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 agentmods add commands/az9713/claude-cowork-content-plugin/linkedingit clone --depth 1 https://github.com/az9713/claude-cowork-content-pluginWrote 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/commands/az9713/claude-cowork-content-plugin/linkedin)<a href="https://agentmods.dev/commands/az9713/claude-cowork-content-plugin/linkedin"><img src="https://agentmods.dev/badge/commands/az9713/claude-cowork-content-plugin/linkedin.svg" alt="Measured on agentmods" 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 | $0.00007 | $0.00111 |
| Opus 5 | $0.00003 | $0.00056 |
| Sonnet 5 | $0.00001 | $0.00022 |
| Haiku 4.5 | $0.00001 | $0.00011 |
Grade A, and why
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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin — 100% identical, 0 lines differ
What it actually says
Generate LinkedIn posts from the following content: $ARGUMENTS
Use the linkedin-post skill to create the posts. Follow these steps:
- Read the source content thoroughly
- Ask the user how many LinkedIn posts they want
- Generate the requested number of posts following LinkedIn best practices
- Each post should have a unique angle and style
- Save all posts to a markdown file in the workspace folder
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.
- 6d ago First seen · 15 lines · 7 tokens per session scan A 4f5433ee1c46
linkedin is a command published in the GitHub repository az9713/claude-cowork-content-plugin (16 stars, last pushed 7mo ago), licensed MIT. It adds 7 tokens to every session and 111 once invoked, about $0.0000 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.
Other commands, from other repositories
all
List all available content repurposing commands and skills.
extract
Quick content extraction from any source into a structured table of ideas.
Generate LinkedIn posts from your content.
quotes
Extract quotable statements from your content.
titles
Generate compelling titles for your content.
Generate Twitter/X threads from your content.