Medical Research Agent Skills is a library of agent instructions for medical and biomedical research, covering evidence analysis, study protocol design, data analysis, and academic writing. Researchers use it to guide compatible coding agents through common scientific workflows. The catalogue contains many of the library's skills and commands.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aipoch/medical-research-skillsnpx agentmods add skills/aipoch/medical-research-skills/linkedin-optimizerWrote 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/aipoch/medical-research-skills/linkedin-optimizer)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/linkedin-optimizer"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/linkedin-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/aipoch/medical-research-skills/linkedin-optimizer"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/linkedin-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 | $0.00044 | $0.02201 |
| Opus 5 | $0.00022 | $0.01100 |
| Sonnet 5 | $0.00009 | $0.00440 |
| Haiku 4.5 | $0.00004 | $0.00220 |
Grade A, and why
linkedin-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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Optimizer for Healthcare Professionals
Optimize LinkedIn profiles for doctors, physicians, nurses, and healthcare professionals to enhance professional visibility and career opportunities.
When to Use
- Use this skill when the task needs Use when optimizing LinkedIn profiles for doctors, physicians, nurses, healthcare professionals, or medical researchers. Crafts compelling headlines, writes professional summaries, integrates healthcare keywords, and builds personal branding for medical careers.
- Use this skill for other tasks that require explicit assumptions, bounded scope, and a reproducible output format.
- Use this skill when the response must stay inside the documented task boundary instead of expanding into adjacent work.
Key Features
- Scope-focused workflow aligned to: Use when optimizing LinkedIn profiles for doctors, physicians, nurses, healthcare professionals, or medical researchers. Crafts compelling headlines, writes professional summaries, integrates healthcare keywords, and builds personal branding for medical careers.
- Packaged executable path(s):
scripts/main.py. - Reference material available in
references/for task-specific guidance. - Structured execution path designed to keep outputs consistent and reviewable.
Dependencies
Python:3.10+. Repository baseline for current packaged skills.Third-party packages:not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.
Example Usage
cd "20260318/scientific-skills/Academic Writing/linkedin-optimizer"
python -m py_compile scripts/main.py
python scripts/main.py --help
Example run plan:
- Confirm the user input, output path, and any required config values.
- Edit the in-file
CONFIGblock or documented parameters if the script uses fixed settings. - Run
python scripts/main.pywith the validated inputs. - Review the generated output and return the final artifact with any assumptions called out.
What ships with it
3 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 · 279 lines · 44 tokens per session scan A d0ba6e75173a
linkedin-optimizer is a skill published in the GitHub repository aipoch/medical-research-skills (1,869 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 2,201 once invoked, about $0.0002 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 2026-09-03.
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