research-workflow — research-workflow install for AI agents research-workflow, community, research-workflow install for AI agents, ide skills, comprehensive research workflow, research investigation techniques, research synthesis methods, Claude Code, Cursor, Windsurf

v1.0
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About this Skill

Ideal for Advanced Research Agents requiring structured methodologies for comprehensive content analysis and synthesis. research-workflow is a systematic approach to conducting thorough research, involving planning, execution, analysis, and synthesis of findings.

Features

Guides users through planning comprehensive research on a topic
Supports multiple search queries to fully answer a question
Emphasizes source credibility and synthesis of research findings
Facilitates the creation of a research report or documented findings
Enables deep investigation and analysis of research topics

# Core Topics

mshafei721 mshafei721
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Updated: 3/8/2026

Agent Capability Analysis

The research-workflow skill by mshafei721 is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for research-workflow install for AI agents, comprehensive research workflow, research investigation techniques.

Ideal Agent Persona

Ideal for Advanced Research Agents requiring structured methodologies for comprehensive content analysis and synthesis.

Core Value

Empowers agents to conduct thorough research using multiple search queries, source credibility assessment, and synthesis techniques, leveraging keywords like research, investigation, and deep analysis to produce high-quality research reports.

Capabilities Granted for research-workflow

Automating research workflows for complex topics
Generating comprehensive research reports with credible sources
Synthesizing findings from multiple search queries and sources

! Prerequisites & Limits

  • Requires access to reliable information sources
  • May need additional tools for data visualization and analysis
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research-workflow

Install research-workflow, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
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Research Workflow

A structured methodology for conducting comprehensive research. This skill guides you through planning, executing, analyzing, and synthesizing research on any topic.

When to Use This Skill

Use this skill when:

  • The user needs comprehensive research on a topic
  • Multiple search queries are needed to fully answer a question
  • Source credibility and synthesis matter
  • A research report or documented findings are expected
  • Keywords mentioned: research, investigate, deep dive, comprehensive analysis

Do NOT use this skill when:

  • A single quick search will suffice (use web-search instead)
  • The user just wants a simple fact lookup
  • No synthesis or analysis is needed
  • Time is extremely limited

Prerequisites

Before using this skill, ensure:

  • Web search capability is available (web-search skill, WebSearch tool, or similar)
  • Sufficient time for multi-phase research process
  • Clear understanding of the research question or topic

Research Phases Overview

┌─────────────────────────────────────────────────────────────┐
│                    RESEARCH WORKFLOW                        │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  1. PLANNING          2. EXECUTION                          │
│  ┌──────────────┐    ┌──────────────┐                       │
│  │ Define       │    │ Run searches │                       │
│  │ questions    │───>│ Evaluate     │                       │
│  │ Plan queries │    │ sources      │                       │
│  └──────────────┘    └──────────────┘                       │
│         │                   │                               │
│         v                   v                               │
│  3. ANALYSIS          4. SYNTHESIS                          │
│  ┌──────────────┐    ┌──────────────┐                       │
│  │ Organize     │    │ Create       │                       │
│  │ findings     │───>│ coherent     │                       │
│  │ Find patterns│    │ output       │                       │
│  └──────────────┘    └──────────────┘                       │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Phase 1: Planning

Before any searches, establish a clear research plan.

Step 1: Define the Research Question

Convert the topic into specific, answerable questions.

Example:

  • Topic: "AI in healthcare"
  • Questions:
    1. What are the current applications of AI in healthcare?
    2. What are the main benefits and challenges?
    3. What regulations govern AI in healthcare?
    4. What are the latest developments (last 6 months)?

Step 2: Identify Sub-Topics

Break down the main topic into searchable components:

  • Core concepts and definitions
  • Current state and applications
  • Benefits and advantages
  • Challenges and limitations
  • Recent developments
  • Future trends

Step 3: Plan Search Strategy

Create a search plan with query progression:

  1. Broad queries first: Get overall landscape

    • "[topic] overview"
    • "[topic] introduction guide"
  2. Specific queries next: Dive into details

    • "[topic] specific aspect"
    • "[topic] case study"
  3. Verification queries last: Confirm findings

    • "[topic] criticism challenges"
    • "[topic] latest news [year]"

Use the template at assets/research-plan-template.md to document your plan.

Phase 2: Execution

Execute your search plan systematically.

Step 1: Run Searches

Execute queries in order, using appropriate search parameters:

bash
1# Broad overview 2web-search "AI in healthcare overview 2024" 3 4# Specific deep dive 5web-search "AI diagnostic imaging applications" --depth advanced 6 7# Current news 8web-search "AI healthcare regulations 2024" --topic news --time month

For each search, record:

  • Query used
  • Number of results reviewed
  • Key findings (2-3 bullet points)
  • Notable sources
  • New questions raised

Step 3: Evaluate Sources

Use the checklist at assets/source-evaluation-checklist.md to assess:

Credibility Indicators:

  • Author/organization expertise
  • Publication reputation
  • Date of publication
  • Citations and references

Quality Signals:

  • Evidence-based claims
  • Multiple perspectives
  • Clear methodology

Step 4: Iterate as Needed

Research is not linear. Based on findings:

  • Add new queries for gaps discovered
  • Verify surprising claims
  • Explore unexpected connections

Phase 3: Analysis

Organize and analyze your collected findings.

