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Plugin Scope Improvements: - Remove language-specialists plugin (not task-focused) - Split specialized-domains into 5 focused plugins: * blockchain-web3 - Smart contract development only * quantitative-trading - Financial modeling and trading only * payment-processing - Payment gateway integration only * game-development - Unity and Minecraft only * accessibility-compliance - WCAG auditing only - Split business-operations into 3 focused plugins: * business-analytics - Metrics and reporting only * hr-legal-compliance - HR and legal docs only * customer-sales-automation - Support and sales workflows only - Fix infrastructure-devops scope: * Remove database concerns (db-migrate, database-admin) * Remove observability concerns (observability-engineer) * Move slo-implement to incident-response * Focus purely on container orchestration (K8s, Docker, Terraform) - Fix customer-sales-automation scope: * Remove content-marketer (unrelated to customer/sales workflows) Marketplace Statistics: - Total plugins: 27 (was 22) - Tool coverage: 100% (42/42 tools referenced) - Fat plugins removed: 3 (language-specialists, specialized-domains, business-operations) - All plugins now have clear, focused tasks Model Migration: - Migrate all 42 tools from claude-sonnet-4-0/opus-4-1 to model: sonnet - Migrate all 15 workflows from claude-opus-4-1 to model: sonnet - Use short model syntax consistent with agent files Documentation Updates: - Update README.md with refined plugin structure - Update plugin descriptions to be task-focused - Remove anthropomorphic and marketing language - Improve category organization (now 16 distinct categories) Ready for October 9, 2025 @ 9am PST launch
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1.4 KiB
model
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| sonnet |
AI Prompt Optimization
Optimize the following prompt for better AI model performance: $ARGUMENTS
Analyze and improve the prompt by:
-
Prompt Engineering:
- Apply chain-of-thought reasoning
- Add few-shot examples
- Implement role-based instructions
- Use clear delimiters and formatting
- Add output format specifications
-
Context Optimization:
- Minimize token usage
- Structure information hierarchically
- Remove redundant information
- Add relevant context
- Use compression techniques
-
Performance Testing:
- Create prompt variants
- Design evaluation criteria
- Test edge cases
- Measure consistency
- Compare model outputs
-
Model-Specific Optimization:
- GPT-4 best practices
- Claude optimization techniques
- Prompt chaining strategies
- Temperature/parameter tuning
- Token budget management
-
RAG Integration:
- Context window management
- Retrieval query optimization
- Chunk size recommendations
- Embedding strategies
- Reranking approaches
-
Production Considerations:
- Prompt versioning
- A/B testing framework
- Monitoring metrics
- Fallback strategies
- Cost optimization
Provide optimized prompts with explanations for each change. Include evaluation metrics and testing strategies. Consider both quality and cost efficiency.