medical-imaging-review

luwill/research-skills · updated Apr 8, 2026

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$npx skills add https://github.com/luwill/research-skills --skill medical-imaging-review
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summary

Write comprehensive literature reviews following a systematic 7-phase workflow.

skill.md

Medical Imaging AI Literature Review Skill

Write comprehensive literature reviews following a systematic 7-phase workflow.

Quick Start

  1. Initialize project with three core files:

    • CLAUDE.md - Writing guidelines and terminology
    • IMPLEMENTATION_PLAN.md - Staged execution plan
    • manuscript_draft.md - Main manuscript
  2. Follow the 7-phase workflow (see references/WORKFLOW.md)

  3. Use domain-specific templates (see references/DOMAINS.md)


Core Principles

Writing Style

  • Hedging language: "may", "suggests", "appears to", "has shown promising results"
  • Avoid absolutes: Never say "X is the best method"
  • Citation support: Every claim needs reference
  • Limitations: Each method section needs a Limitations paragraph

Required Elements

  • Key Points box (3-5 bullets) after title
  • Comparison table for each major section
  • Performance metrics: Dice (0.XXX), HD95 (X.XX mm)
  • Figure placeholders with detailed captions
  • References: 80-120 typical, organized by topic

Paragraph Structure

Topic sentence (main claim)
  → Supporting evidence (citations + data)
  → Analysis (critical evaluation)
  → Transition to next paragraph

Literature Sources

Use multi-source strategy for comprehensive coverage:

Source Best For Tools
ArXiv Latest DL methods, preprints search_papers, read_paper
PubMed Clinical validation, peer-reviewed pubmed_search_articles
Zotero Existing library, organized refs zotero_search_items

For MCP configuration details, see references/MCP_SETUP.md.


Standard Review Structure

# [Title]: State of the Art and Future Directions

## Key Points
- [3-5 bullets summarizing main findings]

## Abstract

## 1. Introduction
### 1.1 Clinical Background
### 1.2 Technical Challenges
### 1.3 Scope and Contributions

## 2. Datasets and Evaluation Metrics
### 2.1 Public Datasets (Table 1)
### 2.2 Evaluation Metrics

## 3. Deep Learning Methods
### 3.1 [Category 1]
### 3.2 [Category 2]
(Table 2: Method Comparison)

## 4. Downstream Applications

## 5. Commercial Products & Clinical Translation (Table 3)

## 6. Discussion
### 6.1 Current Limitations
### 6.2 Future Directions

## 7. Conclusion

## References

Method Description Template

### 3.X [Method Category]

[1-2 paragraph introduction with motivation]

**[Method Name]:** [Author] et al. [ref] proposed [method], which [innovation]:
- [Key component 1]
- [Key component 2]
Achieves Dice of X.XX on [dataset].

**Limitations:** Despite advantages, [category] methods face:
(1) [limit 1]; (2) [limit 2].

Citation Patterns

# Data citation
"...achieved Dice of 0.89 [23]"

# Method citation
"Gu et al. [45] proposed..."

# Multi-citation
"Several studies demonstrated... [12, 15, 23]"

# Comparative
"While [12] focused on..., [15] addressed..."

Reference Files

File Purpose
references/WORKFLOW.md Detailed 7-phase workflow
references/TEMPLATES.md CLAUDE.md and IMPLEMENTATION_PLAN.md templates
references/DOMAINS.md Domain-specific method categories
references/MCP_SETUP.md MCP server configuration
references/QUALITY_CHECKLIST.md Pre-submission quality checklist
how to use medical-imaging-review

How to use medical-imaging-review on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add medical-imaging-review
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/luwill/research-skills --skill medical-imaging-review

The skills CLI fetches medical-imaging-review from GitHub repository luwill/research-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/medical-imaging-review

Reload or restart Cursor to activate medical-imaging-review. Access the skill through slash commands (e.g., /medical-imaging-review) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.753 reviews
  • Daniel Johnson· Dec 28, 2024

    I recommend medical-imaging-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Aanya Yang· Dec 16, 2024

    Registry listing for medical-imaging-review matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aanya Chen· Dec 12, 2024

    Useful defaults in medical-imaging-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chaitanya Patil· Dec 4, 2024

    medical-imaging-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Piyush G· Nov 23, 2024

    medical-imaging-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Layla Bhatia· Nov 19, 2024

    medical-imaging-review reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sophia Chen· Nov 15, 2024

    medical-imaging-review has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kofi Iyer· Nov 7, 2024

    Useful defaults in medical-imaging-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Nia Jain· Nov 3, 2024

    Registry listing for medical-imaging-review matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Meera Chawla· Oct 26, 2024

    I recommend medical-imaging-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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