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:

• 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

date: {ISO timestamp} type: external-research topic: "{topic}" focus: {focus} sources: [nia, perplexity, firecrawl] status: complete

Research: {Topic}

Summary

{2-3 sentence summary of findings}

Key Findings

Library Documentation

{From nia-docs - API references, usage patterns}

Best Practices (2024-2025)

{From perplexity - recommended approaches}

Code Examples

// Working examples found

Recommendations

- {Recommendation 1}

- {Recommendation 2}

Pitfalls to Avoid

- {Pitfall 1}

- {Pitfall 2}

Alternatives Considered

Option
Pros
Cons

{Option 1}
...
...

Sources

- {Source 1}

- {Source 2}

#### Output: `handoff`

Write to: `thoughts/shared/handoffs/{session}/research-{topic-slug}.yaml`

```yaml
---
type: research-handoff
ts: {ISO timestamp}
topic: "{topic}"
focus: {focus}
status: complete
---

goal: Research {topic} for implementation planning
sources_used: [nia, perplexity, firecrawl]

findings:
  key_concepts:
    - {concept1}
    - {concept2}

  code_examples:
    - pattern: "{pattern name}"
      code: |
        // example code

  best_practices:
    - {practice1}
    - {practice2}

  pitfalls:
    - {pitfall1}

recommendations:
  - {rec1}
  - {rec2}

sources:
  - title: "{Source 1}"
    url: "{url1}"
    type: {documentation|article|reference}

for_plan_agent: |
  Based on research, the recommended approach is:
  1. {Step 1}
  2. {Step 2}
  Key libraries: {lib1}, {lib2}
  Avoid: {pitfall1}

Step 5: Return Summary

Research Complete

Topic: {topic}
Focus: {focus}
Output: {path to file}

Key findings:
- {Finding 1}
- {Finding 2}
- {Finding 3}

Sources: {N} sources cited

{If handoff output:}
Ready for plan-agent to continue.

Error Handling

If an MCP tool fails (API key missing, rate limited, etc.):

- 
Log the failure in output:

tool_status:
  nia: success
  perplexity: failed (rate limited)
  firecrawl: skipped

- 
Continue with other sources - partial results are valuable

- 
Set status appropriately:

- complete
 - All requested tools succeeded

- partial
 - Some tools failed, findings still useful

- failed
 - No useful results obtained

- 
Note gaps in findings:

## Gaps
- Perplexity unavailable - best practices section limited to nia results

Examples

Library Research (Shallow)

/research-external library --topic "dependency injection" --library fastapi --registry py_pi

Best Practices (Thorough)

/research-external best-practices --topic "error handling in Python async" --depth thorough

General Research for Handoff

/research-external general --topic "OAuth2 PKCE flow implementation" --depth thorough --output handoff

Quick Library Lookup

/research-external library --topic "useEffect cleanup" --library react

Integration with Other Skills

After Research
Use Skill
For

--output handoff

plan-agent

Create implementation plan

Code examples found
implement_task

Direct implementation

Architecture decision
create_plan

Detailed planning

Library comparison
Present to user
Decision making

Required Environment

- NIA_API_KEY
 or nia
 server in mcp_config.json

- PERPLEXITY_API_KEY
 in environment or ~/.claude/.env

- FIRECRAWL_API_KEY
 and firecrawl
 server in mcp_config.json

Notes

- NOT for codebase exploration - Use research-codebase
 or scout
 for that

- Always cite sources - Include URLs for all findings

- 2024-2025 timeframe - Focus on current best practices

- Graceful degradation - Partial results better than no results