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 TaplioOfficial/taplio-linkedin-plugin --skill linkedin-profile-optimizergit clone --depth 1 https://github.com/TaplioOfficial/taplio-linkedin-pluginWrote 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/taplioofficial/taplio-linkedin-plugin/linkedin-profile-optimizer)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-plugin/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-profile-optimizer/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/taplioofficial/taplio-linkedin-plugin/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-profile-optimizer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00084 | $0.01549 |
| Opus 5 | $0.00042 | $0.00775 |
| Sonnet 5 | $0.00017 | $0.00310 |
| Haiku 4.5 | $0.00008 | $0.00155 |
Grade A, and why
linkedin-profile-optimizer 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.
This is a copy
100% identical to linkedin-profile-optimizer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Profile Optimizer
A LinkedIn profile is a landing page. This skill audits it like one.
When to trigger
The user says "audit my profile", "improve my LinkedIn bio", "rewrite my headline", "people visit my profile but do not follow", "my profile does not convert".
Inputs to ask for
- The current headline.
- The current About / summary.
- The current banner (description or screenshot).
- The current Featured section (what it shows).
- The user's positioning : who they help, with what, and what they want visitors to do.
- The desired CTA from the profile (follow, book a call, sign up to newsletter, DM keyword).
What to audit (in order of impact)
- Headline : the single highest-leverage line on LinkedIn. Visible in feed, in search, in comments. Must answer "who do you help with what" in 220 characters.
- Banner : visible above the fold. Should reinforce the positioning, not display random stock art.
- Profile photo : clear face, eye contact, neutral background. No group photos, no holiday selfies.
- About section : lead with a hook. Explain who you help, how, and what to do next. End with a clear CTA.
- Featured section : 3 to 5 items max. Lead magnet, top post, link to call booking, link to newsletter, anything that converts.
- Experience : the current role description should match the headline promise. Past roles should support the credibility, not list every JD bullet.
Process
- Score each element on a 1-5 scale (1 = "wastes the slot", 5 = "best in class").
- List the top 3 fixes in order of impact (do not overwhelm with 12 fixes).
- For each fix, rewrite the copy so the user can ship the change in 2 minutes.
Output format
PROFILE AUDIT FOR [user]
POSITIONING IN ONE LINE (as I read it from the current profile) :
"[1-line summary, then ask : is this the positioning you actually want ?]"
SCORES
- Headline : X/5 - [one-liner diagnosis]
- Banner : X/5 - [diagnosis]
- Photo : X/5 - [diagnosis, only if obviously off]
- About : X/5 - [diagnosis]
- Featured : X/5 - [diagnosis]
- Experience : X/5 - [diagnosis]
TOP 3 FIXES (in order of impact)
FIX 1 - [element]
Current : "[paste current copy]"
Rewrite :
"[new copy ready to paste]"
Why this is better : [one-liner]
FIX 2 - [element]
Current : "[paste current copy]"
Rewrite :
"[new copy]"
Why this is better : [one-liner]
FIX 3 - [element]
Current : ...
Rewrite : ...
Why this is better : ...
QUICK WINS (under 5 minutes)
- [3 to 5 micro-improvements the user can ship today]
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 · 109 lines · 84 tokens per session scan A 30e2c5d6a02d
linkedin-profile-optimizer is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 84 tokens to every session and 1,549 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to linkedin-profile-optimizer, differing in 0 lines, and is treated as a copy.
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