Write comprehensive literature reviews following a systematic 7-phase workflow.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmedical-imaging-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches medical-imaging-review from luwill/research-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate medical-imaging-review. Access via /medical-imaging-review in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Write comprehensive literature reviews following a systematic 7-phase workflow.
Initialize project with three core files:
CLAUDE.md - Writing guidelines and terminologyIMPLEMENTATION_PLAN.md - Staged execution planmanuscript_draft.md - Main manuscriptFollow the 7-phase workflow (see references/WORKFLOW.md)
Use domain-specific templates (see references/DOMAINS.md)
Topic sentence (main claim)
→ Supporting evidence (citations + data)
→ Analysis (critical evaluation)
→ Transition to next paragraph
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.
# [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
### 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].
# 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..."
| 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 |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend medical-imaging-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for medical-imaging-review matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in medical-imaging-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
medical-imaging-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
medical-imaging-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
medical-imaging-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
medical-imaging-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in medical-imaging-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for medical-imaging-review matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend medical-imaging-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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