
Research Agent
- 1 installs
- Updated July 5, 2026
- cristian-oancea01/ai-skills
Helps with ai & agent building tasks.
About
research-agent is a Claude Code skill for ai & agent building. It helps developers move faster with AI-assisted coding.
- research-agent
- AI & Agent Building
- AI-coding skill
Research Agent by the numbers
- 1 all-time installs (skills.sh)
- Ranked #14,102 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 7, 2026 (Skillselion catalog sync)
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| Installs | 1 |
|---|---|
| Last updated | July 5, 2026 |
| Repository | cristian-oancea01/ai-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
When to use
Use this skill for:
- market scans
- business idea research
- opportunity discovery
- competitor review
- customer-problem signal gathering
- thesis validation with public sources
Do not use this skill for:
- private-environment discovery
- legal or compliance advice
- deep technical implementation work
- pure coding tasks
Core outcome
Produce decision-ready research that:
- separates fact from inference
- logs sources clearly
- ranks opportunities instead of listing them loosely
- states uncertainty and open questions explicitly
Minimal graph
flowchart TD
U([User Task]) --> B[Research Brief]
B --> M[Market Scan]
B --> C[Customer-Problem Scan]
B --> K[Competitor Scan]
M --> S[Synthesis]
C --> S
K --> S
S --> R[Critique]
R --> D([Deliver])Stages
1. Research Brief
- define the question to answer
- define scope and exclusions
- define target customer and geography if relevant
- define what a useful output looks like
2. Market Scan
- collect public evidence of demand, tool adoption, category formation, or workflow pain
- prefer public sources with visible recency
3. Customer-Problem Scan
- look for direct user pain, repeated complaints, budget sensitivity, and operational friction
- prefer first-person reports over polished marketing claims
4. Competitor Scan
- identify substitutes, direct competitors, and adjacent approaches
- capture how the market is framed, priced, and differentiated
5. Synthesis
- score or rank the best opportunities
- explain why the recommendation wins over alternatives
- keep the recommendation narrow and actionable
6. Critique
- look for weak evidence
- note blocked or missing sources
- state what would disconfirm the conclusion
Evidence rules
- Separate facts, inference, and opinion.
- Date claims when recency matters.
- Log sources for every major conclusion.
- Prefer public, accessible sources.
- If key sources are blocked, say so clearly.
- Do not imply precise market size without reliable evidence.
Output contract
Every deliverable should include:
- objective
- recommendation
- why it wins over alternatives
- target customer
- risks
- open questions
- source log
Templates
templates/research-brief.mdtemplates/opportunity-report.mdtemplates/source-log.mdexamples/example-opportunity-report.md
Rule of thumb
- Evidence first.
- Recommendation second.
- Hype never.
Example Opportunity Report
Executive summary
The best near-term opportunity is a productized service that converts public category and competitor research into a fixed-scope audit. It wins because it has low setup cost, strong pain visibility, and simple delivery.
Market context
Public category pages and founder discussions show rising interest in AI-assisted discovery and lightweight automation.
Customer problem signals
- founders say existing agencies are expensive and vague
- buyers want measurable improvements
- teams want low-input services, not long workshops
Alternatives and competitors
- generic SEO agencies
- outbound lead-gen agencies
- broad automation consultancies
Opportunity hypotheses
1. A narrow audit offer sells faster than a broad retainer. 2. Buyers respond better to evidence-backed remediation than to generic strategy decks.
Scoring matrix
| Option | Demand signal | Setup cost | Automation fit | Notes |
|---|---|---|---|---|
| Productized audit service | High | Low | High | Best first step |
| Full custom agency | Medium | Medium | Medium | Harder to standardize |
| SaaS product first | Unknown | High | High | Too much upfront build |
Risks and disconfirming evidence
- category could be noisy but shallow
- some buyers may not yet understand the problem
- blocked sources may hide negative evidence
Recommendation
Start with the productized audit service. Validate with 2 paid pilots before expanding scope.
Open questions
- which niche converts fastest
- which deliverables buyers value most
Sources
- public category pages
- founder discussions
- public pricing and tooling pages
Opportunity Report
Executive summary
Market context
Customer problem signals
Alternatives and competitors
Opportunity hypotheses
Scoring matrix
Risks and disconfirming evidence
Recommendation
Open questions
Sources
Research Brief
Objective
Research question
Scope
Exclusions
Target customer or segment
Geography and timeframe
Success criteria
Key unknowns
Source Log
| Source | Type | Date | Key claim | Reliability note | Used in section |
|---|