home / skills / a5c-ai / babysitter / specialization-researcher

This skill enables systematic research of specialization domains, compiling references, analyzing best practices, and mapping roles to support new

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SKILL.md
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---
name: specialization-researcher
description: Research specialization domains, compile references, analyze best practices, and gather comprehensive knowledge for new specialization creation.
allowed-tools: Read Write Glob Grep WebFetch WebSearch
metadata:
  author: babysitter-sdk
  version: "1.0.0"
  category: research
  backlog-id: SK-META-001
---

# 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

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

### Compile References

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

## Output Format

```json
{
  "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

Overview

This skill researches and compiles comprehensive knowledge for creating new specializations within Babysitter-style agent frameworks. It synthesizes domain concepts, references, workflows, roles, and best practices into structured, actionable outputs for downstream domain creation and README generation.

How this skill works

The skill performs broad WebSearch to identify authoritative sources, then uses WebFetch to validate and extract content from specific URLs. It maps core concepts, documents roles and responsibilities, compiles categorized references, analyzes best practices and anti-patterns, and emits structured JSON outputs suitable for phase-one research and README generation.

When to use it

  • Starting a new specialization or domain within an agent orchestration framework
  • Preparing phase-1 research and README content for domain creation
  • Compiling curated, validated references for teams or documentation
  • Mapping stakeholder roles and collaboration patterns for a domain
  • Analyzing industry best practices and anti-patterns before design

Best practices

  • Cover multiple authoritative sources and prioritize recent materials
  • Organize findings by category: concepts, roles, workflows, references
  • Validate every external link and capture a brief description
  • Produce actionable recommendations and explicit anti-patterns
  • Format outputs for automated consumption (structured JSON, README, references file)

Example use cases

  • Research the data-engineering specialization: map ETL pipelines, roles, and tooling references
  • Compile 50 machine-learning references categorized into papers, tutorials, and tools
  • Analyze best practices for agentic-workflow design and document anti-patterns
  • Create a RACI-style stakeholder mapping for an orchestration domain
  • Generate a phase-1 README and a validated references.md for domain creation

FAQ

What output formats does the skill produce?

Structured JSON for programmatic use plus README-style summaries and a references file when requested.

How are references validated?

The skill checks link accessibility, extracts key metadata, categorizes each item, and flags broken or outdated links.