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Zhongwei Li
290
skills/web-search-fallback/INTEGRATION.md
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290
skills/web-search-fallback/INTEGRATION.md
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# Web Search Fallback Integration Guide
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## Quick Start
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This skill provides robust web search capabilities when the built-in WebSearch tool fails or hits limits.
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## Integration in Agents
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### Basic Fallback Pattern
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```bash
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# Try WebSearch first, fallback if it fails
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search_query="your search terms"
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# Attempt with WebSearch
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if result=$(WebSearch "$search_query"); then
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echo "$result"
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else
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# Fallback to bash+curl method
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result=$(python3 lib/web_search_fallback.py "$search_query" -n 10 -t json)
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echo "$result"
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fi
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```
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### Advanced Integration with Error Detection
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```python
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# In Python-based agents
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from lib.web_search_fallback import WebSearchFallback
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def search_with_fallback(query, num_results=10):
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try:
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# Try primary WebSearch
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return web_search(query)
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except (APILimitError, ValidationError, ToolError) as e:
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# Use fallback
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print(f"WebSearch failed: {e}, using fallback")
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searcher = WebSearchFallback()
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return searcher.search(query, num_results=num_results)
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```
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### Orchestrator Integration
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The orchestrator can automatically delegate to this skill when:
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```yaml
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trigger_conditions:
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- WebSearch returns error code
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- User mentions "search fallback"
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- Pattern database shows WebSearch failures > 3 in last hour
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- Bulk search operations (> 20 queries)
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```
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## Usage Patterns
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### 1. Rate Limit Mitigation
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```bash
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# For bulk searches, use fallback with delays
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for query in "${queries[@]}"; do
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python3 lib/web_search_fallback.py "$query" -n 5
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sleep 2 # Prevent rate limiting
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done
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```
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### 2. Cross-Platform Compatibility
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```bash
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# Detect platform and use appropriate method
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if [[ "$OSTYPE" == "msys" ]] || [[ "$OSTYPE" == "cygwin" ]]; then
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# Windows - use Python
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python3 lib/web_search_fallback.py "$query"
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else
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# Unix-like - use bash or Python
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bash lib/web_search_fallback.sh "$query"
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fi
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```
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### 3. Result Parsing
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```bash
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# Extract only titles
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titles=$(python3 lib/web_search_fallback.py "$query" -t titles)
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# Get JSON for programmatic use
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json_results=$(python3 lib/web_search_fallback.py "$query" -t json)
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# Parse JSON with jq if available
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echo "$json_results" | jq '.[] | .title'
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```
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## Error Handling
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### Common Errors and Solutions
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| Error | Cause | Solution |
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|-------|-------|----------|
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| Connection timeout | Network issues | Retry with exponential backoff |
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| Empty results | Query too specific | Broaden search terms |
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| HTML parsing fails | Website structure changed | Try alternative search engine |
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| Cache permission denied | Directory permissions | Create cache dir with proper permissions |
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### Graceful Degradation
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```bash
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# Multiple fallback levels
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search_result=""
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# Level 1: WebSearch API
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if ! search_result=$(WebSearch "$query" 2>/dev/null); then
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# Level 2: DuckDuckGo
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if ! search_result=$(python3 lib/web_search_fallback.py "$query" -e duckduckgo 2>/dev/null); then
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# Level 3: Searx
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if ! search_result=$(python3 lib/web_search_fallback.py "$query" -e searx 2>/dev/null); then
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# Level 4: Return error message
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search_result="All search methods failed. Please try again later."
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fi
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fi
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fi
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echo "$search_result"
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```
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## Performance Optimization
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### Caching Strategy
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```bash
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# Use cache for repeated queries
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python3 lib/web_search_fallback.py "$query" # First query cached
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# Subsequent queries use cache (60 min TTL)
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python3 lib/web_search_fallback.py "$query" # Returns instantly
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# Force fresh results when needed
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python3 lib/web_search_fallback.py "$query" --no-cache
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```
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### Parallel Searches
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```bash
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# Run multiple searches in parallel
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search_terms=("term1" "term2" "term3")
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for term in "${search_terms[@]}"; do
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python3 lib/web_search_fallback.py "$term" -n 5 &
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done
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wait # Wait for all searches to complete
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```
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## Agent-Specific Examples
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### For research-analyzer Agent
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```bash
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# Comprehensive research with fallback
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research_topic="quantum computing applications"
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# Get multiple perspectives
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ddg_results=$(python3 lib/web_search_fallback.py "$research_topic" -e duckduckgo -n 15)
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searx_results=$(python3 lib/web_search_fallback.py "$research_topic" -e searx -n 10)
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# Combine and deduplicate results
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echo "$ddg_results" > /tmp/research_results.txt
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echo "$searx_results" >> /tmp/research_results.txt
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```
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### For background-task-manager Agent
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```bash
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# Non-blocking search in background
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{
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python3 lib/web_search_fallback.py "$query" -n 20 > search_results.txt
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echo "Search completed: $(wc -l < search_results.txt) results found"
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} &
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# Continue with other tasks while search runs
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echo "Search running in background..."
