name	semantic-intelligence
description	Use Julie's semantic search capabilities for conceptual code understanding. Activates when searching for concepts, cross-language patterns, business logic, or exploring unfamiliar code. Combines text and semantic search for optimal results.
allowed-tools	mcp__julie__fast_search, mcp__julie__fast_explore, mcp__julie__trace_call_path, mcp__julie__fast_goto, mcp__julie__get_symbols

Semantic Intelligence Skill

Purpose

Leverage Julie's semantic understanding to find code by concept, not just keywords. Goes beyond text matching to understand what code does.

When to Activate

Searching for concepts ("authentication logic")
Finding cross-language patterns
Discovering business logic
Exploring unfamiliar codebases
When text search returns nothing
Understanding execution flows

Semantic vs Text Search

Text Search (exact/wildcard matching):

fast_search({ query: "console.error", mode: "text" })
→ Fast (<10ms)
→ Exact matches only
→ Misses variations ("logger.error", "print_error")

Semantic Search (conceptual understanding):

fast_search({ query: "error logging", mode: "semantic" })
→ Slower (~100ms)
→ Finds concepts
→ Discovers: console.error, logger.error, logging.error, errorHandler
→ Cross-language: Python logging, Rust tracing, Go log

Hybrid Search (best of both):

fast_search({ query: "authentication", mode: "hybrid" })
→ Runs text + semantic in parallel
→ Fuses results intelligently
→ Boosts symbols in BOTH searches
→ Optimal: ~150ms

When to Use Each Mode

Use Text Mode When:

✅ Searching for specific API names
✅ Finding exact strings
✅ Speed critical (<10ms)
✅ You know exact symbol name

Examples:
- "getUserData" → find specific function
- "console.log" → find exact API usage
- "import React" → exact import statement
- "TODO: fix" → exact comment

Use Semantic Mode When:

✅ Searching for concepts
✅ Cross-language patterns
✅ Don't know exact names
✅ Understanding what code does

Examples:
- "authentication logic" → find ALL auth-related code
- "error handling" → discover error patterns
- "database connections" → find DB code (MySQL, Postgres, etc.)
- "payment processing" → business logic discovery

Use Hybrid Mode When:

✅ Not sure which mode is best
✅ Want comprehensive results
✅ Concept + exact matches both useful
✅ Willing to wait ~150ms

Examples:
- "user authentication" → concept + exact matches
- "API endpoints" → finds routes, handlers, controllers
- "validation logic" → semantic concept + exact validators

Search Strategy Decision Tree

Know exact symbol name?
  YES → fast_goto("SymbolName")
        → <5ms, jumps to definition

Know exact API/string?
  YES → fast_search({ mode: "text" })
        → <10ms, exact matches

Searching for concept/behavior?
  YES → fast_search({ mode: "semantic" })
        → ~100ms, conceptual understanding

Not sure / want comprehensive?
  YES → fast_search({ mode: "hybrid" })
        → ~150ms, text + semantic fused

Looking for business logic specifically?
  YES → fast_explore({ mode: "logic", domain: "..." })
        → Filters framework noise, 5-tier CASCADE architecture

Cross-Language Semantic Matching

Julie's Superpower: Finds similar code across languages

Example: Finding "user validation" across codebase

fast_search({
  query: "user input validation",
  mode: "semantic",
  limit: 20
})

Results discovered:
- TypeScript: validateUser(input: UserInput)
- Python: def validate_user_input(data: dict)
- Rust: fn validate_user(user: &User) -> Result
- Go: func ValidateUserInput(input *UserInput) error
- Java: public boolean validateUser(User user)

→ Same CONCEPT, different languages
→ Naming variants automatically understood
→ Semantic embeddings capture meaning

Why this works: Embeddings encode what code does, not just what it's called

Business Logic Discovery

Problem: Framework code dominates search results

Solution: fast_explore (logic mode) filters noise, finds actual business logic

fast_explore({
  mode: "logic",
  domain: "payment",
  max_results: 20,
  min_business_score: 0.3
})

Filtering strategy (5-tier CASCADE):
1. Tier 1: SQLite FTS5 keyword search
2. Tier 2: Tree-sitter AST patterns (architectural classes/methods)
3. Tier 3: Path-based scoring (boosts services, penalizes utils/tests)
4. Tier 4: HNSW semantic search (conceptual similarity)
5. Tier 5: Relationship graph centrality (popular symbols)

