MCPInstall

research-external

External research workflow for docs, web, APIs - NOT codebase exploration

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/parcadei/continuous-claude-v3 --skill research-external
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads research-external automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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)

FocusPrimary ToolPurpose
librarynia-docsAPI docs, usage patterns, code examples
best-practicesperplexity-searchRecommended approaches, patterns, comparisons
generalAll MCP toolsComprehensive multi-source research

Options

OptionValuesDescription
--topic"string"Required. The topic/library/concept to research
--depthshallow, thoroughSearch depth (default: shallow)
--outputhandoff, docOutput format (default: doc)
--library"name"For library focus: specific package name
--registrynpm, py_pi, crates, go_modulesFor 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}
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
```{language}
// Working examples found

Recommendations

  • {Recommendation 1}
  • {Recommendation 2}

Pitfalls to Avoid

  • {Pitfall 1}
  • {Pitfall 2}

Alternatives Considered

OptionProsCons
{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.):

  1. Log the failure in output:

    tool_status:
      nia: success
      perplexity: failed (rate limited)
      firecrawl: skipped
    
  2. Continue with other sources - partial results are valuable

  3. Set status appropriately:

    • complete - All requested tools succeeded
    • partial - Some tools failed, findings still useful
    • failed - No useful results obtained
  4. 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 ResearchUse SkillFor
--output handoffplan-agentCreate implementation plan
Code examples foundimplement_taskDirect implementation
Architecture decisioncreate_planDetailed planning
Library comparisonPresent to userDecision 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

FAQ

What does the research-external skill do?

External research workflow for docs, web, APIs - NOT codebase exploration

How do I install research-external?

Run npx skills add https://github.com/parcadei/continuous-claude-v3 --skill research-external in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/research-external/ (personal) or .claude/skills/research-external/ (project).

Which AI agents can use research-external?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is research-external safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains research-external?

It is published by parcadei in parcadei/continuous-claude-v3. It has 501 recorded installs.

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