research

jwynia/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/jwynia/agent-skills --skill research
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summary

Tool-assisted research with Tavily integration. Transforms basic questions into comprehensive search strategies using AI-optimized web search.

skill.md

Research Skill

Tool-assisted research with Tavily integration. Transforms basic questions into comprehensive search strategies using AI-optimized web search.

Setup

This skill includes a bundled Tavily CLI script at scripts/tavily-cli.ts.

Requirements

  1. Deno - Install from https://deno.land
  2. Tavily API Key - Get one at https://tavily.com (free tier available)

Configuration

Set your API key:

export TAVILY_API_KEY="your-key-here"

Create an alias for convenience (add to your shell profile):

# Adjust path to where this skill is installed
alias tavily='deno run --allow-net --allow-env /path/to/skills/research/scripts/tavily-cli.ts'

Or run directly:

deno run --allow-net --allow-env ./scripts/tavily-cli.ts "your query"

Commands below use tavily assuming the alias is configured.


Quick Reference

Common Commands

# Basic search
tavily "your query"

# With AI answer summary
tavily "your query" --answer

# Deep search with more results
tavily "your query" --depth advanced --results 10 --answer

# News/recent content
tavily "your query" --topic news --time week

# Exclude familiar sources to find new perspectives
tavily "your query" --exclude wikipedia.org,reddit.com

Phase Summary

Phase Type Purpose
0 Manual Analyze topic, set scope
1 Tavily Discover expert terminology
2 Tavily Foundational search
3 Tavily Counter-perspectives
4 Manual Synthesize findings

Scope → Tavily Depth

Decision Stakes Tavily Settings
Low, reversible --depth basic --results 3
Moderate --depth basic --results 5 --answer
High, irreversible --depth advanced --results 10 --answer

Phase 0: Analysis

Goal: Structure topic before searching. Prevents unfocused searches and scope mismatch.

Scope Calibration

Before searching, assess stakes:

Decision Type Confidence Needed Research Depth
Reversible, low-stakes 60-70% Quick scan (minutes)
Reversible, moderate 75-85% Working knowledge
Irreversible, moderate 85-90% Solid grounding
Irreversible, high 90-95% Deep expertise

Analysis Template

# Research Analysis: [Topic]

## Core Concepts
- **Primary terms:** [Key terms requiring definition]
- **Terminology variants:** [Synonyms, jargon, historical terms]
- **Ambiguous terms:** [Terms with multiple meanings]

## Stakeholders
- **Primary actors:** [Who is directly involved?]
- **Affected groups:** [Who bears consequences?]
- **Opposing interests:** [Who benefits from different outcomes?]

## Temporal Scope
- **Historical origins:** [When did this begin?]
- **Key transitions:** [What changed and when?]
- **Current state:** [What's happening now?]

## Domains
- **Primary field:** [Main discipline]
- **Adjacent fields:** [Related disciplines]

## Controversies
- **Active debates:** [What's contested?]
- **Competing frameworks:** [Different ways of understanding]

Phase 0 Checklist

  • Identified primary terms
  • Listed potential stakeholders
  • Assessed decision stakes
  • Determined appropriate research depth

Phase 1: Vocabulary Discovery

Goal: Discover expert terminology to unlock deeper search results.

Why Vocabulary Matters

  • Outsider terms → introductory material
  • Expert terms → research, nuanced analysis
  • Cross-domain terms → bridge bodies of work

Tavily Commands for Vocabulary Discovery

Discovery Need Command
Expert terminology tavily "[topic] terminology experts" --answer
Academic terms tavily "[topic] academic research terminology" --answer
Cross-domain synonyms tavily "[topic] also known as called" --answer
Historical terms tavily "[topic] history original term" --answer

Vocabulary Discovery Process

  1. Run initial terminology search:

    tavily "[topic] terminology" --answer --results 5
    
  2. From results, note:

    • Expert terms (technical vocabulary)
    • Outsider terms (popular/introductory language)
    • Cross-domain equivalents
  3. Update vocabulary map (template below)

  4. Re-run searches with expert terms:

    tavily "[expert-term]" --answer
    
  5. Compare result quality - expert terms should surface deeper content

Vocabulary Map Template

## Core Terms
| Term | Domain | Depth Level |
|------|--------|-------------|
| [expert term] | [field] | Expert |
| [outsider term] | General | Introductory |

