linkedin-skills is a collection of Claude Code and Codex skills for creating and managing LinkedIn content from a terminal. It helps users draft posts, comments, and replies, review their feeds, and plan a publishing cadence while requiring approval before publication. The catalogue entries are the project's skills, instructions, and plugin for using these workflows with coding agents.
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 sergebulaev/linkedin-skills --skill linkedin-profile-optimizergit clone --depth 1 https://github.com/sergebulaev/linkedin-skillsWrote 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/sergebulaev/linkedin-skills/linkedin-profile-optimizer)<a href="https://agentmods.dev/skills/sergebulaev/linkedin-skills/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/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/sergebulaev/linkedin-skills/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-profile-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00090 | $0.01269 |
| Opus 5 | $0.00045 | $0.00634 |
| Sonnet 5 | $0.00018 | $0.00254 |
| Haiku 4.5 | $0.00009 | $0.00127 |
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.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Profile Optimizer
Audit the nine components of a LinkedIn profile (photo, banner, headline, About, Featured, Experience, Skills, custom URL, recommendations) against 2026 best practices, then rewrite each section that needs it. Optimized profiles get ~3.9x more views and convert visitors 3-5x better than default/resume-style profiles.
When to use
- User pastes their LinkedIn profile URL and asks for an audit
- User wants to rewrite their headline, About section, or Featured section
- User is launching a content strategy and needs the profile to match
- Any of: "review my profile", "fix my headline", "optimize bio", "profile audit", "LinkedIn optimization"
Input
- Profile URL (or screenshots of sections)
- Goal: clients / job seeking / authority — Featured and CTA vary by goal
- Optional: draft content to grade against the existing profile
Output
A structured audit + rewrite in this shape:
- Scorecard (9 sections, pass/fail/needs-work)
- Priority fixes (ranked by impact)
- Before → After rewrites for each failing section
- Expected uplift (based on benchmark data)
Steps
- Intake. Collect profile state + goal. Flag missing sections.
- Score each of 9 sections against the checklist (see references/).
- Rewrite headline using
[What You Do] | [Who You Help] [Achieve What Result]— fit all 220 chars. - Rebuild About with 7-step structure; verify first 265-275 chars hook before "see more".
- Curate Featured (3 strong items) matched to the goal:
- Clients: lead magnet + case study with results + calendar link
- Job seeking: portfolio + best work samples + top-performing post
- Authority: best content + media/podcast features + newsletter signup
- Rewrite Experience bullets as
action verb + specific metric. Add 5+ skills per role. Pin top 3 skills. - Claim custom URL (linkedin.com/in/firstnamelastname, not the
-123abc456default). - Draft recommendation requests with specifics ("about [project/skill]") — don't send LinkedIn's generic template.
- Deliver before/after diff + expected uplift (3.9x views, 3-5x conversion, 71% more likely to land interviews).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 91 lines · 90 tokens per session scan A 202c7e58dc15
linkedin-profile-optimizer is a skill published in the GitHub repository sergebulaev/linkedin-skills (1,302 stars, last pushed today), licensed MIT. It adds 90 tokens to every session and 1,269 once invoked, about $0.0005 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 2026-08-30.
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linkedin-analytics-interpreter
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linkedin-audience-persona-builder
Build a sharp, post-ready persona of the user's target LinkedIn audience : role, pains, jobs to be done, vocabulary, aspirations, what content they consume, what objections they raise. Use when the user is starting on LinkedIn or when their content does not resonate (low comments, no DMs, traffic without conversion).…
linkedin-content-pillars-builder
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linkedin-niche-definer
Help the user define (or sharpen) their LinkedIn niche : audience, problem they solve, unique angle, and one-line positioning. The skill walks the user through a 7-question diagnostic, then synthesizes a positioning statement they can use across headline, About, and posts. Use when the user says "I do not know what to…
linkedin-post-performance-critic
Cold-read a LinkedIn post draft and audit it across 6 dimensions (hook, structure, scannability, specificity, CTA, voice). Returns a score per dimension, the 2 most important fixes, and a rewrite of the weakest section. Use BEFORE publishing, when the user wants a sanity check from a critic that does not love…