KB Query Skill

KB Query Skill

Purpose

Interactive Knowledge Base search with natural language queries, providing fast access to institutional knowledge through relevance-ranked results with quality scoring.

How It Works

Step 1: Accept Query

User provides search query or problem description:

User: "Search KB for database connection pool issues"

Step 2: Execute Search

Run KB search with intelligent parameters:

# Basic search
make kb-search QUERY="database connection pool"

# With category filter
make kb-search QUERY="JWT authentication" CATEGORY=AUTH

# With quality threshold
make kb-search QUERY="performance optimization" MIN_SCORE=70

# All parameters
make kb-search QUERY="redis cache" CATEGORY=CACHE MIN_SCORE=60 LIMIT=10

Step 3: Display Results

Show top results with relevance and quality scores:

Search Results for "database connection pool"

Found 3 patterns:

1. DB-005: PostgreSQL Connection Pool Optimization
   Category: DB | Quality: 85/​100 | Relevance: 9.2/​10
   Keywords: postgresql, connection-pool, optimization, performance
   Problems solved:
   - Connection pool exhaustion under load
   - Slow query performance due to connection limits

2. PERF-002: Connection Pool Tuning Guide
   Category: PERF | Quality: 78/​100 | Relevance: 8.5/​10
   Keywords: connection-pool, tuning, performance, scalability
   Problems solved:
   - Optimizing pool size for workload
   - Monitoring pool utilization

3. DB-002: Database Connection Retry Logic
   Category: DB | Quality: 72/​100 | Relevance: 7.1/​10
   Keywords: database, connection, retry, error-handling
   Problems solved:
   - Transient connection failures
   - Graceful degradation on DB issues

Step 4: Offer Actions

Interactive follow-up options:

Actions:
1. View full content of a pattern (enter 1-3)
2. Search with different parameters
3. Browse by category
4. Exit

Your choice:

Step 5: View Full Content (if requested)

Display complete pattern details:

# Read full pattern file
cat docs/​kb/​patterns/​DB-005-postgresql-connection-pool.md

Show structured content:

# DB-005: PostgreSQL Connection Pool Optimization

## Metadata
- Category: DB
- Quality Score: 85/​100
- Created: 2025-10-15
- Author: senior-backend-engineer
- Time to solve: 120 minutes
- Complexity: Medium

## Problems This Solves
1. Connection pool exhaustion under load
2. Slow query performance due to connection limits
3. Connection timeout errors during peak traffic

## Solution

### Root Cause
Connection pool size (default: 10) insufficient for concurrent load (50+ requests/​sec).

### Implementation
```javascript
// PostgreSQL pool configuration
const pool = new Pool({
  max: 50,              // Increase from default 10
  min: 10,              // Maintain minimum connections
  idleTimeoutMillis: 30000,
  connectionTimeoutMillis: 5000,
});

// Add pool monitoring
pool.on('error', (err, client) => {
  console.error('Pool error:', err);
});

Validation Checklist

• Pool size matches concurrent request load
• Idle timeout configured to prevent stale connections
• Connection timeout set for fail-fast behavior
• Pool monitoring/​alerting enabled
• Load testing confirms no exhaustion

Related Patterns

• PERF-002: Connection Pool Tuning Guide
• DB-002: Database Connection Retry Logic
• INFRA-003: Database Scaling Strategies

Tags

#postgresql #connection-pool #optimization #performance #database

## Search Parameters

### Query (Required)
Natural language search terms:
- Error messages: "ECONNREFUSED"
- Problem descriptions: "slow database queries"
- Technology + issue: "JWT token expired"
- Keywords: "authentication", "caching", "optimization"

### Category (Optional)
Filter by specific category:
- **ARCH**: Architecture and design patterns
- **DB**: Database patterns
- **API**: API design and integration
- **AUTH**: Authentication and authorization
- **SECURITY**: Security best practices
- **TEST**: Testing patterns
- **PERF**: Performance optimization
- **FRONTEND**: Frontend patterns
- **INFRA**: Infrastructure patterns
- **DEPLOY**: Deployment patterns
- **CACHE**: Caching strategies
- **MCP**: MCP server patterns
- **CLIENT**: Client integration patterns
- **FLOW**: Workflow and process patterns

### MIN_SCORE (Optional)
Minimum quality score (0-100):
- **80+**: Exceptional quality (comprehensive, validated)
- **60-79**: High quality (production-ready)
- **40-59**: Medium quality (usable, may need improvements)
- **<40**: Low quality (review before use)

Default: 60 (high quality only)

### LIMIT (Optional)
Maximum results to return (default: 5)

### Fuzzy Matching
Automatically enabled for typo tolerance:
- "databse" → "database"
- "authentiction" → "authentication"
- "conection" → "connection"

## Examples

### Example 1: Database Error Search

**Query**: "ECONNREFUSED connecting to PostgreSQL"

**Command**:
```bash
make kb-search QUERY="ECONNREFUSED PostgreSQL" CATEGORY=DB