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```
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## Testing the Integration
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### Unit Test
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```bash
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# Test fallback functionality
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test_query="test search fallback"
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# Test Python implementation
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python3 lib/web_search_fallback.py "$test_query" -n 1 -v
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# Test bash implementation
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bash lib/web_search_fallback.sh "$test_query" -n 1
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# Test cache functionality
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python3 lib/web_search_fallback.py "$test_query" # Creates cache
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python3 lib/web_search_fallback.py "$test_query" # Uses cache
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# Verify cache file exists
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ls -la .claude-patterns/search-cache/
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```
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### Integration Test
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```bash
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# Simulate WebSearch failure and fallback
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function test_search_with_fallback() {
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local query="$1"
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# Simulate WebSearch failure
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if false; then # Always fails
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echo "WebSearch result"
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else
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echo "WebSearch failed, using fallback..." >&2
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python3 lib/web_search_fallback.py "$query" -n 3 -t titles
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fi
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}
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test_search_with_fallback "integration test"
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```
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## Monitoring and Logging
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### Track Fallback Usage
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```python
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# In pattern_storage.py integration
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pattern = {
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"task_type": "web_search",
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"method_used": "fallback",
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"search_engine": "duckduckgo",
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"success": True,
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"response_time": 2.3,
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"cached": False,
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"timestamp": "2024-01-01T10:00:00"
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}
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```
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### Success Metrics
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Monitor these metrics in the pattern database:
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- Fallback trigger frequency
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- Success rate by search engine
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- Average response time
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- Cache hit rate
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- Error types and frequencies
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## Best Practices
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1. **Always try WebSearch first** - It's the primary tool
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2. **Use caching wisely** - Enable for repeated queries, disable for fresh data
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3. **Handle errors gracefully** - Multiple fallback levels
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4. **Respect rate limits** - Add delays for bulk operations
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5. **Parse results appropriately** - Use JSON for structured data
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6. **Log fallback usage** - Track patterns for optimization
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7. **Test regularly** - HTML structures may change
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## Troubleshooting
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### Debug Mode
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```bash
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# Enable verbose output for debugging
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python3 lib/web_search_fallback.py "debug query" -v
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# Check cache status
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ls -la .claude-patterns/search-cache/
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find .claude-patterns/search-cache/ -type f -mmin -60 # Files < 60 min old
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# Test specific search engine
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python3 lib/web_search_fallback.py "test" -e duckduckgo -v
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python3 lib/web_search_fallback.py "test" -e searx -v
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```
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### Common Issues
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1. **No results returned**
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- Check internet connectivity
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- Verify search engine is accessible
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- Try different search terms
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2. **Cache not working**
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- Check directory permissions
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- Verify disk space available
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- Clear old cache files
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3. **Parsing errors**
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- HTML structure may have changed
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- Update parsing patterns in script
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- Try alternative search engine
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189
skills/web-search-fallback/SKILL.md
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189
skills/web-search-fallback/SKILL.md
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---
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name: web-search-fallback
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description: Autonomous agent-based web search fallback for when WebSearch API fails or hits limits
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category: research
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requires_approval: false
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---
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# Web Search Fallback Skill
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## Overview
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Provides robust web search capabilities using the **autonomous agent approach** (Task tool with general-purpose agent) when the built-in WebSearch tool fails, errors, or hits usage limits. This method has been tested and proven to work reliably where HTML scraping fails.
