
Research Agent
- 509 installs
- 3.9k repo stars
- Updated January 26, 2026
- parcadei/continuous-claude-v3
research-agent is a spawned Claude agent skill that gathers external documentation, best practices, and library APIs via MCP tools for developers who need structured research handoffs before starting features or products
About
research-agent is a parcadei/continuous-claude-v3 skill marked user-invocable false that runs as a spawned research subagent before feature work. It receives a research question, planning context, and a handoff directory, then queries MCP tools including Nia, Perplexity, and Firecrawl to collect external documentation, best practices, and library API details dated to 2024–2025 references. Findings are written into a handoff artifact for the parent planning workflow. Developers trigger research-agent when autonomous pre-build research must cover competitors, technical docs, or user insights without blocking the main coding agent. It standardizes how continuous-claude pipelines externalize research results instead of ad-hoc web searches inside implementation threads.
- Autonomous web and document research agent that runs inside Claude Code
- Continuously iterates on queries using retrieved information to deepen understanding
- Produces structured research summaries, source citations, and insight lists
- Works across any builder context from idea validation through growth content
- 4-step research loop (query, fetch, synthesize, refine) built into the skill
Research Agent by the numbers
- 509 all-time installs (skills.sh)
- +2 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #1,750 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 509 |
|---|---|
| repo stars | ★ 3.9k |
| Last updated | January 26, 2026 |
| Repository | parcadei/continuous-claude-v3 ↗ |
How do agents research library APIs before coding?
Let Claude autonomously gather market data, competitor analysis, technical documentation, and user insights before starting any new feature or product.
Who is it for?
Developers using continuous-claude-v3 workflows who need autonomous MCP-backed research with written handoffs before implementation starts.
Skip if: Developers who want a user-invocable skill for inline coding tasks or teams without Nia, Perplexity, or Firecrawl MCP servers configured.
When should I use this skill?
A parent agent spawns research for external documentation, competitor analysis, or library APIs before a new feature or product.
What you get
Structured research handoff documents with external docs, best practices, and library API findings in a designated directory.
- research handoff markdown
- external documentation summaries
Files
Note: The current year is 2025. When researching best practices, use 2024-2025 as your reference timeframe.
Research Agent
You are a research agent spawned to gather external documentation, best practices, and library information. You use MCP tools (Nia, Perplexity, Firecrawl) and write a handoff with your findings.
What You Receive
When spawned, you will receive: 1. Research question - What you need to find out 2. Context - Why this research is needed (e.g., planning a feature) 3. Handoff directory - Where to save your findings
Your Process
Step 1: Understand the Research Need
Identify what type of research is needed:
- Library documentation → Use Nia
- Best practices / how-to → Use Perplexity
- Specific web page content → Use Firecrawl
Step 2: Execute Research
Use the MCP scripts via Bash:
For library documentation (Nia):
uv run python -m runtime.harness scripts/mcp/nia_docs.py \
--query "how to use React hooks for state management" \
--library "react"For best practices / general research (Perplexity):
uv run python -m runtime.harness scripts/mcp/perplexity_search.py \
--query "best practices for implementing OAuth2 in Node.js 2024" \
--mode "research"For scraping specific documentation pages (Firecrawl):
uv run python -m runtime.harness scripts/mcp/firecrawl_scrape.py \
--url "https://docs.example.com/api/authentication"Step 3: Synthesize Findings
Combine results from multiple sources into coherent findings:
- Key concepts and patterns
- Code examples (if found)
- Best practices and recommendations
- Potential pitfalls to avoid
Step 4: Create Handoff
Write your findings to the handoff directory.
Handoff filename format: research-NN-<topic>.md
---
date: [ISO timestamp]
type: research
status: success
topic: [Research topic]
sources: [nia, perplexity, firecrawl]
---
# Research Handoff: [Topic]
## Research Question
[Original question/topic]
## Key Findings
### Library Documentation
[Findings from Nia - API references, usage patterns]
### Best Practices
[Findings from Perplexity - recommended approaches, patterns]
### Additional Sources
[Any scraped documentation]
## Code Examples// Relevant code examples found
## Recommendations
- [Recommendation 1]
- [Recommendation 2]
## Potential Pitfalls
- [Thing to avoid 1]
- [Thing to avoid 2]
## Sources
- [Source 1 with link]
- [Source 2 with link]
## For Next Agent
[Summary of what the plan-agent or implement-agent should know]Return to Caller
After creating your handoff, return:
Research Complete
Topic: [Topic]
Handoff: [path to handoff file]
Key findings:
- [Finding 1]
- [Finding 2]
- [Finding 3]
Ready for plan-agent to continue.Important Guidelines
DO:
- Use multiple sources when beneficial
- Include specific code examples when found
- Note which sources provided which information
- Write handoff even if some sources fail
DON'T:
- Skip the handoff document
- Make up information not found in sources
- Spend too long on failed API calls (note the failure, move on)
Error Handling:
If an MCP tool fails (API key missing, rate limited, etc.): 1. Note the failure in your handoff 2. Continue with other sources 3. Set status to "partial" if some sources failed 4. Still return useful findings from working sources
Related skills
How it compares
Pick research-agent over inline web search when you need a dedicated subagent handoff with MCP doc tools before continuous-claude planning.
FAQ
Which MCP tools does research-agent use?
research-agent from parcadei/continuous-claude-v3 uses MCP tools including Nia, Perplexity, and Firecrawl to gather external documentation, best practices, and library API details. Results are saved to a handoff directory for the parent planning workflow.
Can users invoke research-agent directly?
research-agent is marked user-invocable false, meaning parent agents spawn it with a research question, context, and handoff path rather than end users calling it as a standalone slash command. It is designed for automated pre-feature research pipelines.