Orchestr8 Expert Assistance: $ARGUMENTS

I'll provide expert guidance on Orchestr8optimization and resource creation based on your request.

How to Load MCP Resources

CRITICAL: All orchestr8:// URIs in this workflow must be loaded using ReadMcpResourceTool with server: "plugin:orchestr8:orchestr8-resources" and the uri parameter set to the resource URI shown.

For detailed instructions and examples, load: orchestr8://guides/mcp-resource-loading

Token Efficiency Note

Traditional Approach (WASTEFUL):

• Load all documentation upfront: ~15,000 tokens
• Load all examples and patterns: ~8,000 tokens
• Total: ~23,000 tokens - most unused!

JIT Approach (OPTIMAL):

• Load expert agent: ~1,850 tokens
• Load specific patterns as needed: ~900-1,500 tokens
• Total: ~2,750-3,350 tokens - 85% reduction!

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Phase 1: Understanding Your Request (0-20%)

→ Load: orchestr8://agents/orchestr8-expert

Let me understand what you need help with:

Analyzing request: "$ARGUMENTS"

Common topics I can help with:

• Agent Creation: Building optimally-sized expert agents
• Skill Development: Creating focused skill fragments
• Workflow Optimization: Implementing JIT loading patterns
• Command Design: Progressive resource loading
• Pattern Application: Token reduction strategies
• Fragment Sizing: Optimal token counts and splits
• Metadata Optimization: useWhen scenarios and keywords
• Index Performance: Improving match relevance
• Budget Management: Token allocation across phases

→ Checkpoint: Request understood, expert loaded

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Phase 2: Loading Specific Guidance (20-60%)

Based on your topic, I'll load targeted expertise:

If creating new resources (agents, skills, patterns, examples, workflows):

→ Load: orchestr8://skills/orchestr8-optimization-patterns

Guidance areas:

• Fragment size optimization (500-1000 tokens)
• Metadata structure and useWhen scenarios
• Cross-reference patterns
• Content organization
• Validation checklist

If optimizing workflows or commands:

→ Load: orchestr8://skills/jit-loading-progressive-strategies

Guidance areas:

• JIT loading implementation
• Progressive phased loading
• Token budget management
• Match query optimization
• Phase checkpoint patterns

If working with fragments:

→ Load: orchestr8://skills/fragment-creation-workflow

Guidance areas:

• Fragment sizing decisions
• Core + Advanced splitting
• Family organization
• Fragment composition

If improving metadata and matching:

→ Load: orchestr8://skills/fragment-metadata-optimization

Guidance areas:

• useWhen scenario writing
• Keyword extraction
• Tag optimization
• Index performance

→ Checkpoint: Specific expertise loaded

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Phase 3: Providing Examples (60-80%, conditional)

If examples would help, I'll load relevant ones:

→ Load (if applicable): orchestr8://match?query=$ARGUMENTS+example&mode=index&maxResults=5

This will show you:

• Related example code and patterns
• Real-world implementations
• Best practices in action
• Common pitfall avoidance

→ Checkpoint: Examples provided (if needed)

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Phase 4: Applying Best Practices (80-100%)

Now I'll help you apply the optimization strategies:

Six Token Reduction Strategies:

1. JIT Loading (91-97% reduction)

• Load catalog upfront, fetch resources on-demand
• Use orchestr8://match queries with maxTokens
• Example: orchestr8://match?query=typescript+async&mode=index&maxResults=5

2. Fragment-Based Organization (25-40% additional)

• Target size: 500-1000 tokens (sweet spot: 650-850)
• Split if >1500 tokens: core + advanced modules
• Merge if <300 tokens

3. Index-Based Lookup (85-95% reduction)

• Write 5-20 keyword-rich useWhen scenarios
• Specific, technical terms enable O(1) index lookup
• Avoid generic phrases that trigger fuzzy fallback

4. Progressive Loading (50-78% savings)

• Core module: Essential functionality (500-700 tokens)
• Advanced module: Specialized features (600-800 tokens)
• Load advanced only when needed

5. Catalog-First Mode (54% savings)

• Use mode=catalog to explore metadata
• User selects specific resources to load
• Avoid mode=full unless specifically needed

6. Hierarchical Families (56% savings)

• Organize related fragments into families
• Parent covers overview, children handle specifics
• Prevents loading unnecessary siblings

Quick Validation Checklist:

For new resources:

□ Size: 500-1000 tokens (or <1500 max)
□ useWhen: 5-20 keyword-rich scenarios
□ Tags: 5-15 specific, searchable
□ Capabilities: 3-8 action-oriented
□ Cross-refs: 3-10 related resources
□ Content: Practical, copy-paste examples
□ Structure: Clear headings, best practices

For workflows/commands:

□ Uses orchestr8://match for JIT loading
□ maxTokens specified for each query
□ Phases clearly defined (0-X%, X-Y%, etc.)
□ Token budget tracked per phase
□ Checkpoints after each phase
□ Conditional loading for optional expertise
□ Total budget: 3,000-6,000 tokens target

For fragments:

□ Focused on single concern
□ Related concepts split into family
□ Core vs Advanced split if >1500 tokens
□ Cross-references for JIT navigation
□ Index rebuilt after changes

→ Checkpoint: Complete - Ready to implement!

