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From langfuse-traces
Query Langfuse traces for debugging LLM calls, analyzing token usage, and investigating workflow executions. Use when debugging AI/LLM behavior, checking trace data, or analyzing observability metrics.
How this skill is triggered — by the user, by Claude, or both
Slash command
/langfuse-traces:langfuse-tracesThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Query and analyze Langfuse trace data directly from Claude Code.
Query and analyze Langfuse trace data directly from Claude Code.
Invoke the query script using the base directory shown above:
bash <base_directory>/scripts/query.sh <command> [options]
| Command | Args | Description |
|---|---|---|
traces | [limit] [session_id] [name] | List recent traces |
trace | trace_id | Get full trace with observations |
observations | [limit] [trace_id] | List spans/generations |
sessions | [limit] | List sessions |
summary | [limit] | Compact one-line-per-trace view |
# List last 20 traces
bash <base_directory>/scripts/query.sh traces 20
# Get specific trace detail
bash <base_directory>/scripts/query.sh trace tr-abc123
# List observations for a trace
bash <base_directory>/scripts/query.sh observations 50 tr-abc123
# Quick summary of recent activity
bash <base_directory>/scripts/query.sh summary 10
The script reads credentials from .env.local, .env, or environment variables:
LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_SECRET_KEY=sk-lf-...
LANGFUSE_BASE_URL=https://cloud.langfuse.com # optional, default
curl (standard on macOS/Linux)jq for JSON parsingAll commands return JSON (piped through jq). Use jq filters for specific fields:
bash <base_directory>/scripts/query.sh traces 5 | jq '.data[].name'
npx claudepluginhub alberduris/skills --plugin langfuse-tracesAdds LangSmith tracing to Python/JS LLM apps via LangChain auto-tracing, traceable decorators, or OpenTelemetry; queries/export traces with langsmith CLI.
Debug AI agents and LLM applications via Langfuse MCP. Provides playbooks for trace inspection, exception triage, latency analysis, sessions, prompts, and datasets.
Implements Langfuse tracing for LLM calls via OpenAI wrappers, manual spans in RAG pipelines, streaming responses, and LangChain integration.