How do I avoid performance overhead in high-traffic apps?

This skill includes a SamplingCallback pattern that allows you to log only a specific percentage of traces (e.g., 10%), reducing the overhead on your production system.

Can I track API costs in production using this skill?

Yes, the skill includes a ProductionMonitoringCallback template that estimates costs based on token usage and specific model pricing tables.

What metrics can I monitor with the custom callbacks?

You can monitor total cost, total tokens, call count, average cost per call, error counts, and specific LLM latencies.

Does this skill support MLflow integration?

Yes, it provides a 4-step workflow to enable MLflow autologging, allowing you to track traces, compiles, and evaluations in a centralized UI.

How do I inspect the last LLM call in a DSPy program?

You can use dspy.inspect_history(n=1) to print the most recent call to the console, or access dspy.clients.base_lm.GLOBAL_HISTORY programmatically for raw trace data.

DSPy Debugging & Observability

Name: DSPy Debugging & Observability
Author: OmidZamani

byOmidZamani

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분석 및 모니터링

Debugs, traces, and monitors DSPy programs using built-in inspection tools, MLflow integrations, and custom production callbacks.

This skill provides a comprehensive toolkit for developers to inspect the inner workings of DSPy pipelines, ranging from simple CLI history checks to sophisticated production-grade monitoring. It streamlines the process of tracking LLM API usage, analyzing multi-step program flows, and integrating with MLflow for centralized tracing. Whether you are troubleshooting unexpected outputs during development or managing costs and latency in a high-traffic environment, this skill equips Claude with the implementation patterns needed to ensure DSPy reliability and observability.

주요 기능

01Programmatic access to raw execution history and token usage metrics

02High-volume trace sampling strategies to minimize performance overhead

03Full MLflow autologging integration for centralized tracing

0431 GitHub stars

05Interactive LLM history inspection via inspect_history()

06Custom production callback patterns for real-time cost and latency tracking

사용 사례

01Identifying the root cause of hallucinated or incorrect outputs in DSPy programs

02Visualizing complex multi-step RAG pipeline flows and latency bottlenecks

03Monitoring token usage and estimating API costs in production deployments

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add omidzamani/dspy-skills dspy-debugging-observability

For use in Claude.ai and ChatGPT

Download Skill