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 TaplioOfficial/taplio-linkedin-plugin --skill linkedin-repurposergit clone --depth 1 https://github.com/TaplioOfficial/taplio-linkedin-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/skills/taplioofficial/taplio-linkedin-plugin/linkedin-repurposer)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-plugin/linkedin-repurposer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-repurposer/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/taplioofficial/taplio-linkedin-plugin/linkedin-repurposer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-repurposer.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 | $0.00105 | $0.01188 |
| Opus 5 | $0.00053 | $0.00594 |
| Sonnet 5 | $0.00021 | $0.00238 |
| Haiku 4.5 | $0.00011 | $0.00119 |
Grade A, and why
linkedin-repurposer 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 10d 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.
This is a copy
100% identical to linkedin-repurposer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Repurposer
Take content that already exists somewhere else and turn it into a native LinkedIn post that does not feel copy-pasted.
When to trigger
The user says "turn this blog post into a LinkedIn post", "I posted this on Twitter, make it LinkedIn", "transcript of my podcast, make it a post", "repurpose this article".
Inputs to ask for
- The source content (paste, link, or file).
- The source format (blog, tweet, video transcript, podcast, newsletter, doc).
- The angle or takeaway they want to keep (optional). If missing, you pick.
Process
- Read the source. Identify the single strongest takeaway. Not three, not five. One.
- Strip everything that is format-specific :
- Blog posts : remove H2/H3 structure, remove "as we discussed earlier".
- Tweets : remove threads numbering, remove platform jokes.
- Video transcripts : remove "you know", "uh", "so basically", and timestamps.
- Newsletters : remove "in this issue", "subscribe at the bottom".
- Rewrite the hook for LinkedIn feed dynamics (curiosity, contrarian, payoff).
- Reformat the body in LinkedIn style : short lines, white space, one idea per line.
- Add a CTA that fits LinkedIn (question, not "click here").
Output format
SOURCE TAKEAWAY
[one-line summary of the core insight]
LINKEDIN POST
[hook line 1]
[hook line 2]
[body, reformatted, 80-200 words]
[CTA]
WHAT I CHANGED
- [bullet on the angle picked]
- [bullet on what was cut]
- [bullet on what was reframed]
Rules
- A LinkedIn post is not a blog post in disguise. Cut ruthlessly.
- One idea per post. If the source has 5 ideas, produce 5 posts.
- Never paste a tweet thread vertically and call it a LinkedIn post.
- Avoid "as I wrote on my blog". The reader is on LinkedIn. Stay there.
- If the source is a video, lead with the moment, not the topic. "Last week on the podcast we talked about pricing" is weak. "I changed my pricing 4 times in 6 months. Here is what worked" is strong.
Requires the Taplio MCP
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.
- 10d ago First seen · 81 lines · 105 tokens per session scan A e43139d7433d
linkedin-repurposer is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 105 tokens to every session and 1,188 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to linkedin-repurposer, differing in 0 lines, and is treated as a copy.
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