Prompt
Check a dataset before analysis
Find missing values, inconsistent formats, duplicates, and questions that could invalidate results.
ClaudeChatGPTGeminiData & Analytics
Dave AI
LangFuse Integration Skill
LangFuse LLM observability integration for tracing, analytics, and cost tracking
instructions.md
--- name: langfuse-integration-bc3ba5cab5be description: LangFuse LLM observability integration for tracing, analytics, and cost tracking allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:agent-debugging-logging, skill-area:model-monitoring-drift-detection] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # LangFuse Integration Skill ## Capabilities - Set up LangFuse tracing for LLM calls - Configure cost tracking and analytics - Implement prompt management - Set up evaluation datasets - Design custom trace metadata - Create dashboards and alerts ## Target Processes - llm-observability-monitoring - cost-optimization-llm ## Implementation Details ### Core Features 1. **Tracing**: Track LLM calls, chains, and agents 2. **Prompts**: Version and manage prompts 3. **Analytics**: Usage, latency, cost metrics 4. **Datasets**: Evaluation and testing data 5. **Scores**: Track output quality ### Integration Methods - LangChain callback handler - Direct SDK integration - OpenAI drop-in replacement - Decorator-based tracing ### Configuration Options - Public/secret keys - Host URL (cloud or self-hosted) - Sampling rate - Metadata configuration - User tracking ### Best Practices - Consistent trace naming - Meaningful metadata - Regular prompt versioning - Set up alerting ### Dependencies - langfuse - langchain (for callback integration)--- name: langfuse-integration-bc3ba5cab5be description: LangFuse LLM observability integration for tracing, analytics, and cost tracking allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:agent-debugging-logging, skill-area:model-monitoring-drift-detection] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # LangFuse Integration Skill ## Capabilities - Set up LangFuse tracing for LLM calls - Configure cost tracking and analytics - Implement prompt management - Set up evaluation datasets - Design custom trace metadata - Create dashboards and alerts ## Target Processes - llm-observability-monitoring - cost-optimization-llm ## Implementation Details ### Core Features 1. **Tracing**: Track LLM calls, chains, and agents 2. **Prompts**: Version and manage prompts 3. **Analytics**: Usage, latency, cost metrics 4. **Datasets**: Evaluation and testing data 5. **Scores**: Track output quality ### Integration Methods - LangChain callback handler - Direct SDK integration - OpenAI drop-in replacement - Decorator-based tracing ### Configuration Options - Public/secret keys - Host URL (cloud or self-hosted) - Sampling rate - Metadata configuration - User tracking ### Best Practices - Consistent trace naming - Meaningful metadata - Regular prompt versioning - Set up alerting ### Dependencies - langfuse - langchain (for callback integration)
Platform guidance
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