Conduct a systematic literature review on "$ARGUMENTS" using the paper and paper-search CLI tools.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionliterature-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches literature-review from collaborative-deep-research/agent-papers-cli and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate literature-review. Access via /literature-review in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Conduct a systematic literature review on "$ARGUMENTS" using the paper and paper-search CLI tools.
Before searching, clarify with the user:
Search with multiple query variations to maximize coverage:
paper-search semanticscholar papers "<main query>" --limit 20 --year <range>
paper-search semanticscholar papers "<synonym query>" --limit 20 --year <range>
paper-search semanticscholar papers "<related query>" --limit 20 --year <range>
paper-search google scholar "<topic>"
Deduplicate results by title/paper ID.
For each unique paper found:
paper-search semanticscholar details <paper_id>
paper skim <arxiv_id> --lines 2
Categorize as: highly relevant / somewhat relevant / not relevant.
For highly relevant papers:
paper outline <arxiv_id>
paper read <arxiv_id> introduction
paper read <arxiv_id> method
paper read <arxiv_id> results
paper read <arxiv_id> conclusion
Take structured notes on each paper: problem, method, key results, limitations.
For seminal papers, find related work:
paper-search semanticscholar citations <paper_id> --limit 20
paper-search semanticscholar references <paper_id> --limit 20
Add any important papers discovered this way back to the triage step.
Organize findings by theme, not by paper. Include:
paper bibtex <arxiv_id> to generate)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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Useful defaults in literature-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added literature-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: literature-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
literature-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend literature-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: literature-review is focused, and the summary matches what you get after install.
literature-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: literature-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in literature-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added literature-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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