→ Returns business logic, not framework boilerplate
→ Grouped by layer (controllers, services, models)

Common domains:

"payment" → payment processing logic
"auth" → authentication/authorization
"user" → user management
"order" → order processing
"notification" → notification systems

Semantic Search on Memories

Memories are searchable just like code:

# Find similar past decisions
fast_search({
  query: "database choice rationale",
  mode: "semantic",
  file_pattern: ".memories/**/*.json"
})

→ Discovers decision memories about DB selection
→ Finds learnings about database migrations
→ Cross-references related checkpoints

# Find bug fixes related to concept
fast_search({
  query: "race condition fixes",
  mode: "semantic",
  file_pattern: ".memories/**/*.json"
})

→ Returns past race condition bugs
→ Shows solutions that worked
→ Prevents repeating same debugging

Power move: Search codebase + memories together for complete understanding

Execution Flow Tracing (Cross-Language)

Unique capability: Trace calls across language boundaries

trace_call_path({
  symbol: "processPayment",
  direction: "downstream",  // what does this call?
  max_depth: 3
})

Execution flow discovered:
TypeScript: processPayment()
  → Rust: payment_processor::process() ← CROSSES LANGUAGE
    → SQL: stored_proc_charge() ← CROSSES AGAIN

→ Semantic matching finds cross-language connections
→ No other tool does this
→ ~200ms for multi-level traces

Directions:

upstream → Who calls this? (callers)
downstream → What does this call? (callees)
both → Full call graph

Hybrid Search Intelligence

How hybrid mode works:

1. Run text and semantic searches IN PARALLEL
2. Collect results from both
3. Score fusion algorithm:
   - Symbols in BOTH searches → boosted score
   - Text-only → normal score
   - Semantic-only → normal score
4. Sort by fused score
5. Return top results

Performance: ~150ms (parallelized)

When hybrid shines:

Query: "API error handling"

Text search finds:
- handleAPIError()
- api_error_handler.ts
- APIErrorHandler class

Semantic search finds:
- tryRequest() // catches errors
- errorBoundary() // React error handling
- logFailedRequest() // implicit error handling

Hybrid fusion:
- handleAPIError() ← BOTH searches (boosted!)
- tryRequest() ← semantic understanding
- api_error_handler.ts ← text match
→ Comprehensive results, best ranked first

Semantic Reference Discovery

Find semantically similar code:

# After finding a symbol with fast_goto
fast_refs({
  symbol: "UserService",
  include_definition: true
})

→ Returns ALL references (text-based)

# Semantic alternative (conceptual similarity)
fast_search({
  query: "UserService class implementation",
  mode: "semantic",
  search_target: "definitions"
})

→ Finds UserService + similar patterns:
  - AccountService (similar structure)
  - ProfileService (similar purpose)
  - CustomerService (semantic similarity)

Use case: "Find all classes similar to this pattern"

Workflow Patterns

Pattern 1: Unfamiliar Codebase

Step 1: Broad semantic search
  fast_search({ query: "main entry point", mode: "semantic" })

Step 2: Find business logic
  fast_explore({ mode: "logic", domain: "core", max_results: 30 })

Step 3: Trace execution
  trace_call_path({ symbol: "main", direction: "downstream", max_depth: 2 })

Step 4: Get structure
  get_symbols({ file_path: "main.ts", max_depth: 2 })

→ Understand codebase in <5 minutes

Pattern 2: "How is X implemented?"

Step 1: Semantic search
  fast_search({ query: "authentication implementation", mode: "semantic" })

Step 2: Find exact symbols
  fast_goto("authenticate")

Step 3: See usage
  fast_refs({ symbol: "authenticate" })

Step 4: Trace flow
  trace_call_path({ symbol: "authenticate", direction: "downstream" })

→ Complete understanding of feature

Pattern 3: Cross-Language Investigation

Step 1: Semantic search (all languages)
  fast_search({ query: "user validation logic", mode: "semantic" })

Step 2: Review results
  → TypeScript, Python, Rust implementations

Step 3: Compare patterns
  → See how each language handles validation
  → Identify best practices

Step 4: Trace connections
  trace_call_path({ symbol: "validateUser", direction: "both" })
  → Understand cross-service calls

