Research Cog
by @nitishgargiitd
AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with ci...
clawhub install research-cogπ About This Skill
name: research-cog description: "AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026)." author: CellCog homepage: https://cellcog.ai metadata: openclaw: emoji: "π¬" os: [darwin, linux, windows] requires: bins: [python3] env: [CELLCOG_API_KEY] dependencies: [cellcog]
Research Cog - Deep Research Powered by CellCog
#1 on DeepResearch Bench (Apr 2026). Your AI research analyst for comprehensive, citation-backed research on any topic.
Leaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard
How to Use
For your first CellCog task in a session, read the cellcog skill for the full SDK reference β file handling, chat modes, timeouts, and more.
OpenClaw (fire-and-forget):
result = client.create_chat(
prompt="[your task prompt]",
notify_session_key="agent:main:main",
task_label="my-task",
chat_mode="agent",
)
All agents except OpenClaw (blocks until done):
from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
prompt="[your task prompt]",
task_label="my-task",
chat_mode="agent",
)
print(result["message"])
What You Can Research
Competitive Analysis
Analyze companies against their competitors with structured insights:
Market Research
Understand markets, industries, and trends:
Stock & Investment Analysis
Financial research with data and analysis:
Academic & Technical Research
Deep dives with proper citations:
Due Diligence
Comprehensive research for decision-making:
Research Output Formats
CellCog can deliver research in multiple formats:
| Format | Best For | |--------|----------| | Interactive HTML Report | Explorable dashboards with charts, expandable sections | | PDF Report | Shareable, printable professional documents | | Markdown | Integration into your docs/wikis | | Plain Response | Quick answers in chat |
Specify your preferred format in the prompt:
Chat Mode for Research
| Scenario | Recommended Mode |
|----------|------------------|
| Trivial lookups, basic facts | "agent" |
| Deep research, competitive analysis, market research, investment analysis | "agent team" |
| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | "agent team max" |
Use "agent team" for most research (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.
Use "agent" only for trivial lookups like "What's Apple's stock ticker?"
Use "agent team max" for cutting-edge academic research and high-stakes due diligence β when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires β₯2,000 credits.
Research Quality Features
Citations (On Request)
Citations are NOT automatic. CellCog focuses on delivering accurate, well-researched content by default.
If you need citations:
Without explicit citation requests, CellCog prioritizes delivering accurate information efficiently.
Data Accuracy
CellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.Structured Analysis
Complex research is organized with clear sections, executive summaries, and actionable insights.Visual Elements
Research reports can include:Example Research Prompts
Quick competitive intel: > "Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed."
Deep market research: > "Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report."
Investment analysis: > "Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts."
Academic deep dive: > "Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape."
Tips for Better Research
1. Be specific: "AI market" is vague. "Enterprise AI automation market in healthcare" is better.
2. Specify timeframe: "Recent" is ambiguous. "2025-2026" or "last 6 months" is clearer.
3. Define scope: "Compare everything about X and Y" leads to bloat. "Compare X and Y on pricing, features, and market positioning" is focused.
4. Request structure: "Include executive summary, key findings, and recommendations" helps organize output.
5. Mention output format: "Deliver as PDF" or "Create interactive HTML dashboard" gets you the right format.
If CellCog is not installed
Run /cellcog-setup (or /cellcog:cellcog-setup depending on your tool) to install and authenticate.
OpenClaw users: Run clawhub install cellcog instead.
Manual setup: pip install -U cellcog and set CELLCOG_API_KEY. See the cellcog skill for SDK reference.