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Token Budget Summary

This command's token usage:

• Phase 1 (Expert agent): 1,850 tokens
• Phase 2 (Specific skill): 900-1,500 tokens
• Phase 3 (Examples, conditional): 0-1,000 tokens
• Phase 4 (Application): 0 tokens (already loaded)
• Total: 2,750-4,350 tokens

vs Traditional approach: ~23,000 tokens Savings: 81-88% reduction 🎯

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Next Steps

Based on your topic "$ARGUMENTS", here's what to do next:

If creating a new resource:

1. Choose resource type: agent, skill, pattern, example, or workflow
2. Define scope: Single focused concern (500-1000 tokens)
3. Write metadata: Rich useWhen scenarios with keywords
4. Create content: Practical examples, best practices
5. Add cross-refs: Link 3-10 related resources
6. Validate: Check size, metadata, structure
7. Rebuild index: npm run build-index
8. Test matching: Query to verify discoverability

If optimizing existing resource:

1. Check token count: Target 500-1000 (max 1500)
2. If too large: Split into core + advanced
3. Review useWhen: Add keyword-rich scenarios
4. Add cross-refs: Enable JIT navigation
5. Update structure: Clear sections, examples
6. Rebuild index: npm run build-index
7. Test before/after: Compare match relevance

If creating workflow/command:

1. Define phases: 3-5 phases with clear goals
2. Add JIT loading: orchestr8://match queries
3. Set budgets: 1,000-2,000 tokens per phase
4. Add checkpoints: After each phase
5. Document savings: Show traditional vs JIT
6. Test token usage: Validate actual costs
7. Iterate: Adjust budgets based on reality

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Need More Help?

For detailed guidance on specific topics:

# Agent creation
/orchestr8:orchestr8-expert "How do I create an optimized agent?"

# Workflow optimization
/orchestr8:orchestr8-expert "Optimize my workflow with JIT loading"

# Fragment sizing
/orchestr8:orchestr8-expert "Should I split this 1800 token resource?"

# Metadata optimization
/orchestr8:orchestr8-expert "Writing better useWhen scenarios"

# Index performance
/orchestr8:orchestr8-expert "Why isn't my resource matching?"

For browsing resources directly:

// Explore the expert agent
orchestr8://agents/orchestr8-expert

// Quick reference patterns
orchestr8://skills/orchestr8-optimization-patterns

// Fragment creation workflow
orchestr8://skills/fragment-creation-workflow

// JIT loading strategies
orchestr8://skills/jit-loading-progressive-strategies

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Key Takeaways

Remember these principles:

1. ✅ Load Just-In-Time: Never load everything upfront
2. ✅ Fragment Everything: 500-1000 tokens, focused, composable
3. ✅ Rich Metadata: Powers indexing and discovery
4. ✅ Progressive Complexity: Core first, advanced on-demand
5. ✅ Catalog Before Content: Explore, then load selectively
6. ✅ Organize Hierarchically: Families prevent sibling bloat
7. ✅ Budget Every Phase: Track and optimize token usage
8. ✅ Validate Before Publishing: Use the checklists
9. ✅ Cross-Reference Everything: Enable JIT navigation
10. ✅ Rebuild Index: After any resource changes

You're now ready to create world-class token-efficient resources for Orchestr8! 🚀

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Actual Resources Loaded

The following resources were dynamically loaded during this command execution:

Phase 1:

• orchestr8://agents/orchestr8-expert (1,850 tokens)

Phase 2 (based on your topic):

• orchestr8://skills/orchestr8-optimization-patterns (~900 tokens)
• OR orchestr8://skills/jit-loading-progressive-strategies (~800 tokens)
• OR orchestr8://skills/fragment-creation-workflow (~750 tokens)
• OR orchestr8://skills/fragment-metadata-optimization (~700 tokens)

Phase 3 (conditional):

• orchestr8://match?query=$ARGUMENTS+example&mode=index&maxResults=5 (0-1,000 tokens)

Total tokens loaded: 2,750-4,350 tokens (vs 23,000 traditional) Efficiency achieved: 81-88% reduction through JIT loading