research-first-dev
Research-first development methodology that investigates existing solutions, brainstorms alternatives, and evaluates trade-offs before any implementation begins.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/research-first-dev/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How research-first-dev Compares
| Feature / Agent | research-first-dev | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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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