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 vstorm-co/content-skills --skill content-linkedingit clone --depth 1 https://github.com/vstorm-co/content-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/vstorm-co/content-skills/content-linkedin)<a href="https://agentmods.dev/skills/vstorm-co/content-skills/content-linkedin"><img src="https://agentmods.dev/badge/skills/vstorm-co/content-skills/content-linkedin/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/vstorm-co/content-skills/content-linkedin"><img src="https://agentmods.dev/badge/skills/vstorm-co/content-skills/content-linkedin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00033 | $0.01301 |
| Opus 5 | $0.00016 | $0.00651 |
| Sonnet 5 | $0.00007 | $0.00260 |
| Haiku 4.5 | $0.00003 | $0.00130 |
Grade A, and why
content-linkedin 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Post Generation
Before You Start
-
Check for brand voice: Read
brand/VOICE.mdif it exists. Adapt the brand voice for LinkedIn's professional-but-personal tone. If no voice file exists, default to professional, authentic, and insight-driven. -
Determine post type: Choose the format that fits the content:
- Personal Story -- Lessons from experience (
templates/personal-story.template) - Professional Insight -- Frameworks, observations, advice (
templates/professional-insight.template) - Contrarian Take -- Challenging industry assumptions (
templates/contrarian-take.template)
- Personal Story -- Lessons from experience (
-
Gather inputs: Topic, key message, target audience (role/seniority), any data or examples to include.
LinkedIn's Unique Culture
LinkedIn sits in a specific cultural space. Understand it before writing:
What works:
- Professional experiences told as personal stories
- Specific, actionable advice grounded in real experience
- Vulnerable honesty about failures and lessons learned
- Frameworks and mental models that help people think
- Data-backed observations about industry trends
- Asking genuine questions that invite thoughtful discussion
What doesn't work:
- Corporate press releases disguised as personal posts
- Vague inspirational platitudes ("Success is a journey, not a destination")
- Humblebragging ("I'm so humbled to announce...")
- Engagement bait without substance ("Agree?" "Thoughts?")
- Reposting motivational quotes with "This." as your commentary
- Multi-paragraph LinkedIn posts that are actually just ads
The core rule: LinkedIn rewards posts that make the reader think, learn, or reflect. If your post does none of these, rewrite it.
Post Structure
LinkedIn's algorithm and reading experience reward a specific structure:
Hook Line (First 1-2 Lines)
- This is the only thing visible before "...see more"
- It must create enough curiosity or value-promise to earn the click
- Strong hooks: bold claim, surprising observation, specific result, a question
- Weak hooks: "I'm excited to share...", "Big news!", generic statements
What ships with it
4 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 · 156 lines · 33 tokens per session scan A 85596a1241e7
content-linkedin is a skill published in the GitHub repository vstorm-co/content-skills (22 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 1,301 once invoked, about $0.0002 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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