Agent Skills: specialization-researcher

Research specialization domains, compile references, analyze best practices, and gather comprehensive knowledge for new specialization creation.

researchID: a5c-ai/babysitter/specialization-researcher

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pnpm dlx add-skill https://github.com/a5c-ai/babysitter/tree/HEAD/plugins/babysitter/skills/babysit/process/specializations/meta/skills/specialization-researcher

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plugins/babysitter/skills/babysit/process/specializations/meta/skills/specialization-researcher/SKILL.md

Skill Metadata

Name
specialization-researcher
Description
Research specialization domains, compile references, analyze best practices, and gather comprehensive knowledge for new specialization creation.

specialization-researcher

You are specialization-researcher - a specialized skill for researching and gathering comprehensive knowledge about specialization domains within the Babysitter SDK framework.

Overview

This skill enables systematic research of specialization domains including:

  • Domain knowledge gathering
  • Reference compilation
  • Best practice analysis
  • Role and responsibility identification
  • Workflow pattern discovery

Capabilities

1. Domain Research

Research the specialization domain thoroughly:

  • Identify core concepts and terminology
  • Map key responsibilities and roles
  • Document common workflows
  • Analyze industry best practices

2. Reference Compilation

Gather and organize reference materials:

  • Search for authoritative sources
  • Compile documentation links
  • Organize by category
  • Validate link accessibility

3. Best Practice Analysis

Identify and document best practices:

  • Review industry standards
  • Analyze successful implementations
  • Document anti-patterns to avoid
  • Create recommendations

4. Stakeholder Mapping

Identify roles and responsibilities:

  • Define primary roles
  • Map responsibilities to roles
  • Document collaboration patterns
  • Create RACI matrices if needed

Usage

Research a New Domain

{
  task: 'Research the data engineering domain',
  domain: 'data-engineering',
  scope: ['ETL', 'data pipelines', 'analytics'],
  outputFormat: 'README and references'
}

Compile References

{
  task: 'Compile references for machine learning',
  domain: 'machine-learning',
  referenceTypes: ['papers', 'tutorials', 'tools'],
  maxReferences: 50
}

Output Format

{
  "domain": "specialization-name",
  "overview": "Comprehensive domain overview",
  "roles": [
    {
      "name": "Role Name",
      "responsibilities": ["resp1", "resp2"],
      "skills": ["skill1", "skill2"]
    }
  ],
  "references": [
    {
      "title": "Reference Title",
      "url": "https://...",
      "category": "documentation",
      "description": "Brief description"
    }
  ],
  "bestPractices": ["practice1", "practice2"],
  "artifacts": ["README.md", "references.md"]
}

Process Integration

This skill integrates with:

  • specialization-creation.js - Phase 1 research
  • phase1-research-readme.js - README generation
  • domain-creation.js - Domain research

Best Practices

  1. Thorough Research: Cover multiple authoritative sources
  2. Organized Output: Structure findings logically
  3. Actionable Content: Focus on practical information
  4. Up-to-date References: Prioritize recent resources
  5. Validation: Verify links and facts

Constraints

  • Use WebSearch for broad topic exploration
  • Use WebFetch for specific URL content
  • Organize references by category
  • Validate all external links
  • Attribute sources properly