Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sarveshtalele/linkedin-content-skillnpx agentmods add skills/sarveshtalele/linkedin-content-skill/generate-carouselWrote 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/sarveshtalele/linkedin-content-skill/generate-carousel)<a href="https://agentmods.dev/skills/sarveshtalele/linkedin-content-skill/generate-carousel"><img src="https://agentmods.dev/badge/skills/sarveshtalele/linkedin-content-skill/generate-carousel.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 | $0.00047 | $0.00300 |
| Opus 5 | $0.00023 | $0.00150 |
| Sonnet 5 | $0.00009 | $0.00060 |
| Haiku 4.5 | $0.00005 | $0.00030 |
Grade A, and why
generate-carousel 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 8d 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.
What it actually says
You are an expert LinkedIn Content Strategist. A user wants to generate a LinkedIn Carousel.
Step 1 — Parse Arguments
The user's input is: $ARGUMENTS
Extract:
topic— carousel subject (required)niche— industry/niche (default: "AI & Technology")slides— number of slides (default: 7, range: 3–12)style— how-to | listicle | myth-busting | framework | story-arc (default: listicle)
Step 2 — Run the Prompt Builder
python3 scripts/generate_carousel.py --topic "<parsed_topic>" --niche "<parsed_niche>" --slides <parsed_slides> --style <parsed_style>
Step 3 — Generate the Carousel
Read the script output. Follow the instructions to generate numbered slide content + LinkedIn caption.
Step 4 — Show Output
Show slides clearly numbered, followed by the caption. Ready to use.
Step 5 — Ask for Feedback
🎯 Liked the format? Type
/feedback <what worked>to save this to memory.
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.
- 8d ago First seen · 31 lines · 0 tokens per session scan A 8077ade89ab9
generate-carousel is a skill published in the GitHub repository sarveshtalele/linkedin-content-skill (6 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 300 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 2026-08-31.
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