Orchestrates multi-source web research by delegating to subagents, synthesizing findings, and producing cited reports.
Works with
Breaks research questions into 2–5 distinct subtopics, creates a research plan file, and spawns up to 3 subagents in parallel for efficient investigation
Each subagent performs 3–5 web searches per subtopic and writes findings to local files with key facts, quotes, and source URLs
Synthesizes results by reading local findings files, integrating insights across subtop
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionweb-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches web-research from langchain-ai/deepagents 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 web-research. Access via /web-research 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.
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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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Before delegating to subagents, you MUST:
Create a research folder - Organize all research files in a dedicated folder relative to the current working directory:
mkdir research_[topic_name]
This keeps files organized and prevents clutter in the working directory.
Analyze the research question - Break it down into distinct, non-overlapping subtopics
Write a research plan file - Use the write_file tool to create research_[topic_name]/research_plan.md containing:
Planning Guidelines:
For each subtopic in your plan:
Use the task tool to spawn a research subagent with:
research_[topic_name]/findings_[subtopic].mdRun up to 3 subagents in parallel for efficient research
Subagent Instructions Template:
Research [SPECIFIC TOPIC]. Use the web_search tool to gather information.
After completing your research, use write_file to save your findings to research_[topic_name]/findings_[subtopic].md.
Include key facts, relevant quotes, and source URLs.
Use 3-5 web searches maximum.
After all subagents complete:
Review the findings files that were saved locally:
list_files research_[topic_name] to see what files were createdread_file with the file paths (e.g., research_[topic_name]/findings_*.md)read_file for LOCAL files only, not URLsSynthesize the information - Create a comprehensive response that:
Write final report (optional) - Use write_file to create research_[topic_name]/research_report.md if requested
Note: If you need to fetch additional information from URLs, use the fetch_url tool, not read_file.
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.
supercent-io/skills-template
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
Keeps context tight: web-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: web-research is focused, and the summary matches what you get after install.
web-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
web-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
web-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
web-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: web-research is focused, and the summary matches what you get after install.
I recommend web-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
web-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in web-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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