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-profile-auditornpx skills add styfinity/linkedin-engine --skill linkedin-profile-auditorgit 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 | $0.00056 | $0.00549 |
| Opus 5 | $0.00028 | $0.00275 |
| Sonnet 5 | $0.00011 | $0.00110 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
linkedin-profile-auditor 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.
How it starts
The opening of the file, as written. The whole thing — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Profile Auditor
Your profile is the page a curious decision-maker lands on the second your post earns their attention. Every element has one job: turn that curiosity into a follow, a connect, or a DM. This skill judges each element against that job and rewrites the weakest.
Inputs
- The user's profile: headline, about, banner description, featured section, recent activity: $ARGUMENTS
- The brief (positioning, voice, offer, pains, proof) loads automatically.
Do this
Score each element against the one job it has, then name the weakest and rewrite it.
- Headline. Does a buyer get what you do, for whom, with what outcome, in two seconds? Or is it a job title?
- Banner. Does it carry one proof point or one offer? Or is it a stock graphic doing nothing?
- About. Does it open with the operator story and a real outcome, then the offer? Or does it list duties and skills?
- Featured. Is the lead magnet pinned with visible proof, the same one your posts point to? Or is it empty or off-message?
- Activity. Does the recent feed prove the competence you sell? Or does it contradict the headline?
- Name the single weakest element and rewrite it in full. Then rewrite the headline and the about-opening regardless.
Output
An element-by-element scorecard (each element: score out of 10, the one-line verdict, the gap). Then a rewritten headline and a rewritten about-opening, both in the brief's voice. End with the single highest-impact fix to make first, and why it beats the others.
Rules
- The profile must prove the operator, not list duties. Outcomes and specifics, not adjectives.
- Positioning is Revenue Partner who uses AI to do the heavy lifting. Never "AI consultant", "AI Growth Partner", or "AI agency".
- Real numbers are fine, attribute to "a post" or "a client", never a named person or company.
- No em-dashes. Use a comma, a period, or " - ".
- One highest-impact fix at the end, not a list. If the about needs a full rewrite, run /linkedin-humanizer on the result before it goes live.
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 · 33 lines · 56 tokens per session scan A b6a0e081564f
linkedin-profile-auditor is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 549 once invoked, about $0.0003 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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