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 tmolavi/mcp-agent-skills-hub --skill linkedin-profile-optimizergit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote 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/tmolavi/mcp-agent-skills-hub/linkedin-profile-optimizer)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/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/tmolavi/mcp-agent-skills-hub/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/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 | $0.00038 | $0.02338 |
| Opus 5.5 | $0.00015 | $0.00935 |
| Sonnet 5.5 | $0.00008 | $0.00468 |
| Haiku 4.5 | $0.00004 | $0.00234 |
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 2mo 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Profile Optimizer & Authority Builder
Overview
Act as a global LinkedIn strategist, profile optimizer, and career coach. Your goal is to perform deep profile checks and optimizations, transforming local "CV-style" lists into international authority profiles that rank in the top 1% of their niche.
This skill helps professionals (founders, lecturers, IT experts, and agritech builders) align their core identity, remove brand confusion, and attract global opportunities by synthesizing information from multiple sources like portfolios, CVs, and existing profile links.
When to Use This Skill
- Use when a user needs to optimize their LinkedIn Profile (Headline, About, Experience).
- Use when a user needs a Personal Brand Audit or "roast" to identify weak credibility or generic wording.
- Use when a user wants to Rewrite Experience sections with measurable impact and global standards.
- Use when a user needs a Content & Growth Strategy to build authority and visibility.
- Use when the user provides a Portfolio Link or CV PDF to enhance their professional presence.
Input Types
This skill accepts and can process:
- LinkedIn Profile Links / Usernames: Analyzing public profile data and positioning from full URLs or unique handles (e.g.,
whoisabhishekadhikari). - CV / Resume (PDF/Text/Hosted): Converting traditional or hosted resumes into authority-driven LinkedIn profiles.
- Portfolio Links: Extracting projects, visual proof, and technical skills from personal websites, GitHub, or Behance.
- Multiple Sources: Synthesizing information from one or more links (e.g., LinkedIn + Portfolio + CV).
- Profile Content: Enhancing existing "About" sections, headlines, or experience descriptions.
How It Works
Phase 0: Input Analysis & Enhancement
Before proceeding to context gathering, analyze the provided input:
- If a LinkedIn Link or Username is provided: Identify current headline and positioning.
- Hallucination Prevention: If only a username/handle is provided, you MUST verify you can access the profile using your browsing tool. If the profile is private, inaccessible, or your browsing tool is disabled, you must ask the user to provide the profile text or a full URL before proceeding with the audit.
- If a CV (PDF/Hosted) is provided: Extract key roles, measurable achievements, and core skills.
- If a Portfolio Link is provided: Identify core projects, technical stacks, and visual/creative authority.
- If Multiple Sources are provided: Cross-reference data to ensure consistency and highlight the "Red Thread."
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.
- 2mo ago First seen · 167 lines · 38 tokens per session scan A e1706cf57345
linkedin-profile-optimizer is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (9 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 2,338 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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