DEV Community: The_resa

Medical Research Agent Skills: Blind Review Sanitizer

The_resa — Tue, 14 Apr 2026 01:54:30 +0000

You can explore a growing collection of Medical Research Agent Skills on AIPOCH Github.

If you find it useful, consider giving it a ⭐ to support the project!

If you want to explore more about this skill—including Complete Workflow Example, Common Patterns, Quality Checklist, Common Pitfalls, Troubleshooting, References, and more—please visit this page: Blind Review Sanitizer.

What is Blind Review Sanitizer?

Automatically anonymize academic manuscripts for double-blind peer review by removing author identifiers, institutional affiliations, acknowledgments, and excessive self-citations while preserving document formatting and scholarly content integrity.

What Are The Key Capabilities of This Agent Skill?

Author Identity Removal: Automatically detect and redact author names, institutional affiliations, and contact information using pattern matching and customizable rules
Acknowledgment Section Sanitization: Identify and remove or flag acknowledgment sections that may reveal author identity through funding sources or personal thanks
Self-Citation Detection and Neutralization: Identify first-person citations and excessive self-references that could deanonymize the submission
Multi-Format Document Support: Process DOCX, Markdown, and plain text files with format-aware sanitization strategies
Audit Trail Generation: Create detailed logs of all redactions made for verification and transparency

Limitations and Considerations

Important Limitations:

Not Foolproof: Automated sanitization cannot guarantee complete anonymity. Always perform manual verification.
Context Blindness: Pattern matching may miss context-dependent identifiers or incorrectly flag legitimate content.
Image Processing: This tool processes text only. Images, figures, and embedded objects may contain identifying information not detected.
LaTeX Support: Limited support for LaTeX source files. Consider using LaTeX-specific tools for LaTeX manuscripts.
Language Support: Optimized for English and Chinese. Other languages may have reduced accuracy.

Ethical and Legal Considerations:

Author Consent: Ensure all authors consent to anonymization before submission
Copyright: Anonymization does not change copyright ownership
Data Availability: Some journals require non-anonymized versions for data/code availability statements
Post-Acceptance: Plan for deanonymization process after paper acceptance

Explore AIPOCH Agent Skills

Researchers and AI agents can explore the growing library of medical research agent skills through multiple resources:

Open-Source Repository on GitHub
AIPOCH Medical Research Agent Skills List – Browse all skills organized by category.
Full Agent Skills Overview – Learn about the purpose and capabilities of each skill in detail.

These resources make it easy to explore, validate, and experiment with AIPOCH’s growing library.

AIPOCH Medical Skill Auditor: How We Evaluates Agent Skills?

The_resa — Thu, 09 Apr 2026 06:48:57 +0000

You can explore a growing collection of Medical Research Agent Skills on the AIPOCH Github.

If you find it useful, consider giving it a ⭐ to support the project!

What is Medical Skill Auditor?

AIPOCH Medical Skill Evaluator is a framework for assessing the quality of AIPOCH's Agent Skills. Its core function is to perform a comprehensive quality check on a Skill before it is released to users.

How does Medical Skill Auditor Work?

Veto Gates

To enforce strict quality control, Skill Auditor is designed with two layers of veto mechanisms. Any failure in these checks may lead to immediate rejection of a skill.

Skill Veto

Take the agent skill “medical-research-literature-reader-pro” as an example：

Operational Stability
Structural Consistency
Result Determinism
System Security

Research Veto

Take the agent skill “medical-research-literature-reader-pro” as an example：

Scientific Integrity
Practice Boundaries
Methodological Ground
Code Usability

Core Capability

Take the agent skill “medical-research-literature-reader-pro” as an example：

Evaluates a skill’s design and contract against key dimensions such as Functional Suitability, Reliability, Performance & Context, Agent Usability, Human Usability, Security, Agent-Specific and Maintainability.

Medical Task
Take the agent skill “medical-research-literature-reader-pro” as an example：

Assesses actual outputs of a skill with layered criteria.

For skill testing, the AI automatically generates inputs. The number of inputs in specific categories will increase or decrease depending on the complexity of the skill. The following 7 inputs represent the most comprehensive version.

