Technology Stack Analysis Mechanism
@team-configurator enables accurate technology identification through a multi-stage analysis process:
- Document Fingerprinting: scanning the project root for feature files (e.g. Gemfile=Ruby, package.json=Node.js, docker-compose.yml=containerization)
- Dependency analysis: parse the lock file (yarn.lock/pipfile.lock) to determine the specific framework version (e.g. Rails 7 vs 6)
- Directory structure validation: check standard framework structures (e.g. Django's apps directory, React's components hierarchy)
- Configuration file parsing: Read framework-specific configuration (e.g. next.config.js for Next.js, application.properties for Spring)
The output results contain:
- Technology Stack Matrix Report (main framework/supporting tools/test suite)
- List of recommended smartbody teams (@rspec-specialist is automatically included if RSpec is detected)
- Early warning of potential technology conflicts (e.g. alerts when both Flask and Django exist)
Typical scenario: When detecting the presence of both Django and Celery in a project, @distributed-task-expert is automatically configured to handle asynchronous tasks.
This answer comes from the articleClaude 007 Agents: an AI Intelligent Body Orchestration System for Modern Software DevelopmentThe
May not be reproduced without permission:AI productivity tools " How exactly does the @team-configurator intelligence analyze the project technology stack?
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