## Cross-Domain Synonyms
| Concept | Terms by Domain |
|---------|-----------------|
| [concept] | Field A: [term], Field B: [term] |

## Depth Indicators
| Level | Terms | What They Surface |
|-------|-------|-------------------|
| Introductory | [terms] | Overviews, explainers |
| Expert | [terms] | Research, nuanced analysis |

Phase 1 Checklist

  • Ran terminology discovery search
  • Identified expert vs. outsider terms
  • Mapped cross-domain synonyms
  • Created vocabulary map

Phase 2: Foundational Search

Goal: Build foundational understanding with authoritative sources.

Question Pattern → Tavily Command

Question Pattern Strategy Command
"What is X?" Consensus from authorities tavily "[expert-term] definition" --answer --depth advanced
"Should I X?" Pros/cons, alternatives tavily "[expert-term] pros cons comparison" --answer
"Is X true?" Evidence, counter-evidence tavily "[claim] evidence research" --answer --depth advanced
"How do I X?" Step-by-step, pitfalls tavily "[expert-term] guide tutorial" --answer
Historical context Origins and evolution tavily "[topic] history origins development" --answer

Source Type Selection

Source Type Best For Tavily Approach
Academic/Research Mechanism, causation --depth advanced --results 10
Practitioner content How things work, edge cases --topic general --answer
News/Current Recent developments --topic news --time week
Official docs Technical specs, policy --include [official-domain]

Foundational Search Process

  1. Start with expert terminology from Phase 1

  2. Run foundational queries:

    # Definition/overview
    tavily "[expert-term] comprehensive overview" --answer --depth advanced
    
    # Key perspectives
    tavily "[expert-term] major approaches" --answer --results 7
    
  3. For each major perspective found, get 2-3 authoritative sources:

    tavily "[perspective-name] [expert-term]" --answer --results 5
    
  4. Track sources in research notes

Phase 2 Checklist

  • Used expert terminology from Phase 1
  • Searched for foundational overview
  • Identified 2-3 major perspectives
  • Found authoritative sources per perspective
  • Tracked sources

Phase 3: Counter-Perspective Search

Goal: Explicitly find opposing viewpoints to avoid confirmation bias.

Why Counter-Perspectives Matter

Single-perspective research:

  • All sources support one viewpoint
  • Missing counterarguments
  • Echo chamber risk

Tavily Commands for Counter-Perspectives

Need Command
General criticism tavily "[topic] criticism problems" --answer
Opposing viewpoint tavily "[topic] skeptics critique" --answer
Alternative approaches tavily "[topic] alternatives instead of" --answer
Failure cases tavily "[topic] failures when wrong" --answer
Avoid echo chamber tavily "[topic] debate" --exclude [familiar-sources]

Counter-Perspective Process

  1. Identify your current understanding/lean

  2. Search for strongest counterargument:

    tavily "[topic] strongest argument against" --answer --depth advanced
    
  3. Exclude sources you've already seen:

    tavily "[topic]" --exclude [domains-already-searched]
    
  4. Search for failure modes:

    tavily "[topic] when fails problems limitations" --answer
    
  5. Document opposing perspectives in research notes

Phase 3 Checklist

  • Identified current understanding/position
how to use research

How to use research on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add research
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/jwynia/agent-skills --skill research

The skills CLI fetches research from GitHub repository jwynia/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/research

Reload or restart Cursor to activate research. Access the skill through slash commands (e.g., /research) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.747 reviews
  • James Verma· Dec 24, 2024

    Solid pick for teams standardizing on skills: research is focused, and the summary matches what you get after install.

  • Aditi Zhang· Dec 12, 2024

    research has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Neel Li· Nov 27, 2024

    We added research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yash Thakker· Nov 23, 2024

    We added research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Nikhil Liu· Nov 15, 2024

    research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Michael Ndlovu· Nov 3, 2024

    Keeps context tight: research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aanya Harris· Oct 22, 2024

    research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Nikhil Zhang· Oct 18, 2024

    research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dhruvi Jain· Oct 14, 2024

    research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Xiao Mensah· Oct 6, 2024

    Keeps context tight: research is the kind of skill you can hand to a new teammate without a long onboarding doc.

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