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/natsummerance/readmd/linkedin-profile-optimizernpx skills add Natsummerance/readMD --skill linkedin-profile-optimizergit clone --depth 1 https://github.com/Natsummerance/readMDWrote 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/natsummerance/readmd/linkedin-profile-optimizer)<a href="https://agentmods.dev/skills/natsummerance/readmd/linkedin-profile-optimizer"><img src="https://agentmods.dev/badge/skills/natsummerance/readmd/linkedin-profile-optimizer.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.02297 |
| Opus 5 | $0.00009 | $0.01149 |
| Sonnet 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 4d 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 — 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.
- 4d ago First seen · 371 lines · 18 tokens per session scan A 3fffcd58f30f
linkedin-profile-optimizer is a skill published in the GitHub repository Natsummerance/readMD (21 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 2,297 once invoked, about $0.0001 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.
Other skills, from other repositories
gonavi-cli
Operate databases through the GoNavi headless CLI — the gonavi executable shipped in verified GitHub Release archives. Covers listing/adding/importing saved connections, running SQL queries against saved connections or ad-hoc connection files, exporting result sets to csv/json/md/html/xlsx, batch-executing SQL files…
bibverify
Verify, repair, explain, and generate BibTeX references with Bibverify's DOI-first CLI and MCP tools.
memory
Use this skill when Pioneer should proactively use durable memory or recalled context: decide whether memory can improve a turn, answer from remembered user/project facts, request memory tools, search/list/get stored memories, save durable preferences or project decisions, forget memories, audit or clean up memory, or…
trellis-brainstorm
Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex…
trellis-update-spec
Captures executable contracts and coding conventions into .trellis/spec/ documents. Use when learning something valuable from debugging, implementing, or discussion that should be preserved for future sessions.
trellis-break-loop
Deep bug analysis to break the fix-forget-repeat cycle. Analyzes root cause category, why fixes failed, prevention mechanisms, and captures knowledge into specs. Use after fixing a bug to prevent the same class of bugs.