bug where INSIGHTFUL returns 400, and a 2-level thread-flattening quirk that breaks most third-party implementations. Publora handles all of it. We built on top of their API so we didn't have to.
⚫ Tier 2 — Build your own poster (advanced)
Prefer not to SaaS it? Ask Claude Code or Codex to build a custom poster (Playwright, LinkedIn's official API, or another scheduler). Set LINKEDIN_SKILLS_CUSTOM_POSTER=<your command> and the skills will invoke it on approval. This is a weekend of work. Publora is 2 minutes.
Optional: Apify (read-side LinkedIn fetching)
Several skills (linkedin-comment-drafter, linkedin-reply-handler, linkedin-thread-monitor, linkedin-engager-analytics, linkedin-hook-extractor) can read LinkedIn post bodies, comment threads, a user's own recent comments, and the people who liked or commented on any post. They use the Apify platform when an APIFY_TOKEN is set; otherwise they ask you to paste the relevant text.
- Sign up free: https://console.apify.com/sign-up (free tier ships with $5/month of credit, enough for ~1,000 post fetches or ~1,000 comment-thread fetches).
- Generate a token: Console → Settings → Integrations.
- Drop into
.env:APIFY_TOKEN=apify_api_...
Actors used (all no-cookies, public, no LinkedIn login required):
| Use case | Actor | Approx 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 |
The thin client lives at lib/apify_client.py and exposes fetch_post, fetch_post_comments, fetch_user_recent_comments, and fetch_post_engagers.
Voice rules (baked into every skill)
- No em dashes (
—), en dashes, or double dashes — biggest AI tell. - Use
..as soft pause when mid-sentence rhythm calls for it. - Capitalize all personal names, company names, and product names. Lowercase reads as disrespectful.
- Sentence starts can be lowercase (natural voice), but names inside are always capitalized.
- Avoid AI vocabulary:
leverage,fundamentally,streamline,harness,delve,unlock,foster. - Specific numbers beat adjectives —
47%beatssignificant. - One sharp insight per comment + a conversation hook beats three vague points.
- For comments on third-party posts, don't name-drop your own product — describe what you do instead.
- LinkedIn posts: 900–1,300 chars sweet spot. Comments: 200–350 chars.
- Hook lives in the first 210 chars (before "… see more" on mobile).
(Canonical reference, plus comment-specific extensions: references/voice-rules.md. See also references/hook-formulas.md and references/algorithm-heuristics.md.)
How URLs map to URNs
LinkedIn ships three post URN types (the library handles all three):
| URN type | Example URL fragment | Example URN |
|---|---|---|
activity | /posts/slug-activity-7448...-XX | urn:li:activity:7448... |
share | /posts/slug-share-7449...-XX | urn:li:share:7449... |
ugcPost | /feed/update/urn:li:ugcPost:7447... | urn:li:ugcPost:7447... |
Comment URLs:
/feed/update/urn:li:activity:POST_ID?commentUrn=urn%3Ali%3Acomment%3A%28activity%3APOST_ID%2CCOMMENT_ID%29
The library decodes the commentUrn fragment and returns both post_urn and comment_id.
Known gotchas
- LinkedIn flattens reply threads to 2 levels. When replying to a reply, pass the top-level comment URN as
parentComment, not the reply's URN. INSIGHTFULis NOT a valid Publora reaction type. UseINTERESTinstead (the client auto-maps).- A post URN returned by
url_parsermay beactivitywhen the canonical URN is actuallyugcPost. If posting fails with 404, fall back to resolving vialib.ApifyClient.fetch_post_comments(post_id=...)and read the canonical URN from any existing comment'scomment_url. - Publora schedules comments ~90s in the future by default.
Resources
- Publora API docs — full endpoint reference for the publishing layer
- Apify console — manage actors, tokens, and usage for the read layer
lib/publora_client.py,lib/apify_client.py— thin Python clients used by every skill
Acknowledgments
Publishing powered by the Publora REST API. Algorithm insights via arXiv 2501.16450 (360Brew) and AuthoredUp 2026 reach data.
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