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 audit
```
pre-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.py
```
to 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%
```
beats
```
significant
```
.
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.
```
INSIGHTFUL
```
is NOT a valid Publora reaction type. Use
```
INTEREST
```
instead (the client auto-maps).
A post URN returned by
```
url_parser
```
may be
```
activity
```
when the canonical URN is actually
```
ugcPost
```
. If posting fails with 404, fall back to resolving via
```
lib.ApifyClient.fetch_post_comments(post_id=...)
```
and read the canonical URN from any existing comment's
```
comment_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.

linkedin-marketing

NPX Install

Tags

SKILL.md Content