
Research Agent
This is a background agent that hits MCP tools (Nia for library docs, Perplexity for best practices, Firecrawl for web scraping) and writes up what it finds in a structured handoff markdown file. You spawn it when you need external documentation or current best practices before planning a feature. It's not user-invocable, so it runs as part of a larger agent workflow where one agent does research and hands off to a planning or implementation agent. The handoff format is well thought out with sections for findings, code examples, and pitfalls. Honest take: this is solid infrastructure for keeping AI context grounded in actual docs rather than hallucinated APIs, though you'll need those MCP connections configured.
- skills.sh installs
- 530
- GitHub stars
- 3.9K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add parcadei/continuous-claude-v3 --skill research-agent --agent claude-codeInstalls into .claude/skills of the current project.
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:
- Research question - What you need to find out
- Context - Why this research is needed (e.g., planning a feature)
- 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"
Sponsors
Details
- Repository
- github.com/parcadei/continuous-claude-v3
- Categories
- Documentation, AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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