research-first-dev
Research-first development methodology executes structured investigation before implementation, analyzing requirements, searching existing solutions across GitHub and package registries, brainstorming multiple alternatives with trade-off scoring across complexity, time, risk and scalability axes, then recommending the optimal approach. Use this skill when selecting technologies, evaluating dependencies, planning feature development, or designing architectural changes to avoid premature implementation and costly rework.
git clone --depth 1 https://github.com/a5c-ai/babysitter /tmp/research-first-dev && cp -r /tmp/research-first-dev/library/methodologies/everything-claude-code/skills/research-first-dev ~/.claude/skills/research-first-devSKILL.md
# 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)
Review TypeScript code changes for consistency, type safety, and monorepo patterns across babysitter packages
Generate and validate documentation for @a5c-ai/babysitter-sdk CLI commands and exported APIs
Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.
Architect code review with DRY, YAGNI, abstraction, and test coverage principle enforcement