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 skills/styfinity/linkedin-engine/linkedin-booking-closernpx skills add styfinity/linkedin-engine --skill linkedin-booking-closergit clone --depth 1 https://github.com/styfinity/linkedin-engineWrote 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/styfinity/linkedin-engine/linkedin-booking-closer)<a href="https://agentmods.dev/skills/styfinity/linkedin-engine/linkedin-booking-closer"><img src="https://agentmods.dev/badge/skills/styfinity/linkedin-engine/linkedin-booking-closer.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 | $0.00044 | $0.00417 |
| Opus 5 | $0.00022 | $0.00209 |
| Sonnet 5 | $0.00009 | $0.00083 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
linkedin-booking-closer 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 3d 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
LinkedIn Booking Closer
Setter discipline: once they have said yes, stop selling and book the call. After a yes, every extra word is downside.
Inputs
- The thread at the "yes / show me" moment, and the booking link: $ARGUMENTS
- The brief (offer, the one result they want) loads automatically.
Do this
- Read the thread. Confirm it is a real yes, not a soft maybe. If it is a maybe, say so and stop - this is not the moment to book.
- Write one warm line plus the booking link. Nothing more.
- Tease one result, never the mechanism. The mechanism is the demo's job, so do not explain how it works here.
- Strip every extra sentence. No recap, no "as I mentioned", no second pitch. Once they have said yes, more words only risk talking them out of it.
Output
The one-line booking message with the link, labelled "Booking message". If the draft runs longer than one warm line plus the link, flag it: "Over-selling - this is too long, cut to one line."
Rules
- After a yes, stop selling and book. That is the whole job.
- One result-hook max. No mechanism dump, no feature list.
- No em-dashes, no exclamation marks, no "looking forward to it".
- Draft only. Nothing sends without BOTH the connected CLI/MCP layer AND explicit approval. Sending runs through that layer on the user's own LinkedIn session or provider key, and the 20 actions/day cap applies to new accounts. A calendar link is the one message you never want auto-sent.
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.
- 3d ago First seen · 29 lines · 44 tokens per session scan A 76c142068d8d
linkedin-booking-closer is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 417 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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