ResumeSkills is a collection of AI-agent skills for improving resumes, preparing job applications, practicing interviews, and planning career moves. It is intended for job seekers, career changers, and professionals using Claude Code for tasks such as ATS checks, job-description matching, resume tailoring, and salary negotiation. The catalogue entry consists of the project's career-focused skills.
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 Paramchoudhary/ResumeSkills --skill linkedin-profile-optimizergit clone --depth 1 https://github.com/Paramchoudhary/ResumeSkillsWrote 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/paramchoudhary/resumeskills/linkedin-profile-optimizer)<a href="https://agentmods.dev/skills/paramchoudhary/resumeskills/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/paramchoudhary/resumeskills/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/paramchoudhary/resumeskills/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/paramchoudhary/resumeskills/linkedin-profile-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00018 | $0.02297 |
| Opus 5.5 | $0.00007 | $0.00919 |
| Sonnet 5.5 | $0.00004 | $0.00459 |
| Haiku 4.5 | $0.00002 | $0.00230 |
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 1mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- linkedin-profile-optimizer — 100% identical, 0 lines differ
- linkedin-profile-optimizer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Profile Optimizer
When to Use This Skill
Use this skill when the user wants to:
- Optimize their LinkedIn profile for job searching
- Improve LinkedIn visibility and searchability
- Sync their resume with their LinkedIn profile
- Attract recruiters and job opportunities
- Mentions: "LinkedIn", "LinkedIn profile", "optimize LinkedIn", "LinkedIn headline", "recruiter"
Core Capabilities
- Optimize headline for searchability
- Write compelling About/Summary sections
- Structure Experience section for impact
- Improve profile completeness score
- Add relevant keywords for recruiter searches
- Align LinkedIn with resume while leveraging platform differences
LinkedIn vs. Resume: Key Differences
| Aspect | Resume | |
|---|---|---|
| Length | 1-2 pages | Unlimited |
| Tone | Formal | More conversational |
| Keywords | Job-specific | Industry-wide |
| Audience | One specific employer | All recruiters |
| Updates | Per application | Always current |
| Personality | Minimal | Show more |
Profile Section Optimization
1. Profile Photo
Requirements:
- Professional headshot (not casual photo)
- Face takes up 60% of frame
- Neutral or branded background
- Good lighting, high resolution
- Appropriate attire for your industry
- Friendly expression (slight smile)
Impact: Profiles with photos get 21x more views
2. Background Banner
Best Practices:
- Use a professional design or industry-related image
- Can include personal branding elements
- Size: 1584 x 396 pixels
- Avoid busy patterns that distract from your photo
Options:
- Company brand (if appropriate)
- Industry-related imagery
- Professional abstract design
- Personal brand statement
3. Headline (Most Important for Searchability)
Character Limit: 220 characters
Formula: [Role] | [Key Expertise] | [Value Proposition]
Examples:
❌ Weak Headlines:
- "Looking for opportunities"
- "Unemployed Product Manager"
- "Student at University"
- "Open to work"
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.
- 1mo ago First seen · 371 lines · 18 tokens per session scan A 3fffcd58f30f
linkedin-profile-optimizer is a skill published in the GitHub repository Paramchoudhary/ResumeSkills (2,845 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 2,297 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…