Fetches research-external from parcadei/continuous-claude-v3 and configures it for Cursor.
3
Select Cursor when prompted
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│ ── Universal (.agents/skills) ────────────────
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/research-external
Restart Cursor to activate research-external. Access via /research-external in your agent's command palette.
⚠
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Research external sources (documentation, web, APIs) for libraries, best practices, and general topics.
Note: The current year is 2025. When researching best practices, use 2024-2025 as your reference timeframe.
Invocation
/research-external <focus> [options]
Question Flow (No Arguments)
If the user types just /research-external with no or partial arguments, guide them through this question flow. Use AskUserQuestion for each phase.
Phase 1: Research Type
question:"What kind of information do you need?"header:"Type"options:-label:"How to use a library/package"description:"API docs, examples, patterns"-label:"Best practices for a task"description:"Recommended approaches, comparisons"-label:"General topic research"description:"Comprehensive multi-source search"-label:"Compare options/alternatives"description:"Which tool/library/approach is best"
Mapping:
"How to use library" → library focus
"Best practices" → best-practices focus
"General topic" → general focus
"Compare options" → best-practices with comparison framing
Phase 2: Specific Topic
question:"What specifically do you want to research?"header:"Topic"options:[]# Free text input
Then ask for specific library name if not already provided.
Phase 4: Depth
question:"How thorough should the research be?"header:"Depth"options:-label:"Quick answer"description:"Just the essentials"-label:"Thorough research"description:"Multiple sources, examples, edge cases"
Mapping:
"Quick answer" → --depth shallow
"Thorough" → --depth thorough
Phase 5: Output
question:"What should I produce?"header:"Output"options:-label:"Summary in chat"description:"Tell me what you found"-label:"Research document"description:"Write to thoughts/shared/research/"-label:"Handoff for implementation"description:"Prepare context for coding"
Mapping:
"Research document" → --output doc
"Handoff" → --output handoff
Summary Before Execution
Based on your answers, I'll research:
**Focus:** library
**Topic:** "Prisma ORM connection pooling"
**Library:** prisma (npm)
**Depth:** thorough
**Output:** doc
Proceed? [Yes / Adjust settings]
Focus Modes (First Argument)
Focus
Primary Tool
Purpose
library
nia-docs
API docs, usage patterns, code examples
best-practices
perplexity-search
Recommended approaches, patterns, comparisons
general
All MCP tools
Comprehensive multi-source research
Options
Option
Values
Description
--topic
"string"
Required. The topic/library/concept to research
--depth
shallow, thorough
Search depth (default: shallow)
--output
handoff, doc
Output format (default: doc)
--library
"name"
For library focus: specific package name
--registry
npm, py_pi, crates, go_modules
For library focus: package registry
Workflow
Step 1: Parse Arguments
Extract from user input:
FOCUS=$1 # library | best-practices | general
TOPIC="..." # from --topic
DEPTH="shallow" # from --depth (default: shallow)
OUTPUT="doc" # from --output (default: doc)
LIBRARY="..." # from --library (optional)
REGISTRY="npm" # from --registry (default: npm)
# Semantic search in package(cd $CLAUDE_OPC_DIR&& uv run python -m runtime.harness scripts/mcp/nia_docs.py \--package"$LIBRARY"\--registry"$REGISTRY"\--query"$TOPIC"\--limit10)# If thorough depth, also grep for specific patterns(cd $CLAUDE_OPC_DIR&& uv run python -m runtime.harness scripts/mcp/nia_docs.py \--package"$LIBRARY"\--grep"$TOPIC")# Supplement with official docs if URL known(cd $CLAUDE_OPC_DIR&& uv run python -m runtime.harness scripts/mcp/firecrawl_scrape.py \--url"https://docs.example.com/api/$TOPIC"\--format markdown)
# AI-synthesized research (sonar-pro)(cd $CLAUDE_OPC_DIR&& uv run python scripts/mcp/perplexity_search.py \--research"$TOPIC best practices 2024 2025")# If comparing alternatives(cd $CLAUDE_OPC_DIR&& uv run python scripts/mcp/perplexity_search.py \--reason"$TOPIC vs alternatives - which to choose?")
Thorough depth additions:
# Chain-of-thought for complex decisions(cd $CLAUDE_OPC_DIR&& uv run python scripts/mcp/perplexity_search.py \--reason"$TOPIC tradeoffs and considerations 2025")# Deep comprehensive research(cd $CLAUDE_OPC_DIR&& uv run python scripts/mcp/perplexity_search.py \--deep"$TOPIC comprehensive guide 2025")# Recent developments(cd $CLAUDE_OPC_DIR&& uv run python scripts/mcp/perplexity_search.py \--search"$TOPIC latest developments"\--recency month --max-results 5)
Focus: general
Use ALL available MCP tools - comprehensive multi-source research.
Step 2a: Library documentation (nia-docs)
(cd $CLAUDE_OPC_DIR&& uv run python -m runtime.harness scripts/mcp/nia_docs.py \--search"$TOPIC")
Step 2b: Web research (perplexity)
(cd $CLAUDE_OPC_DIR&& uv run python scripts/mcp/perplexity_search.py \--research"$TOPIC")
Step 2c: Specific documentation (firecrawl)
# Scrape relevant documentation pages found in perplexity results(cd $CLAUDE_OPC_DIR&& uv run python -m runtime.harness scripts/mcp/firecrawl_scrape.py \--url"$FOUND_DOC_URL"\--format markdown)
Thorough depth additions:
Run all three tools with expanded queries
Cross-reference findings between sources
Follow links from initial results for deeper context
Step 3: Synthesize Findings
Combine results from all sources:
Key Concepts - Core ideas and terminology
Code Examples - Working examples from documentation
Best Practices - Recommended approaches
Pitfalls - Common mistakes to avoid
Alternatives - Other options considered
Sources - URLs for all citations
Step 4: Write Output
Output: doc (default)
Write to: thoughts/shared/research/YYYY-MM-DD-{topic-slug}.md
›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
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share 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
