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-onboardnpx skills add styfinity/linkedin-engine --skill linkedin-onboardgit clone --depth 1 https://github.com/styfinity/linkedin-engineWhat 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 | $0.00035 | $0.00533 |
| Opus 5 | $0.00017 | $0.00267 |
| Sonnet 5 | $0.00007 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
linkedin-onboard 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 2d 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 — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Onboard
The setup pass. This skill assembles the one file every other skill reads: linkedin-brief.md. Run it once, in the user's own words and evidence, and the whole engine knows the voice, the market, and the offer.
Inputs
- The user's business, who they sell to, their offer, and a paste of their past posts and DMs (the more real text, the better): $ARGUMENTS
- Nothing loads automatically yet. This skill is what creates the brief that loads from now on.
Do this
- Run /linkedin-icp-definer on the business and buyer details. Capture the ICP it returns: who, role, company shape, the trigger that makes them a fit.
- Run /linkedin-voice-profiler on the pasted posts and DMs. Capture the voice profile: cadence, sentence length, words they reach for, words they never use.
- Capture the offer in the user's language: one sentence (what it is), the outcome it produces, the proof behind it, and the risk reversal (the guarantee or the safety net).
- Capture the top three to five buyer pains in the buyer's words, pulled from the paste where possible, not paraphrased into jargon.
- Assemble all of it into one file and write it to linkedin-brief.md in the project root, with clear sections: ICP, Voice profile, Offer, Pains.
Output
The assembled linkedin-brief.md (ICP, voice profile, offer, pains), shown in full for the user to confirm. End with a one-line note: the SessionStart hook will load this file into every session from now on, so no other skill will ask for this context again.
Rules
- Build only from the user's real evidence and real words. If a section is thin because the paste was thin, say so and ask for more text rather than inventing it.
- The offer positions the user as a Revenue Partner who uses AI to do the heavy lifting. Never "AI consultant" or "AI agency".
- This is the file every other skill reads. Get it right before running anything else.
- No em-dashes. Draft first: show the assembled brief, the user confirms, then it is saved.
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.
- 2d ago First seen · 30 lines · 35 tokens per session scan A 9fccda8d7c6e
linkedin-onboard is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 533 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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