linkedin-article-publisher

linkedin-article-publisher is a skill for Claude Code from iamzifei/linkedin-article-publisher-skill. It costs 118 tokens per session (3,611 once invoked), scanned A, original, MIT.

A tool that turns a Markdown article into formatted content for LinkedIn Articles, LinkedIn’s long-form publishing editor.

In plain words
What is it for?
Use it to prepare a Markdown file or URL for LinkedIn, including its title, cover image, other images, and rich text formatting.
Why use it?
It removes the need to recreate headings, text formatting, cover images, and content images manually in the editor.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: reads .claude/ paths.

Part of the linkedin-article-publisher plugin — 1 skill shipped together

Good fit Use it to prepare a Markdown file or URL for LinkedIn, including its title, cover image, other images, and rich text formatting.

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Install with agentmods
npx agentmods add skills/iamzifei/linkedin-article-publisher-skill/linkedin-article-publisher
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add iamzifei/linkedin-article-publisher-skill --skill linkedin-article-publisher
Clone the repo
git clone --depth 1 https://github.com/iamzifei/linkedin-article-publisher-skill

Made for: Claude Code.

Or install linkedin-article-publisher, the plugin that ships this one along with the rest of its 1 skill.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for linkedin-article-publisher

README.md
[![agentmods](https://agentmods.dev/badge/skills/iamzifei/linkedin-article-publisher-skill/linkedin-article-publisher/github.svg)](https://agentmods.dev/skills/iamzifei/linkedin-article-publisher-skill/linkedin-article-publisher)
Your own site
<a href="https://agentmods.dev/skills/iamzifei/linkedin-article-publisher-skill/linkedin-article-publisher"><img src="https://agentmods.dev/badge/skills/iamzifei/linkedin-article-publisher-skill/linkedin-article-publisher/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for linkedin-article-publisher

Your own site · 80×15
<a href="https://agentmods.dev/skills/iamzifei/linkedin-article-publisher-skill/linkedin-article-publisher"><img src="https://agentmods.dev/badge/skills/iamzifei/linkedin-article-publisher-skill/linkedin-article-publisher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,611 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00118 $0.03611
Opus 5 $0.00059 $0.01806
Sonnet 5 $0.00024 $0.00722
Haiku 4.5 $0.00012 $0.00361

Measured 12d ago against content hash b6745bb850d0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

linkedin-article-publisher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/copy_to_clipboard.py, scripts/parse_markdown.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/linkedin-article-publisher/SKILL.md · 433 lines

How it starts

The opening of the file, as written. The whole thing — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn Article Publisher

Publish Markdown content to LinkedIn Articles editor, preserving formatting with rich text conversion.

Prerequisites

  • Playwright MCP for browser automation
  • User logged into LinkedIn in browser
  • Python 3.9+ with dependencies: pip install Pillow pyobjc-framework-Cocoa

Scripts

Located in ~/.claude/skills/linkedin-article-publisher/scripts/:

parse_markdown.py

Parse Markdown and extract structured data:

python parse_markdown.py <markdown_file> [--output json|html] [--html-only]

Returns JSON with: title, cover_image, content_images (with block_index for positioning), html, total_blocks

copy_to_clipboard.py

Copy image or HTML to system clipboard:

# Copy image (with optional compression)
python copy_to_clipboard.py image /path/to/image.jpg [--quality 80]

# Copy HTML for rich text paste
python copy_to_clipboard.py html --file /path/to/content.html

Workflow

Strategy: "Text First, Images Later"

For articles with multiple images, paste ALL text content first, then insert images at correct positions using block index.

  1. Parse Markdown with Python script -> get title, images with block_index, HTML
  2. Navigate to LinkedIn Articles editor
  3. Upload cover image (first image)
  4. Fill title
  5. Copy HTML to clipboard (Python) -> Paste with Cmd+V
  6. Insert content images at positions specified by block_index
  7. Save as draft (NEVER auto-publish)

Efficiency Guidelines

Goal: Minimize wait time between operations for smooth automation.

1. Avoid unnecessary browser_snapshot

Most browser operations (click, type, press_key, etc.) return page state in response. Don't call browser_snapshot after every operation - use the returned state directly.

Bad:
browser_click -> browser_snapshot -> analyze -> browser_click -> browser_snapshot -> ...

Good:
browser_click -> use returned state -> browser_click -> ...

2. Avoid unnecessary browser_wait_for

Only use browser_wait_for when:

  • Waiting for image upload to complete
  • Waiting for initial page load (rare cases)

Read the full file on GitHub · 433 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 433 lines · 118 tokens per session scan A b6745bb850d0

Subscribe to this mod's changes

linkedin-article-publisher is a skill published in the GitHub repository iamzifei/linkedin-article-publisher-skill (21 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 3,611 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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