Step 1: Group Findings by Theme

Organize results into categories:

  • Core concepts
  • Current state
  • Benefits/opportunities
  • Challenges/risks
  • Recent developments
  • Expert opinions

Step 2: Identify Patterns

Look for:

  • Consensus: Where do multiple sources agree?
  • Conflicts: Where do sources disagree?
  • Gaps: What questions remain unanswered?
  • Trends: What direction is the field moving?

Step 3: Assess Confidence

For each finding, determine confidence level:

  • High: Multiple authoritative sources agree
  • Medium: Some evidence, limited sources
  • Low: Single source or conflicting information

Step 4: Note Limitations

Document:

  • What couldn't be found
  • Areas needing more research
  • Potential biases in sources

Phase 4: Synthesis

Create coherent, useful output from your analysis.

Step 1: Structure the Output

Choose appropriate format based on use case:

  • Executive summary: Quick overview for decisions
  • Full report: Comprehensive documentation
  • Action items: Practical next steps

Use the template at assets/research-report-template.md.

Step 2: Write the Synthesis

Key principles:

  • Lead with most important findings
  • Connect related concepts
  • Note confidence levels
  • Acknowledge limitations
  • Cite sources

Step 3: Include Actionable Elements

End with practical outputs:

  • Key takeaways (3-5 points)
  • Recommendations
  • Further research suggestions
  • Decision points

Complete Example

Scenario: Research "Best practices for API versioning"

Phase 1 - Planning:

Research Question: What are the best practices for API versioning?

Sub-questions:
1. What versioning strategies exist?
2. What are pros/cons of each?
3. What do major companies use?
4. What do experts recommend?

Search Plan:
- "API versioning strategies comparison"
- "REST API versioning best practices 2024"
- "API versioning header vs URL vs query parameter"
- "large companies API versioning approach"

Phase 2 - Execution:

Query 1: "API versioning strategies comparison"
- Found: URL versioning, header versioning, query parameter
- Key insight: URL versioning most common, header more "RESTful"
- Sources: REST API tutorial, Martin Fowler blog

Query 2: "REST API versioning best practices 2024"
- Found: Semantic versioning principles apply
- Key insight: Version only when breaking changes
- Sources: API design guides, Stack Overflow discussions

Phase 3 - Analysis:

Consensus Points:
- Version only for breaking changes
- Be consistent within an API
- Document version lifecycle

Conflicts:
- URL vs header placement (no clear winner)
- When to deprecate old versions

Gaps:
- Limited data on performance impact
- Few studies on developer experience

Phase 4 - Synthesis:

Key Findings:
1. Three main strategies exist (URL, header, query param)
2. URL versioning is most common and discoverable
3. Header versioning is considered more "pure" REST
4. Version only on breaking changes
5. Major companies split between approaches

Recommendations:
- Use URL versioning for public APIs (discoverability)
- Consider header versioning for internal APIs
- Document deprecation timeline clearly
- Use semantic versioning principles

Quality Checklist

Before completing research, verify:

  • Clear research questions were defined
  • Multiple queries were executed (minimum 3-5)
  • Sources were evaluated for credibility
  • Findings are organized by theme
  • Consensus and conflicts are noted
  • Confidence levels are indicated
  • Limitations are acknowledged
  • Output is actionable

Reference Materials

For detailed guidance, see:

Templates

Limitations

This workflow has the following limitations:

  • Quality depends on available web search capability
  • Cannot access paywalled or restricted content
  • Time-intensive for comprehensive research
  • Synthesis quality depends on agent capabilities
  • May miss very recent developments not yet indexed
  • web-search: For executing individual web searches (used within this workflow)

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is research-workflow?

Ideal for Advanced Research Agents requiring structured methodologies for comprehensive content analysis and synthesis. research-workflow is a systematic approach to conducting thorough research, involving planning, execution, analysis, and synthesis of findings.

How do I install research-workflow?

Run the command: npx killer-skills add mshafei721/Video/research-workflow. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for research-workflow?

Key use cases include: Automating research workflows for complex topics, Generating comprehensive research reports with credible sources, Synthesizing findings from multiple search queries and sources.

Which IDEs are compatible with research-workflow?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for research-workflow?

Requires access to reliable information sources. May need additional tools for data visualization and analysis.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add mshafei721/Video/research-workflow. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use research-workflow immediately in the current project.

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