# AI Research Agent > Use when 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. - Skill: `oyi77/ai-research-agent` (Agent Skill) - Install (CLI): `npx skillmds add oyi77/ai-research-agent` - Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/ai-research-agent/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: Apache-2.0 - Author: oyi77 (https://skillmd.com/u/oyi77) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/oyi77/ai-research-agent --- 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 | --- ## 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 --- ## 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](../mckinsey-research/SKILL.md) - Deep research - [self-improving](../../core/self-improving/SKILL.md) - Learning system - All 1ai-skills - Research targets ## Process 1. Analyze the task requirements 2. Apply domain expertise 3. Verify output quality