research-first-dev

Research-first development methodology that investigates existing solutions, brainstorms alternatives, and evaluates trade-offs before any implementation begins.

509 stars

Best use case

research-first-dev is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Research-first development methodology that investigates existing solutions, brainstorms alternatives, and evaluates trade-offs before any implementation begins.

Teams using research-first-dev should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/research-first-dev/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/methodologies/everything-claude-code/skills/research-first-dev/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/research-first-dev/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How research-first-dev Compares

Feature / Agentresearch-first-devStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Research-first development methodology that investigates existing solutions, brainstorms alternatives, and evaluates trade-offs before any implementation begins.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Research-First Development

## Overview

Research-first development methodology adapted from the Everything Claude Code project. Mandates investigation of existing solutions and alternatives before writing any code.

## Research Process

### 1. Problem Analysis
- Parse the request into specific technical requirements
- Identify the domain and relevant technology stack
- List known constraints (time, resources, compatibility)
- Define success criteria

### 2. Existing Solution Search
- Search GitHub for similar implementations
- Check package registries (npm, PyPI, crates.io, etc.)
- Review documentation for framework-specific solutions
- Identify relevant design patterns
- Check for known anti-patterns to avoid

### 3. Alternative Brainstorming
- Generate at least 3 alternative approaches
- Include a "build" option and at least one "buy/reuse" option
- Consider unconventional approaches

### 4. Trade-Off Evaluation
- Complexity: implementation effort, learning curve
- Time: development timeline, time-to-value
- Risk: failure modes, dependency risks, maintenance burden
- Scalability: growth limits, performance under load
- Score each alternative on all 4 axes

### 5. Recommendation
- Rank alternatives by composite score
- Provide clear recommendation with justification
- Include risk mitigation plan for chosen approach
- Define go/no-go criteria

## Iterative Retrieval
- Start broad, narrow based on findings
- Use confidence scoring to decide when to stop
- Maximum 3 retrieval rounds per topic
- Cache findings for reuse in subsequent phases

## When to Use

- New feature development (always)
- Architecture changes
- Technology selection
- Dependency evaluation
- Performance optimization strategy

## Agents Used

- `planner` (primary consumer)
- `architect` (architecture-specific research)

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Workflow & Productivity