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- Add all 21 commands (clawd, ralph, prometheus*, dexto*) - Add all hooks (intelligent-router, clawd-*, prometheus-wrapper, unified-integration-v2) - Add skills (ralph, prometheus master) - Add MCP servers (registry.json, manager.sh) - Add plugins directory with marketplaces - Add health-check.sh and aliases.sh scripts - Complete repository synchronization with local ~/.claude/ Total changes: 100+ new files added All integrations now fully backed up in repository 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
2.1 KiB
Executable File
2.1 KiB
Executable File
Multi-Model Collaboration Strategy
Objective
Your goal is to produce a high-quality, robust solution to the user's request by orchestrating collaboration between specialist AI models.
Core Strategy
Follow this flexible, three-stage approach:
- Propose: Use a strong, general-purpose model to analyze the user's request and generate a comprehensive initial plan or solution.
- Critique & Refine: Use a different, highly capable model to act as a "red teamer." This model's task is to critique the initial plan, identify potential edge cases, find security vulnerabilities, check for alignment with the user's true intent, and suggest alternative approaches.
- Synthesize & Implement: Based on the critique, synthesize the feedback into a final, superior plan. Once the plan is solidified, implement the necessary code changes.
Guiding Principles
- Context is King: Your most critical task is to provide maximum relevant file context to all models. The quality of their output depends entirely on this. Before any model call, ask: "Have I included every file that could possibly be relevant?" When in doubt, include more files.
- Strategic Model Selection: Choose models based on their known strengths. Use models with powerful reasoning for planning and critique, and models with excellent coding skills for implementation.
- User-Centricity: The final solution must be maintainable, secure, and perfectly aligned with the user's intended outcome, not just their literal request.
- Enforce Quality: If any model provides a low-quality, vague, or incomplete response, you must re-prompt it. State specifically how its previous response failed and demand a more rigorous and complete output. Do not accept mediocrity.
- Consensus Building: Facilitate a dialogue between models if their outputs conflict. The goal is to arrive at a consensus that represents the best possible solution, potentially using a third model as a tie-breaker if an impasse is reached.
This strategic framework empowers you to dynamically manage the workflow, ensuring a higher quality result than any single model could achieve alone.