perplexity-search▌
parcadei/continuous-claude-v3 · updated Apr 8, 2026
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Web search with AI-powered answers, deep research, and chain-of-thought reasoning.
Perplexity AI Search
Web search with AI-powered answers, deep research, and chain-of-thought reasoning.
When to Use
- Direct web search for ranked results (no AI synthesis)
- AI-synthesized research with citations
- Chain-of-thought reasoning for complex decisions
- Deep comprehensive research on topics
Models (2025)
| Model | Purpose |
|---|---|
sonar |
Lightweight search with grounding |
sonar-pro |
Advanced search for complex queries |
sonar-reasoning-pro |
Chain of thought reasoning |
sonar-deep-research |
Expert-level exhaustive research |
Usage
Quick question (AI answer)
uv run python scripts/mcp/perplexity_search.py \
--ask "What is the latest version of Python?"
Direct web search (ranked results, no AI)
uv run python scripts/mcp/perplexity_search.py \
--search "SQLite graph database patterns" \
--max-results 5 \
--recency week
AI-synthesized research
uv run python scripts/mcp/perplexity_search.py \
--research "compare FastAPI vs Django for microservices"
Chain-of-thought reasoning
uv run python scripts/mcp/perplexity_search.py \
--reason "should I use Neo4j or SQLite for small graph under 10k nodes?"
Deep comprehensive research
uv run python scripts/mcp/perplexity_search.py \
--deep "state of AI agent observability 2025"
Parameters
| Parameter | Description |
|---|---|
--ask |
Quick question with AI answer (sonar) |
--search |
Direct web search - ranked results without AI synthesis |
--research |
AI-synthesized research (sonar-pro) |
--reason |
Chain-of-thought reasoning (sonar-reasoning-pro) |
--deep |
Deep comprehensive research (sonar-deep-research) |
Search-specific options
| Parameter | Description |
|---|---|
--max-results N |
Number of results (1-20, default: 10) |
--recency |
Filter: day, week, month, year |
--domains |
Limit to specific domains |
Mode Selection Guide
| Need | Use | Why |
|---|---|---|
| Quick fact | --ask |
Fast, lightweight |
| Find sources | --search |
Raw results, no AI overhead |
| Synthesized answer | --research |
AI combines multiple sources |
| Complex decision | --reason |
Chain-of-thought analysis |
| Comprehensive report | --deep |
Exhaustive multi-source research |
Examples
# Find recent sources on a topic
uv run python scripts/mcp/perplexity_search.py \
--search "OpenTelemetry AI agent tracing" \
--recency month --max-results 5
# Get AI synthesis
uv run python scripts/mcp/perplexity_search.py \
--research "best practices for AI agent logging 2025"
# Make a decision
uv run python scripts/mcp/perplexity_search.py \
--reason "microservices vs monolith for startup MVP"
# Deep dive
uv run python scripts/mcp/perplexity_search.py \
--deep "comprehensive guide to building feedback loops for autonomous agents"
API Key Required
Requires PERPLEXITY_API_KEY in environment or ~/.claude/.env.
How to use perplexity-search on Cursor
AI-first code editor with Composer
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 perplexity-search
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches perplexity-search from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate perplexity-search. Access the skill through slash commands (e.g., /perplexity-search) 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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★33 reviews- ★★★★★Pratham Ware· Dec 28, 2024
I recommend perplexity-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Dhruvi Jain· Dec 24, 2024
perplexity-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zaid Abebe· Dec 4, 2024
Solid pick for teams standardizing on skills: perplexity-search is focused, and the summary matches what you get after install.
- ★★★★★Mateo Malhotra· Nov 23, 2024
perplexity-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Harper Sethi· Nov 19, 2024
perplexity-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Oshnikdeep· Nov 15, 2024
Keeps context tight: perplexity-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Harper Harris· Oct 14, 2024
Useful defaults in perplexity-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Harper Dixit· Oct 10, 2024
I recommend perplexity-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Oct 6, 2024
Registry listing for perplexity-search matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arjun Liu· Sep 21, 2024
Registry listing for perplexity-search matched our evaluation — installs cleanly and behaves as described in the markdown.
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