Mmcp.market

research-first-dev skill

by a5c-ai·a5c-ai/babysitter·1.8k stars·MIT

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

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Install the research-first-dev skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/a5c-ai/babysitter.git /tmp/babysitter
mkdir -p ~/.claude/skills
cp -r /tmp/babysitter/library/methodologies/everything-claude-code/skills/research-first-dev ~/.claude/skills/research-first-dev
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

  • 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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