ai-research-agent

name	ai-research-agent
description	Autonomous research agent that monitors trends, discovers income opportunities, and creates new skills — runs daily research cycles to keep the one-man-company evolving. Use when working with ai research agent.
domain	research
author	oyi77
license	Apache-2.0
subdomain	research
tags	["agent","ai-agent","analysis","investigation","research"]
version	1.0.0

persona: name: "Domain Expert" title: "Master of Ai Research Agent" expertise: ['Specialized Knowledge', 'Best Practices', 'Industry Standards'] philosophy: "Excellence through expertise." credentials: ['Industry leader', 'Practiced expert', 'Thought leader'] principles: ['Quality first', 'Continuous improvement', 'Evidence-based decisions', 'Customer focus']

AI Research Agent Skill

Anti-Rationalization Table

Rationalization	Reality
"I'll figure it out as I go"	A structured approach saves time and reduces errors. Follow the workflow in this skill rather than improvising.
"I already know this topic"	Familiarity breeds shortcuts. Use the checklist to verify you haven't missed critical steps.
"This doesn't apply to my situation"	The patterns here generalize across contexts. Adapt, don't skip — the underlying principles hold.
"One more tool will fix it"	Adding complexity rarely solves process gaps. Master the core workflow first.

When to Use

Trigger phrases:

• "ai research agent"
• "Help me with ai research agent"

Use cases:

• When the task matches this skill's domain expertise

When NOT to use:

• For tasks outside this skill's scope

Overview

An autonomous research agent that continuously monitors trends, discovers new opportunities, and enhances the skill ecosystem. This is the "brain" that keeps the one-man-company evolving and adapting to new market conditions.

Purpose: Continuous improvement and opportunity discovery
Output: New skills, workflow improvements, market insights
Frequency: Daily research cycles

---

Core Functions

Primary capabilities and entry points.

1. Trend Monitoring

Daily Tasks:
- Monitor AI/news/trends
- Track competitor activities  
- Scan new tools/platforms
- Watch market shifts

2. Opportunity Discovery

Weekly Tasks:
- Identify new income streams
- Find emerging markets
- Discover automation opportunities
- Analyze gaps in current skills

3. Skill Enhancement

Ongoing:
- Update existing skills
- Add new tools to workflows
- Optimize processes
- Document learnings

4. Knowledge Accumulation

Continuous:
- Save research findings
- Build knowledge base
- Create new skills
- Share insights

---

Research Sources

T1 (official docs, specs), T2 (technical blogs, SO), T3 (forums, social).

News & Trends

Source	Frequency	Use
Hacker News	Daily	Tech trends
Twitter/X	Daily	Real-time news
Substack	Daily	Deep dives
Reddit	Daily	Community sentiment
LinkedIn	Daily	Business trends

AI-Specific

Source	Frequency	Use
Anthropic Blog	Weekly	Model updates
OpenAI Blog	Weekly	New capabilities
AI News	Daily	Industry news
Arxiv	Weekly	Research papers

Monetization

Source	Frequency	Use
Indie Hackers	Weekly	Revenue stories
Product Hunt	Daily	New products
GrowthLab	Weekly	Tactics
Sidebar	Weekly	Curated tools

---

Research Workflow

Follow a systematic methodology: scope, gather, validate, synthesize.

Morning Research (30 min)

1. Scan 5 key newsletters
2. Check Twitter for AI news
3. Review competitor channels
4. Note interesting findings

Deep Research (2 hours/week)

1. Pick 1 emerging trend
2. Research comprehensively
3. Test with small experiment
4. Document findings
5. Propose skill addition

Monthly Review

1. Analyze what worked
2. Identify new opportunities
3. Update skill priorities
4. Plan experiments
5. Share learnings

---

Opportunity Analysis

Evaluate market gaps, timing, and competitive positioning.

Evaluate New Income Streams

Market Size

- TAM (Total Addressable Market)
- Growth rate
- Competition level

Feasibility

- Time to implement
- Required skills
- Initial investment
- Complexity

Monetization

- Revenue potential
- Time to revenue
- Recurring or one-time
- Scalability

Score Formula

Score = (Market × 0.3) + (Feasibility × 0.4) + (Revenue × 0.3)

Score > 7: Prioritize
Score 5-7: Add to backlog
Score < 5: Skip

---

Skill Development Process

Iterative improvement through practice, feedback, and measurement.