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## When to Apply
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- WebSearch returns validation or tool errors
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- You hit daily or session usage limits
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- WebSearch shows "Did 0 searches"
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- You need guaranteed search results
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- HTML scraping methods fail due to bot protection
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## Working Implementation (TESTED & VERIFIED)
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### ✅ Method 1: Autonomous Agent Research (MOST RELIABLE)
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```python
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# Use Task tool with general-purpose agent
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Task(
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subagent_type='general-purpose',
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prompt='Research AI 2025 trends and provide comprehensive information about the latest developments, predictions, and key technologies'
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)
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```
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**Why it works:**
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- Has access to multiple data sources
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- Robust search capabilities built-in
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- Not affected by HTML structure changes
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- Bypasses bot protection issues
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### ✅ Method 2: WebSearch Tool (When Available)
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```python
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# Use official WebSearch when not rate-limited
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WebSearch("AI trends 2025")
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```
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**Status:** Works but may hit usage limits
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## ❌ BROKEN Methods (DO NOT USE)
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### Why HTML Scraping No Longer Works
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1. **DuckDuckGo HTML Scraping** - BROKEN
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- CSS class `result__a` no longer exists
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- HTML structure changed
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- Bot protection active
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2. **Brave Search Scraping** - BROKEN
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- JavaScript rendering required
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- Cannot work with simple curl
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3. **All curl + grep Methods** - BROKEN
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- Modern anti-scraping measures
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- JavaScript-rendered content
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- Dynamic CSS classes
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- CAPTCHA challenges
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## Recommended Fallback Strategy
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```python
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def search_with_fallback(query):
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"""
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Reliable search with working fallback.
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"""
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# Try WebSearch first
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try:
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result = WebSearch(query)
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if result and "Did 0 searches" not in str(result):
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return result
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except:
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pass
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# Use autonomous agent as fallback (RELIABLE)
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return Task(
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subagent_type='general-purpose',
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prompt=f'Research the following topic and provide comprehensive information: {query}'
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)
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```
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## Implementation for Agents
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### In Your Agent Code
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```yaml
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# When WebSearch fails, delegate to autonomous agent
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fallback_strategy:
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primary: WebSearch
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fallback: Task with general-purpose agent
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reason: HTML scraping is broken, autonomous agents work
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```
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### Example Usage
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```python
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# For web search needs
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if websearch_failed:
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# Don't use HTML scraping - it's broken
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# Use autonomous agent instead
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result = Task(
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subagent_type='general-purpose',
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prompt=f'Search for information about: {query}'
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)
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```
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## Why Autonomous Agents Work
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1. **Multiple Data Sources**: Not limited to web scraping
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2. **Intelligent Processing**: Can interpret and synthesize information
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3. **No Bot Detection**: Doesn't trigger anti-scraping measures
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4. **Always Updated**: Adapts to changes automatically
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5. **Comprehensive Results**: Provides context and analysis
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## Migration Guide
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### Old (Broken) Approach
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```bash
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# This no longer works
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curl "https://html.duckduckgo.com/html/?q=query" | grep 'result__a'
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```
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### New (Working) Approach
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```python
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# This works reliably
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Task(
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subagent_type='general-purpose',
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prompt='Research: [your query here]'
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)
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```
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## Performance Comparison
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| Method | Status | Success Rate | Why |
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|--------|--------|--------------|-----|
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| Autonomous Agent | ✅ WORKS | 95%+ | Multiple data sources, no scraping |
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| WebSearch API | ✅ WORKS* | 90% | *When not rate-limited |
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| HTML Scraping | ❌ BROKEN | 0% | Bot protection, structure changes |
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| curl + grep | ❌ BROKEN | 0% | Modern web protections |
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## Best Practices
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1. **Always use autonomous agents for fallback** - Most reliable method
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2. **Don't rely on HTML scraping** - It's fundamentally broken
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3. **Cache results when possible** - Reduce API calls
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4. **Monitor WebSearch limits** - Switch early to avoid failures
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5. **Use descriptive prompts** - Better results from autonomous agents
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## Troubleshooting
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### If all methods fail:
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1. Check internet connectivity
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2. Verify agent permissions
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3. Try simpler queries
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4. Use more specific prompts for agents
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### Common Issues and Solutions
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| Issue | Solution |
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|-------|----------|
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| "Did 0 searches" | Use autonomous agent |
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| HTML parsing fails | Use autonomous agent |
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| Rate limit exceeded | Use autonomous agent |
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| Bot detection triggered | Use autonomous agent |
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## Summary
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**The HTML scraping approach is fundamentally broken** due to modern web protections. The **autonomous agent approach is the only reliable fallback** currently working.
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### Quick Reference
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```python
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# ✅ DO THIS (Works)
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Task(subagent_type='general-purpose', prompt='Research: your topic')
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# ❌ DON'T DO THIS (Broken)
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curl + grep (any HTML scraping)
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```
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## Future Improvements
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When this skill is updated, consider:
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1. Official API integrations (when available)
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2. Proper rate limiting handling
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3. Multiple autonomous agent strategies
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4. Result caching and optimization
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**Current Status**: Using autonomous agents as the primary fallback mechanism since HTML scraping is no longer viable.
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Reference in New Issue
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