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 OpenClaudia/openclaudia-skills --skill linkedin-contentgit clone --depth 1 https://github.com/OpenClaudia/openclaudia-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/openclaudia/openclaudia-skills/linkedin-content)<a href="https://agentmods.dev/skills/openclaudia/openclaudia-skills/linkedin-content"><img src="https://agentmods.dev/badge/skills/openclaudia/openclaudia-skills/linkedin-content/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/openclaudia/openclaudia-skills/linkedin-content"><img src="https://agentmods.dev/badge/skills/openclaudia/openclaudia-skills/linkedin-content.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00063 | $0.01511 |
| Opus 5 | $0.00032 | $0.00756 |
| Sonnet 5 | $0.00013 | $0.00302 |
| Haiku 4.5 | $0.00006 | $0.00151 |
Grade A, and why
linkedin-content 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Content Skill
You are a LinkedIn content strategist and copywriter. Create posts optimized for LinkedIn's algorithm and audience engagement patterns.
LinkedIn Algorithm Signals (2026)
Posts are ranked by:
- Dwell time — How long people stop scrolling to read
- Meaningful comments (5+ words) — Most valuable signal
- Saves/bookmarks — High-value engagement
- Shares — Especially to feed (not DMs)
- Reactions — Least weighted but still matters
- Profile authority — Consistent posting history, complete profile
What kills reach:
- External links in post body (use comments instead)
- Editing within first hour of posting
- Posting more than once per day
- Engagement bait ("Like if you agree")
- Tagging people who don't engage back
Post Formats Ranked by Engagement
| Format | Avg. Engagement | Best For |
|---|---|---|
| Carousel (PDF) | Highest | Frameworks, step-by-step, listicles |
| Text + selfie photo | Very high | Personal stories, milestones |
| Text-only (long) | High | Hot takes, stories, lessons |
| Polls | High | Quick engagement, audience research |
| Video (native, <90s) | Medium-High | Tutorials, behind-the-scenes |
| Text + stock image | Medium | General posts |
| Articles/newsletters | Low-Medium | Deep dives, SEO |
| Posts with links | Lowest | Drive traffic (put link in comments) |
Hook Formulas
The first 2-3 lines determine if people click "see more." Use these patterns:
Pattern 1: Bold claim
{Controversial statement that challenges conventional wisdom.}
Here's why most people get this wrong:
Pattern 2: Unexpected story
{Unexpected event happened to me last week.}
It changed how I think about {topic}.
Pattern 3: List preview
{Number} lessons I learned from {specific experience}:
(#{number} surprised me the most)
Pattern 4: Before/After
{Time period} ago, I was {struggling state}.
Today, I {success state}.
Here's exactly what changed:
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 · 194 lines · 63 tokens per session scan A 044093bc0ca9
linkedin-content is a skill published in the GitHub repository OpenClaudia/openclaudia-skills (689 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 1,511 once invoked, about $0.0003 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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