# Research Assistant

> Systematic research workflows for SOTA AI agents. Includes tool-triggered searches, multi-source synthesis, source credibility evaluation, bias detection, structured reporting, and citation practices. Use for deep research, competitive analysis, literature reviews, due diligence, or any investigation task.

- Skill: `florencevision/research-assistant` (Agent Skill)
- Install (CLI): `npx skillmds@latest add florencevision/research-assistant`
- Raw SKILL.md: https://api.skillmd.com/api/skills/florencevision/research-assistant/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: florencevision (https://skillmd.com/u/florencevision)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/florencevision/research-assistant

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# Research Assistant

When active, guide the agent through rigorous, transparent research processes.

## Core Principles
- Always use tools explicitly for current information (web search, X search, browse specific pages).
- Cross-verify information across multiple independent sources.
- Distinguish between facts, opinions, and speculation.
- Evaluate source credibility, recency, and potential bias.
- Structure outputs clearly with sources and confidence levels.

## Recommended Workflow
1. **Clarify Scope**: Define exact research questions and success criteria with the user.
2. **Tool Activation**: Explicitly trigger searches: "Search the web for...", "Search X for recent discussions on...", "Browse [specific authoritative page]".
3. **Gather & Synthesize**: Collect key points from multiple sources. Note agreements, contradictions, and gaps.
4. **Evaluate**: Assess reliability of each source. Flag potential biases or outdated info.
5. **Structure Output**:
   - Executive Summary
   - Key Findings (with sources)
   - Conflicting Views / Uncertainties
   - Recommendations or Next Steps
   - Full Source List with dates/access info
6. **Iterate**: Offer to dive deeper into specific areas or update with new searches.

## Output Quality Rules
- Every claim should be traceable to a source.
- Use tables for comparisons when helpful.
- Include confidence levels or "as of [date]" notes for time-sensitive info.
- Be transparent about limitations of available data.

## Pitfalls to Avoid
- Relying on single source or model training data alone.
- Presenting speculation as fact.
- Over-generalizing from limited results.

This skill produces reliable, well-sourced research outputs.