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3802bca865
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
54 lines
1.4 KiB
Markdown
54 lines
1.4 KiB
Markdown
---
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model: sonnet
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---
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# AI Prompt Optimization
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Optimize the following prompt for better AI model performance: $ARGUMENTS
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Analyze and improve the prompt by:
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1. **Prompt Engineering**:
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- Apply chain-of-thought reasoning
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- Add few-shot examples
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- Implement role-based instructions
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- Use clear delimiters and formatting
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- Add output format specifications
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2. **Context Optimization**:
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- Minimize token usage
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- Structure information hierarchically
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- Remove redundant information
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- Add relevant context
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- Use compression techniques
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3. **Performance Testing**:
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- Create prompt variants
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- Design evaluation criteria
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- Test edge cases
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- Measure consistency
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- Compare model outputs
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4. **Model-Specific Optimization**:
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- GPT-4 best practices
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- Claude optimization techniques
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- Prompt chaining strategies
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- Temperature/parameter tuning
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- Token budget management
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5. **RAG Integration**:
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- Context window management
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- Retrieval query optimization
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- Chunk size recommendations
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- Embedding strategies
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- Reranking approaches
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6. **Production Considerations**:
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- Prompt versioning
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- A/B testing framework
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- Monitoring metrics
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- Fallback strategies
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- Cost optimization
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Provide optimized prompts with explanations for each change. Include evaluation metrics and testing strategies. Consider both quality and cost efficiency.
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