External Research Workflow

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

Examples of good answers:

• "How to use Prisma ORM with TypeScript"
• "Best practices for error handling in Python"
• "React vs Vue vs Svelte for dashboards"

Phase 3: Library Details (if library focus)

If user selected library focus:

question: "Which package registry?"
header: "Registry"
options:
  - label: "npm (JavaScript/TypeScript)"
    description: "Node.js packages"
  - label: "PyPI (Python)"
    description: "Python packages"
  - label: "crates.io (Rust)"
    description: "Rust crates"
  - label: "Go modules"
    description: "Go packages"

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)

Step 2: Execute Research by Focus

Focus: library

Primary tool: nia-docs - Find API documentation, usage patterns, code examples.

# 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" \
  --limit 10)

# 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)

Thorough depth additions:

• Multiple semantic queries with variations
• Grep for specific function/class names
• Scrape official documentation pages

Focus: best-practices

Primary tool: perplexity-search - Find recommended approaches, patterns, anti-patterns.

# 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:

1. Key Concepts - Core ideas and terminology
2. Code Examples - Working examples from documentation
3. Best Practices - Recommended approaches
4. Pitfalls - Common mistakes to avoid
5. Alternatives - Other options considered
6. Sources - URLs for all citations

Step 4: Write Output

Output: doc (default)

Write to: thoughts/shared/research/YYYY-MM-DD-{topic-slug}.md

---
date: {ISO timestamp}
type: external-research
topic: "{topic}"
focus: {focus}
how to use research-external
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use research-external on Cursor
AI-first code editor with Composer
1
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 research-external
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill research-externalcopy
The skills CLI fetches research-external from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/research-external
Reload or restart Cursor to activate research-external. Access the skill through slash commands (e.g., /research-external) 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.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?