
research-external
External research workflow for docs, web, APIs - NOT codebase exploration
- Installs
- 501
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill research-external- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads research-external automatically when relevant.
- 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)
| 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
```{language}
// 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 succeededpartial- Some tools failed, findings still usefulfailed- 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_KEYorniaserver in mcp_config.jsonPERPLEXITY_API_KEYin environment or~/.claude/.envFIRECRAWL_API_KEYandfirecrawlserver in mcp_config.json
Notes
- NOT for codebase exploration - Use
research-codebaseorscoutfor 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.
More from parcadei/continuous-claude-v3
discovery-interview
parcadei/continuous-claude-v3
Deep interview process to transform vague ideas into detailed specs. Works for technical and non-technical users.
premortem
parcadei/continuous-claude-v3
Identify failure modes before they occur using structured risk analysis
math
parcadei/continuous-claude-v3
Unified math capabilities - computation, solving, and explanation. I route to the right tool.
github-search
parcadei/continuous-claude-v3
Search GitHub code, repositories, issues, and PRs via MCP
dead-code
parcadei/continuous-claude-v3
Find unused functions and dead code in the codebase
refactor
parcadei/continuous-claude-v3
Code refactoring workflow - analyze → plan → implement → review → validate
Popular productivity & automation skills
agent-browser
vercel-labs/agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
lark-doc
open.feishu.cn
飞书云文档(Docx / Wiki)内容操作:读取、创建、编辑文档,插入或下载图片附件,以及操作思维笔记。用户提供文档 URL/token(包括 doubao.com 的 /docx/、/wiki/)时使用;按 URL 路径/token 而非域名路由;/page/ 是妙搭应用,非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive;随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。
lark-base
open.feishu.cn
飞书多维表格(Base)操作:建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式(BaseApp/AppMode 页面与组件)、Workspace 目录、workflow、角色权限、模板中心(多维表格模板分类/列表/搜索);遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps;文件导入/导出转 lark-drive,认证/授权转 lark-shared。
lark-drive
open.feishu.cn
飞书云空间(云盘/云存储):管理 Drive 文件和文件夹,包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签(secure labels)和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题,或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用;doubao.com 云空间 URL/token 也按资源路径和 token 路由,不回退 WebFetch。不负责:文档内容编辑(走 lark-doc)、表格/Base 表内数据操作(走 lark-sheets/lark-base)、知识空间节点/成员管理(走 lark-wiki)、原生 Markdown 文件读写/patch/diff(走 lark-markdown)。
lark-shared
open.feishu.cn
Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.
lark-im
open.feishu.cn
飞书即时通讯:收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片(Interactive Card)、监听卡片按钮回调(card.action.trigger)。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶(添加/移除/查询置顶会话)、管理标签数据、处理卡片回调时使用。