Stage 1: Research

1. Find skill in market
2. Analyze similar skills
3. Identify unique angle
4. Document requirements

Stage 2: Prototype

1. Create basic skill doc
2. Define core capabilities
3. Add tools and integrations
4. Test with sample use case

Stage 3: Implementation

1. Create folder structure
2. Write SKILL.md
3. Add to SKILL_INDEX.json
4. Test and refine

Stage 4: Documentation

1. Document use cases
2. Add examples
3. Create tutorials
4. Share with community

---

AI Research Prompts

Key aspects of ai-research-agent relevant to this section.

Daily Scan

Search for:
- New AI tools released today
- Trending AI use cases
- Money-making AI strategies
- Automation opportunities

Format findings as bullet points with links.

Deep Dive

Research [TOPIC] thoroughly:

1. What is it?
2. How does it work?
3. Who is it for?
4. How to make money with it?
5. What tools needed?
6. Time to implement?
7. Risk level?

Provide specific examples and resources.

Competitor Analysis

Find 5 competitors in [NICHE]:

For each:
- What they offer
- Pricing
- What's working
- What's missing
- How to differentiate

---

Knowledge Management

Key aspects of ai-research-agent relevant to this section.

Structure

research/
├── trends/
│   ├── daily/      # Daily findings
│   ├── weekly/    # Weekly analysis
│   └── monthly/   # Monthly reviews
├── opportunities/
│   ├── validated/ # Tested & working
│   └── pending/   # To test
├── skills/
│   ├── existing/  # Current skills
│   └── proposals/ # New skill ideas
└── learnings/
    ├── what-worked/
    └── what-failed/

Tools

Tool	Use	Price
Notion	Knowledge base	$10/mo
Obsidian	Local notes	Free
Readwise	Article capture	$10/mo
Perplexity	Research	$20/mo

---

Integration with 1ai-skills

How to connect this tool with the 1ai-skills ecosystem.

The Self-Improving System

AI Research Agent
      ↓
Discover Opportunity
      ↓
Create/Enhance Skill
      ↓
Deploy & Test
      ↓
Measure Results
      ↓
Share Learnings
      ↓
Loop

Skill Synergies

Skill	Use Case
All Skills	Research target
mckinsey-research	Deep analysis
self-improving	Implementation

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Automation Ideas

Key aspects of ai-research-agent relevant to this section.

Automated Research Pipeline

1. RSS feeds → Zapier → Notion
2. Twitter lists → API → Database
3. Newsletter → AI summary → Slack
4. Competitor → Site monitoring → Alerts

Automated Documentation

1. Research → AI summary
2. AI summary → Skill format
3. Skill format → SKILL.md
4. SKILL.md → GitHub

---

Best Practices

Key aspects of ai-research-agent relevant to this section.

Do's

✅ Research daily (even 15 min)
✅ Document everything
✅ Test quickly, fail fast
✅ Share learnings
✅ Stay curious
✅ Focus on action

Don'ts

❌ Don't over-research
❌ Don't skip execution
❌ Don't ignore failures
❌ Don't work in isolation
❌ Don't skip reviews

---

Metrics

Metric	Target
Research time/day	30 min
Trends captured/week	10+
Experiments/month	3+
Skills added/quarter	2+
Revenue tested/month	1+

---

Version History

• v1.0 (2026-02-27) - Initial creation
  • Research workflow
  • Opportunity analysis
  • Skill development process

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When NOT to Use

• When the research requires access to proprietary databases or paywalled sources
• When findings will be used for financial decisions requiring licensed advisor review
• When the task is too trivial to warrant this skill
• When a more appropriate skill exists

Common Rationalizations

Rationalization	Reality
"I'll do this later"	Explain why this excuse is wrong for this skill
"This is simple, skip steps"	Even simple tasks benefit from process

Red Flags

• Research relies on a single unverified source
• Agent presents speculation as confirmed findings
• Watch for shortcuts and skipped steps

Verification

After completing this skill, confirm:

• Findings are verified across multiple independent sources
• Research methodology is documented and reproducible
• All required outputs generated
• Success criteria met

Related Skills

• mckinsey-research - Deep research
• self-improving - Learning system
• All 1ai-skills - Research targets

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality