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
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 aipoch/medical-research-skills --skill blind-review-sanitizergit clone --depth 1 https://github.com/aipoch/medical-research-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/aipoch/medical-research-skills/blind-review-sanitizer)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/blind-review-sanitizer"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/blind-review-sanitizer/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/blind-review-sanitizer"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/blind-review-sanitizer.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 | $0.00035 | $0.01797 |
| Opus 5 | $0.00017 | $0.00898 |
| Sonnet 5 | $0.00007 | $0.00359 |
| Haiku 4.5 | $0.00003 | $0.00180 |
Grade A, and why
blind-review-sanitizer 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blind Review Sanitizer
Structured manuscript anonymization for double-blind peer review.
Quick Check
Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Audit-Ready Commands
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
python -m py_compile scripts/main.py
python scripts/main.py --help
When to Use
- Use this skill when the task needs removal or review of author-identifying content in manuscripts prepared for double-blind submission.
- Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
- Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
Workflow
- Confirm the submission target, source file type, anonymization strictness, and whether acknowledgments should be preserved.
- Check whether the provided material is a supported file format and whether author names or known identifiers are available.
- Use the packaged script for supported files; otherwise produce a manual anonymization checklist without claiming full sanitization.
- Return the sanitized artifact or a verification plan that separates changes made, remaining risks, and manual review points.
- If the request lacks a file path or enough identifiers, stop and request the minimum missing input.
Use Cases
- Blind a manuscript before conference submission
- Review acknowledgments and self-citations for deanonymization risk
- Produce a manual anonymity checklist when automated processing is not possible
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
--input, -i |
string | Yes | - | Input manuscript file path (.docx, .md, .txt) |
--output, -o |
string | No | auto-generated | Output path with blinded suffix when omitted |
--authors |
string | No | - | Comma-separated author names for stronger detection |
--keep-acknowledgments |
flag | No | false | Preserve acknowledgment section |
--highlight-self-cites |
flag | No | false | Highlight self-citations without replacement |
What ships with it
4 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 · 198 lines · 35 tokens per session scan A a8fc28e35210
blind-review-sanitizer is a skill published in the GitHub repository aipoch/medical-research-skills (1,869 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 1,797 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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