Key Behaviors

✅ DO

Use semantic search for concepts and behaviors
Use text search for exact API/symbol names
Use hybrid when uncertain (comprehensive)
Search memories semantically (find past learnings)
Use fast_explore(mode="logic") to discover business code
Trace execution flows cross-language
Combine multiple search modes for completeness

❌ DON'T

Use semantic for exact symbol names (use fast_goto)
Use text search for concepts (misses variations)
Ignore hybrid mode (often best choice)
Forget to search memories (knowledge base!)
Skip cross-language tracing (unique capability)
Search without strategy (use decision tree)

Success Criteria

This skill succeeds when:

✅ Concepts found across languages
✅ Business logic discovered efficiently
✅ Execution flows traced completely
✅ Memories searched semantically
✅ Right search mode used for query type
✅ Cross-language patterns identified
✅ Unfamiliar code understood quickly

Performance

Text search: <10ms
Semantic search: ~100ms
Hybrid search: ~150ms
fast_explore (logic mode): ~200ms (5-tier CASCADE)
fast_explore (similar mode): ~100ms (HNSW embeddings)
fast_explore (dependencies mode): ~50ms (BFS graph traversal)
trace_call_path: ~200ms (cross-language)

Remember: Semantic understanding is Julie's superpower. Text finds what you ask for. Semantic finds what you mean.

The Rule: Searching for WHAT (exact) → text. Searching for WHY (concept) → semantic. Not sure → hybrid.

name	semantic-intelligence
description	Use Julie's semantic search capabilities for conceptual code understanding. Activates when searching for concepts, cross-language patterns, business logic, or exploring unfamiliar code. Combines text and semantic search for optimal results.
allowed-tools	mcp__julie__fast_search, mcp__julie__fast_explore, mcp__julie__trace_call_path, mcp__julie__fast_goto, mcp__julie__get_symbols

Semantic Intelligence Skill

Purpose

Leverage Julie's semantic understanding to find code by concept, not just keywords. Goes beyond text matching to understand what code does.

When to Activate

Searching for concepts ("authentication logic")
Finding cross-language patterns
Discovering business logic
Exploring unfamiliar codebases
When text search returns nothing
Understanding execution flows

Semantic vs Text Search

Text Search (exact/wildcard matching):

fast_search({ query: "console.error", mode: "text" })
→ Fast (<10ms)
→ Exact matches only
→ Misses variations ("logger.error", "print_error")

Semantic Search (conceptual understanding):

fast_search({ query: "error logging", mode: "semantic" })
→ Slower (~100ms)
→ Finds concepts
→ Discovers: console.error, logger.error, logging.error, errorHandler
→ Cross-language: Python logging, Rust tracing, Go log

Hybrid Search (best of both):

fast_search({ query: "authentication", mode: "hybrid" })
→ Runs text + semantic in parallel
→ Fuses results intelligently
→ Boosts symbols in BOTH searches
→ Optimal: ~150ms

When to Use Each Mode

Use Text Mode When:

✅ Searching for specific API names
✅ Finding exact strings
✅ Speed critical (<10ms)
✅ You know exact symbol name

Examples:
- "getUserData" → find specific function
- "console.log" → find exact API usage
- "import React" → exact import statement
- "TODO: fix" → exact comment

Use Semantic Mode When:

✅ Searching for concepts
✅ Cross-language patterns
✅ Don't know exact names
✅ Understanding what code does

Examples:
- "authentication logic" → find ALL auth-related code
- "error handling" → discover error patterns
- "database connections" → find DB code (MySQL, Postgres, etc.)
- "payment processing" → business logic discovery

Use Hybrid Mode When:

✅ Not sure which mode is best
✅ Want comprehensive results
✅ Concept + exact matches both useful
✅ Willing to wait ~150ms

Examples:
- "user authentication" → concept + exact matches
- "API endpoints" → finds routes, handlers, controllers
- "validation logic" → semantic concept + exact validators

Search Strategy Decision Tree

Know exact symbol name?
  YES → fast_goto("SymbolName")
        → <5ms, jumps to definition

Know exact API/string?
  YES → fast_search({ mode: "text" })
        → <10ms, exact matches

Searching for concept/behavior?
  YES → fast_search({ mode: "semantic" })
        → ~100ms, conceptual understanding

