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 swan-gtm/gtm-skills --skill linkedin-post-to-newslettergit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/linkedin-post-to-newsletter)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-post-to-newsletter"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-post-to-newsletter/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/swan-gtm/gtm-skills/linkedin-post-to-newsletter"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-post-to-newsletter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00153 | $0.01331 |
| Opus 5 | $0.00077 | $0.00665 |
| Sonnet 5 | $0.00031 | $0.00266 |
| Haiku 4.5 | $0.00015 | $0.00133 |
Grade A, and why
linkedin-post-to-newsletter 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repurpose A LinkedIn Post Into A Newsletter Issue
Transform a high-performing LinkedIn post into a longer-form educational newsletter that educates your audience, nurtures trust, and drives sales — without starting from scratch.
Why This Works
Your best LinkedIn posts are already proven. The hook worked. The idea resonated. The engagement was there.
But LinkedIn has limits. No room for deeper examples, mini case studies, or additional commentary.
This skill takes what's already working and expands it into a richer email experience — without bloating the content or diluting the core message.
Workflow
- Ask the user for the 3 inputs below
- Write the full newsletter email using the 5-part framework
- Flag any sections where you need more context before fabricating
What To Ask For
Request all 3 inputs at once:
To repurpose your LinkedIn post into a newsletter, I need 3 things:
1. The original LinkedIn post Paste the full text of the post.
2. What you want to emphasize or expand Tell me what to go deeper on. For example: "Add a mini case study to tip #2" or "Expand the framework section with a real example."
3. Any additional context, anecdotes, or stats (optional) Anything you want included — a client story, a personal experience, a specific number or result. If you leave this blank, I'll flag where I'd want more context rather than making something up.
The 5-Part Newsletter Framework
Part 1: Subject Line
- Under 42 characters
- Creates a curiosity gap — tease the insight without giving it away
- Use sentence case (not Title Case)
- Write 2 subject line options for the user to choose from
Examples:
- "The email mistake killing your open rates"
- "Why your lead magnet isn't converting"
- "What I learned from 100 welcome sequences"
Part 2: Opening Section
- Reintroduce the core message or hook from the LinkedIn post
- Add brief personal context if it strengthens the angle
- Set expectations for what the email will cover
- Keep it tight — 2–4 short paragraphs max
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 · 161 lines · 153 tokens per session scan A c44a17047991
linkedin-post-to-newsletter is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 153 tokens to every session and 1,331 once invoked, about $0.0008 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.
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