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 claude-office-skills/skills --skill linkedin-automationgit clone --depth 1 https://github.com/claude-office-skills/skillsWrote 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/claude-office-skills/skills/linkedin-automation)<a href="https://agentmods.dev/skills/claude-office-skills/skills/linkedin-automation"><img src="https://agentmods.dev/badge/skills/claude-office-skills/skills/linkedin-automation/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/claude-office-skills/skills/linkedin-automation"><img src="https://agentmods.dev/badge/skills/claude-office-skills/skills/linkedin-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00019 | $0.02472 |
| Opus 5 | $0.00010 | $0.01236 |
| Sonnet 5 | $0.00004 | $0.00494 |
| Haiku 4.5 | $0.00002 | $0.00247 |
Grade A, and why
LinkedIn Automation 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 9d 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 — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Automation
Comprehensive skill for automating LinkedIn marketing and B2B lead generation.
Core Workflows
1. Content Pipeline
LINKEDIN CONTENT FLOW:
┌─────────────────┐
│ Content Plan │
│ - Topics │
│ - Calendar │
└────────┬────────┘
▼
┌─────────────────┐
│ Create Post │
│ - Text │
│ - Visual │
│ - Document │
└────────┬────────┘
▼
┌─────────────────┐
│ Optimize │
│ - Hook │
│ - Hashtags │
│ - CTA │
└────────┬────────┘
▼
┌─────────────────┐
│ Schedule │
│ - Best time │
│ - Frequency │
└────────┬────────┘
▼
┌─────────────────┐
│ Engage │
│ - Comments │
│ - DMs │
└─────────────────┘
2. Lead Generation Flow
lead_gen_workflow:
search:
filters:
- industry: "Software"
- company_size: "51-200"
- title_contains: ["CEO", "CTO", "VP"]
- location: "San Francisco Bay Area"
qualify:
criteria:
- has_recent_activity: true
- mutual_connections: "> 3"
- engagement_score: "> 50"
outreach:
sequence:
- action: connect
message: connection_request
- wait: 2_days
- action: message
template: intro_message
- wait: 3_days
- action: follow_up
template: value_add
Content Templates
Post Formats
post_templates:
story_post:
format: |
{{hook_line}}
↓
{{story_paragraph_1}}
{{story_paragraph_2}}
{{lesson_learned}}
{{call_to_action}}
---
♻️ Repost if this resonated
🔔 Follow for more insights
example: |
I got rejected 47 times before landing my dream job.
↓
Each rejection felt like a punch to the gut.
But I kept going.
Here's what changed everything:
I stopped trying to "impress" and started being authentic.
The 48th interview? I got 2 offers.
Lesson: Rejection is redirection, not the end.
---
♻️ Repost if this resonated
🔔 Follow @profile for career tips
list_post:
format: |
{{title}}
{{point_1}}
{{point_2}}
{{point_3}}
{{point_4}}
{{point_5}}
{{wrap_up}}
Which one is most important to you? 👇
carousel:
slides:
- cover: hook_title
- slides: [content_1, content_2, content_3]
- cta: follow_cta
design:
size: "1080x1350"
format: "pdf"
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.
- 9d ago First seen · 456 lines · 19 tokens per session scan A 118be0e0ed91
LinkedIn Automation is a skill published in the GitHub repository claude-office-skills/skills (465 stars, last pushed 7mo ago), licensed MIT. It adds 19 tokens to every session and 2,472 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
linkedin-hand-skill
Expert knowledge for AI LinkedIn management -- API reference, content strategy, networking playbook, and professional engagement best practices.
Social Post Thread Writer
Converts a blog post, idea, or document into an engaging Twitter/X or LinkedIn thread with hooks and CTAs.
linkedin-cold-reviver
Detect a going-cold lead and fire one completely new angle that lands value whether or not they reply. Use when a thread has gone quiet and a re-ask would only nag.
linkedin-comment-engine
Draft two authority-building comments to leave on someone else's post, warming through visibility instead of DMs. Use when you want a decision-maker to notice you and click your profile.
linkedin-engage-plan
Design a multi-touch warming sequence for one target so they recognise you before you ever message them. Use when you have a specific person worth warming up before any pitch.
linkedin-engager-analytics
Analyse everyone who liked or commented on a post, score them against your ICP, and return the match rate plus the top 10 profiles with a real-signal outreach note for each. Use after a post lands to find the warm prospects hiding in your engagement.