Results:

Found 2 results:

1. BUG-002: PostgreSQL Connection Refused
   Type: Bug Fix | Relevance: 9.5/​10
   Error: ECONNREFUSED, Could not connect to PostgreSQL
   Technologies: postgresql, docker, networking
   Fix Type: Configuration | Difficulty: Easy

2. DB-002: Database Connection Retry Logic
   Category: DB | Quality: 72/​100 | Relevance: 6.8/​10
   Keywords: database, connection, retry, error-handling

Action: View BUG-002 for exact fix steps

Example 2: Architecture Pattern Discovery

Query: "How to structure API endpoints for multi-tenant app"

Command:

make kb-search QUERY="multi-tenant API architecture" CATEGORY=ARCH MIN_SCORE=70

Results:

Found 3 patterns:

1. ARCH-004: Multi-Tenant API Design Patterns
   Category: ARCH | Quality: 88/​100 | Relevance: 9.7/​10

2. API-007: Tenant Isolation Strategies
   Category: API | Quality: 82/​100 | Relevance: 8.9/​10

3. DB-011: Multi-Tenant Database Schema Design
   Category: DB | Quality: 76/​100 | Relevance: 7.5/​10

Action: View ARCH-004 for comprehensive design approach

Example 3: No Results Found

Query: "quantum blockchain AI optimization"

Command:

make kb-search QUERY="quantum blockchain AI" MIN_SCORE=60

Results:

No results found for "quantum blockchain AI"

Suggestions:
1. Try broader search terms
2. Remove technical jargon
3. Search by specific error message
4. Browse categories: make kb-help
5. Create new pattern: make kb-pattern

Would you like to:
- Retry search with different terms? (r)
- Browse by category? (b)
- Create new pattern? (c)
- Exit? (e)

Example 4: Category Browse

Query: "Show all authentication patterns"

Command:

make kb-search QUERY="*" CATEGORY=AUTH

Results:

All AUTH patterns (7 total):

1. AUTH-001: JWT Token Authentication
   Quality: 92/​100 | Problems: 3 | Code examples: 5

2. AUTH-003: OAuth 2.0 Implementation
   Quality: 88/​100 | Problems: 4 | Code examples: 7

3. AUTH-005: Session Management Best Practices
   Quality: 85/​100 | Problems: 3 | Code examples: 4

... (4 more patterns)

Error Handling

No Results

No results found for "your query"

This might mean:
- Pattern doesn't exist yet - create one with `make kb-pattern`
- Query too specific - try broader terms
- Typo in category name - available: ARCH, DB, API, AUTH, etc.

KB Not Indexed

Error: KB index not found

Run: make kb-index

This will scan all patterns and build the search index.

Invalid Category

Error: Invalid category "DATABAS"

Valid categories:
ARCH, DB, API, AUTH, SECURITY, TEST, PERF, FRONTEND,
INFRA, DEPLOY, CACHE, MCP, CLIENT, FLOW

Example: make kb-search QUERY="your query" CATEGORY=DB

Integration

Uses

• Bash: Execute make kb-search commands
• Read: Display full pattern content
• Grep: Find related patterns by keyword
• Glob: Browse patterns by category

References

• docs/​kb/​KB.md: KB quick start guide
• docs/​kb/​INDEX.md: Organized pattern index
• docs/​agents/​KB-USAGE-GUIDE.md: Search workflow and API reference

Related Skills

• kb-create: Create new patterns after search fails
• root-cause-analyzer: Use KB results during 5 Whys analysis
• debugger: Search KB before debugging

Workflow Summary

1. Accept user query (natural language or keywords)
2. Execute KB search with appropriate parameters
3. Display top 3-5 results with scores and metadata
4. Offer interactive actions:
   • View full content
   • Refine search
   • Browse categories
   • Create new pattern (if no results)
5. Follow up based on user choice

Tips for Effective Searching

Use Specific Keywords

• Good: "JWT token expired 401"
• Bad: "authentication doesn't work"

Include Technology Names

• Good: "PostgreSQL connection pool exhaustion"
• Bad: "database is slow"

Copy Exact Error Messages

• Good: "ECONNREFUSED"
• Bad: "connection error"

Filter by Category

• Narrows results to relevant domain
• Faster than browsing all patterns

Adjust Quality Threshold

• Start with MIN_SCORE=60 (default)
• Lower to 40 for broader results
• Raise to 80 for only best patterns

Success Criteria

• Search completes in <3 seconds
• Relevance ranking surfaces best match first
• Quality scores help assess pattern reliability
• Fuzzy matching handles typos gracefully
• Interactive actions provide clear next steps
• Error messages are actionable and helpful