A structured methodology for conducting comprehensive research. This skill guides you through planning, executing, analyzing, and synthesizing research on any topic.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionresearch-workflowExecute the skills CLI command in your project's root directory to begin installation:
Fetches research-workflow from jwynia/agent-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 research-workflow. Access via /research-workflow in your agent's command palette.
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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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A structured methodology for conducting comprehensive research. This skill guides you through planning, executing, analyzing, and synthesizing research on any topic.
Use this skill when:
Do NOT use this skill when:
Before using this skill, ensure:
┌─────────────────────────────────────────────────────────────┐
│ RESEARCH WORKFLOW │
├─────────────────────────────────────────────────────────────┤
│ │
│ 1. PLANNING 2. EXECUTION │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Define │ │ Run searches │ │
│ │ questions │───>│ Evaluate │ │
│ │ Plan queries │ │ sources │ │
│ └──────────────┘ └──────────────┘ │
│ │ │ │
│ v v │
│ 3. ANALYSIS 4. SYNTHESIS │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Organize │ │ Create │ │
│ │ findings │───>│ coherent │ │
│ │ Find patterns│ │ output │ │
│ └──────────────┘ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
Before any searches, establish a clear research plan.
Convert the topic into specific, answerable questions.
Example:
Break down the main topic into searchable components:
Create a search plan with query progression:
Broad queries first: Get overall landscape
Specific queries next: Dive into details
Verification queries last: Confirm findings
Use the template at assets/research-plan-template.md to document your plan.
Execute your search plan systematically.
Execute queries in order, using appropriate search parameters:
# Broad overview
web-search "AI in healthcare overview 2024"
# Specific deep dive
web-search "AI diagnostic imaging applications" --depth advanced
# Current news
web-search "AI healthcare regulations 2024" --topic news --time month
For each search, record:
Use the checklist at assets/source-evaluation-checklist.md to assess:
Credibility Indicators:
Quality Signals:
Research is not linear. Based on findings:
Organize and analyze your collected findings.
Organize results into categories:
Look for:
For each finding, determine confidence level:
Document:
Create coherent, useful output from your analysis.
Choose appropriate format based on use case:
Use the template at assets/research-report-template.md.
Key principles:
End with practical outputs:
Phase 1 - Planning:
Research Question: What are the best practices for API versioning?
Sub-questions:
1. What versioning strategies exist?
2. What are pros/cons of each?
3. What do major companies use?
4. What do experts recommend?
Search Plan:
- "API versioning strategies comparison"
- "REST API versioning best practices 2024"
- "API versioning header vs URL vs query parameter"
- "large companies API versioning approach"
Phase 2 - Execution:
Query 1: "API versioning strategies comparison"
- Found: URL versioning, header versioning, query parameter
- Key insight: URL versioning most common, header more "RESTful"
- Sources: REST API tutorial, Martin Fowler blog
Query 2: "REST API versioning best practices 2024"
- Found: Semantic versioning principles apply
- Key insight: Version only when breaking changes
- Sources: API design guides, Stack Overflow discussions
Phase 3 - Analysis:
Consensus Points:
- Version only for breaking changes
- Be consistent within an API
- Document version lifecycle
Conflicts:
- URL vs header placement (no clear winner)
- When to deprecate old versions
Gaps:
- Limited data on performance impact
- Few studies on developer experience
Phase 4 - Synthesis:
Key Findings:
1. Three main strategies exist (URL, header, query param)
2. URL versioning is most common and discoverable
3. Header versioning is considered more "pure" REST
4. Version only on breaking changes
5. Major companies split between approaches
Recommendations:
- Use URL versioning for public APIs (discoverability)
- Consider header versioning for internal APIs
- Document deprecation timeline clearly
- Use semantic versioning principles
Before completing research, verify:
For detailed guidance, see:
This workflow has the following limitations:
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.
jwynia/agent-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend research-workflow for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
research-workflow has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: research-workflow is the kind of skill you can hand to a new teammate without a long onboarding doc.
research-workflow reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: research-workflow is focused, and the summary matches what you get after install.
Useful defaults in research-workflow — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
research-workflow reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for research-workflow matched our evaluation — installs cleanly and behaves as described in the markdown.
research-workflow has been reliable in day-to-day use. Documentation quality is above average for community skills.
research-workflow is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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