Not sure / want comprehensive?
  YES → fast_search({ mode: "hybrid" })
        → ~150ms, text + semantic fused

Looking for business logic specifically?
  YES → fast_explore({ mode: "logic", domain: "..." })
        → Filters framework noise, 5-tier CASCADE architecture

Cross-Language Semantic Matching

Julie's Superpower: Finds similar code across languages

Example: Finding "user validation" across codebase

fast_search({
  query: "user input validation",
  mode: "semantic",
  limit: 20
})

Results discovered:
- TypeScript: validateUser(input: UserInput)
- Python: def validate_user_input(data: dict)
- Rust: fn validate_user(user: &User) -> Result
- Go: func ValidateUserInput(input *UserInput) error
- Java: public boolean validateUser(User user)

→ Same CONCEPT, different languages
→ Naming variants automatically understood
→ Semantic embeddings capture meaning

Why this works: Embeddings encode what code does, not just what it's called

Business Logic Discovery

Problem: Framework code dominates search results

Solution: fast_explore (logic mode) filters noise, finds actual business logic

fast_explore({
  mode: "logic",
  domain: "payment",
  max_results: 20,
  min_business_score: 0.3
})

Filtering strategy (5-tier CASCADE):
1. Tier 1: SQLite FTS5 keyword search
2. Tier 2: Tree-sitter AST patterns (architectural classes/methods)
3. Tier 3: Path-based scoring (boosts services, penalizes utils/tests)
4. Tier 4: HNSW semantic search (conceptual similarity)
5. Tier 5: Relationship graph centrality (popular symbols)

→ Returns business logic, not framework boilerplate
→ Grouped by layer (controllers, services, models)

Common domains:

"payment" → payment processing logic
"auth" → authentication/authorization
"user" → user management
"order" → order processing
"notification" → notification systems

Semantic Search on Memories

Memories are searchable just like code:

# Find similar past decisions
fast_search({
  query: "database choice rationale",
  mode: "semantic",
  file_pattern: ".memories/**/*.json"
})

→ Discovers decision memories about DB selection
→ Finds learnings about database migrations
→ Cross-references related checkpoints

# Find bug fixes related to concept
fast_search({
  query: "race condition fixes",
  mode: "semantic",
  file_pattern: ".memories/**/*.json"
})

→ Returns past race condition bugs
→ Shows solutions that worked
→ Prevents repeating same debugging

Power move: Search codebase + memories together for complete understanding

Execution Flow Tracing (Cross-Language)

Unique capability: Trace calls across language boundaries

trace_call_path({
  symbol: "processPayment",
  direction: "downstream",  // what does this call?
  max_depth: 3
})

Execution flow discovered:
TypeScript: processPayment()
  → Rust: payment_processor::process() ← CROSSES LANGUAGE
    → SQL: stored_proc_charge() ← CROSSES AGAIN

→ Semantic matching finds cross-language connections
→ No other tool does this
→ ~200ms for multi-level traces

Directions:

upstream → Who calls this? (callers)
downstream → What does this call? (callees)
both → Full call graph

Hybrid Search Intelligence

How hybrid mode works:

1. Run text and semantic searches IN PARALLEL
2. Collect results from both
3. Score fusion algorithm:
   - Symbols in BOTH searches → boosted score
   - Text-only → normal score
   - Semantic-only → normal score
4. Sort by fused score
5. Return top results

Performance: ~150ms (parallelized)

When hybrid shines:

Query: "API error handling"

Text search finds:
- handleAPIError()
- api_error_handler.ts
- APIErrorHandler class

Semantic search finds:
- tryRequest() // catches errors
- errorBoundary() // React error handling
- logFailedRequest() // implicit error handling

Hybrid fusion:
- handleAPIError() ← BOTH searches (boosted!)
- tryRequest() ← semantic understanding
- api_error_handler.ts ← text match
→ Comprehensive results, best ranked first

Semantic Reference Discovery

Find semantically similar code:

# After finding a symbol with fast_goto
fast_refs({
  symbol: "UserService",
  include_definition: true
})

→ Returns ALL references (text-based)

# Semantic alternative (conceptual similarity)
fast_search({
  query: "UserService class implementation",
  mode: "semantic",
  search_target: "definitions"
})