Canonical
Variant A
Edge
Variant B
Stress
Scope Boundary
Adversarial

Skill Complexity Classification

Label	Code/Rank	Definition
Simple	S	Narrow task scope
Moderate	M	Moderate branching or multiple task types
Complex	C	Broad or multi-step specialized skill

Simple (S): 3 inputs

Moderate (M): 5 inputs

Complex (C): 7 inputs

Final Score

Take the agent skill “medical-research-literature-reader-pro” as an example：

The Skill Evaluator uses a two-stage scoring system: static evaluation (design quality, accounting for 40%) and dynamic evaluation (runtime performance, accounting for 60%). The final overall score is derived by combining both.

Static (40%)
Dynamic (60%)

Final Score = Static Score × 40% + Dynamic Score × 60%

You can view evaluation results for selected AIPOCH skills here.

Feedback and possible future directions

This framework is still under active development.Right now it is only applied to a subset of AIPOCH’s skills, but we’re considering expanding it more broadly.

😎AIPOCH – 450+ Modular Agent Skills for Medical Research

The_resa — Wed, 25 Mar 2026 09:10:00 +0000

Hi! I’m part of the team building AIPOCH, an open-source library of 450+ executable Agent Skills designed specifically for medical research workflows.

AIPOCH GitHub Repository
see our Website here

Why we built AIPOCH?

Most medical research AI tools today are essentially a bundle of prompt engineering + fixed toolchains + a UI. They handle "published knowledge" well (like summarizing a paper), but they fall apart the moment you say: "Now validate this hypothesis using my own cohort data." Existing tools often lack a persistent research context. There is no version-controlled hypothesis tracking, no seamless link between literature evidence and actual data execution. We wanted to move beyond point-solutions to a modular, extensible protocol.

What is AIPOCH？

AIPOCH is a curated library of 450+ Medical Research Agent Skills, built to work with OpenClaw and other AI agent platforms, including OpenCode and Claude. To achieve this, we have encoded specialized medical research logic directly into our Skills.

Scientific Integrity Constraints
Study type identification
Medically Specialized Prompt Logic

A Skill is a structured capability package consisting of:

skill.md: A "contract" containing YAML metadata (trigger logic) and specific operational steps.
Python Scripts: Executable engines called directly via bash under the guidance of the skill.md.

In the context of AIPOCH, we define our developed skills as structured capability packages designed for professional medical research tasks, utilizing skill.md as the trigger contract and Python scripts as the execution engine. We have embedded medical research constraints directly into our skill.md, references, and Python scripts.

AIPOCH Medical Skill Auditor (in development)

What is Medical Skill Auditor?

Skill Auditor is AIPOCH’s evaluation framework under active development for scoring Medical Research Agent Skills with rigorous, multi‑dimensional quality metrics. It’s intended to go beyond static descriptions by measuring both core capability and real execution performance—giving users and developers a clearer, data‑driven understanding of skill quality.

How does it work?

🧰 Core Capability
Evaluates a skill’s design and contract against key dimensions such as Functional Suitability, reliability, performance & context, Agent Usability, human usability, Security, Agent-Specific and maintainability.

📊 Medical Task
Assesses actual outputs of a skill with layered criteria, weighting general competence and category‑specific behaviors to reflect real‑world execution quality.

🚫Veto Gates
To enforce strict quality control, Skill Auditor is designed with two layers of veto mechanisms. Any failure in these checks may lead to immediate rejection of a skill.

Skill Veto
Operational Stability
Structural Consistency
Result Determinism
System Security
Research Veto
Scientific Integrity
Practice Boundaries
Methodological Ground
Code Usability

The Most Frustrating Moment

One of our biggest early mistakes was using a cheaper LLM to "vibe coding" the initial batch of scripts.
On the surface, it worked. The scripts ran, and the logic seemed okay. The nightmare only surfaced during our audit: we realized the executing agent was silently correcting the script's logic on the fly. Because the agent read the intent in skill.md, it would "patch" the sloppy edge cases and vague error branches in the Python code during execution.
The result? We were burning massive amounts of extra tokens just to fix errors that shouldn't have existed. It didn't throw an error; it just showed up on the API bill.
We eventually scrapped the lot. We learned the hard way: Quantity isn't a moat; high-quality scripts are.

All questions/feedback welcome！😎😎😎