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.
npx skills add iamzifei/linkedin-article-publisher-skill --skill linkedin-article-publishergit clone --depth 1 https://github.com/iamzifei/linkedin-article-publisher-skillWrote 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.
[](https://agentmods.dev/skills/iamzifei/linkedin-article-publisher-skill/linkedin-article-publisher)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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.
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.
- Parse Markdown with Python script -> get title, images with block_index, HTML
- Navigate to LinkedIn Articles editor
- Upload cover image (first image)
- Fill title
- Copy HTML to clipboard (Python) -> Paste with Cmd+V
- Insert content images at positions specified by block_index
- 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)
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.
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.
- 12d ago First seen · 433 lines · 118 tokens per session scan A b6745bb850d0
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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