→ Finds UserService + similar patterns:
  - AccountService (similar structure)
  - ProfileService (similar purpose)
  - CustomerService (semantic similarity)

Use case: "Find all classes similar to this pattern"

Workflow Patterns

Pattern 1: Unfamiliar Codebase

Step 1: Broad semantic search
  fast_search({ query: "main entry point", mode: "semantic" })

Step 2: Find business logic
  fast_explore({ mode: "logic", domain: "core", max_results: 30 })

Step 3: Trace execution
  trace_call_path({ symbol: "main", direction: "downstream", max_depth: 2 })

Step 4: Get structure
  get_symbols({ file_path: "main.ts", max_depth: 2 })

→ Understand codebase in <5 minutes

Pattern 2: "How is X implemented?"

Step 1: Semantic search
  fast_search({ query: "authentication implementation", mode: "semantic" })

Step 2: Find exact symbols
  fast_goto("authenticate")

Step 3: See usage
  fast_refs({ symbol: "authenticate" })

Step 4: Trace flow
  trace_call_path({ symbol: "authenticate", direction: "downstream" })

→ Complete understanding of feature

Pattern 3: Cross-Language Investigation

Step 1: Semantic search (all languages)
  fast_search({ query: "user validation logic", mode: "semantic" })

Step 2: Review results
  → TypeScript, Python, Rust implementations

Step 3: Compare patterns
  → See how each language handles validation
  → Identify best practices

Step 4: Trace connections
  trace_call_path({ symbol: "validateUser", direction: "both" })
  → Understand cross-service calls

Key Behaviors

✅ DO

Use semantic search for concepts and behaviors
Use text search for exact API/symbol names
Use hybrid when uncertain (comprehensive)
Search memories semantically (find past learnings)
Use fast_explore(mode="logic") to discover business code
Trace execution flows cross-language
Combine multiple search modes for completeness

❌ DON'T

Use semantic for exact symbol names (use fast_goto)
Use text search for concepts (misses variations)
Ignore hybrid mode (often best choice)
Forget to search memories (knowledge base!)
Skip cross-language tracing (unique capability)
Search without strategy (use decision tree)

Success Criteria

This skill succeeds when:

✅ Concepts found across languages
✅ Business logic discovered efficiently
✅ Execution flows traced completely
✅ Memories searched semantically
✅ Right search mode used for query type
✅ Cross-language patterns identified
✅ Unfamiliar code understood quickly

Performance

Text search: <10ms
Semantic search: ~100ms
Hybrid search: ~150ms
fast_explore (logic mode): ~200ms (5-tier CASCADE)
fast_explore (similar mode): ~100ms (HNSW embeddings)
fast_explore (dependencies mode): ~50ms (BFS graph traversal)
trace_call_path: ~200ms (cross-language)

Remember: Semantic understanding is Julie's superpower. Text finds what you ask for. Semantic finds what you mean.

The Rule: Searching for WHAT (exact) → text. Searching for WHY (concept) → semantic. Not sure → hybrid.

export default semantic-intelligence

Semantic Intelligence Skill

Purpose

When to Activate

Semantic vs Text Search

When to Use Each Mode

Use Text Mode When:

Use Semantic Mode When:

Use Hybrid Mode When:

Search Strategy Decision Tree

Cross-Language Semantic Matching

Business Logic Discovery

Semantic Search on Memories

Execution Flow Tracing (Cross-Language)

Hybrid Search Intelligence

Semantic Reference Discovery

Workflow Patterns

Pattern 1: Unfamiliar Codebase

Pattern 2: "How is X implemented?"

Pattern 3: Cross-Language Investigation

Key Behaviors

✅ DO

❌ DON'T

Success Criteria

Performance

Semantic Intelligence Skill

Purpose

When to Activate

Semantic vs Text Search

When to Use Each Mode

Use Text Mode When:

Use Semantic Mode When:

Use Hybrid Mode When:

Search Strategy Decision Tree

Cross-Language Semantic Matching

Business Logic Discovery

Semantic Search on Memories

Execution Flow Tracing (Cross-Language)

Hybrid Search Intelligence

Semantic Reference Discovery

Workflow Patterns

Pattern 1: Unfamiliar Codebase

Pattern 2: "How is X implemented?"

Pattern 3: Cross-Language Investigation

Key Behaviors

✅ DO

❌ DON'T

Success Criteria

Performance