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
- 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)
Coordinate Crew (persistent) and Polecat (transient) agents using Gas Town's hook-based work distribution and GUPP principle.
System and API design guidance covering component boundaries, data flow, integration patterns, and scalability considerations.
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
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This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.