dspy-debugging-observability
This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.
npx skills add majiayu000/claude-skill-registry --skill dspy-debugging-observability --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# DSPy Debugging & Observability ## Goal Debug, trace, and monitor DSPy programs using built-in inspection, MLflow tracing, and custom callbacks for production observability. ## When to Use - Debugging unexpected outputs - Understanding multi-step program flow - Production monitoring (cost, latency, errors) - Analyzing optimizer behavior - Tracking LLM API usage ## Related Skills - Optimize programs: [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md) - Evaluate quality: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md) - Build agents: [dspy-react-agent-builder](../dspy-react-agent-builder/SKILL.md) ## Inputs | Input | Type | Description | |-------|------|-------------| | `program` | `dspy.Module` | Program to debug/monitor | | `callback` | `BaseCallback` | Optional custom callback (subclass of `dspy.utils.callback.BaseCallback`) | ## Outputs | Output | Type | Description | |--------|------|-------------| | `GLOBAL_HISTORY` | `list[dict]` | Raw execution trace from `dspy.clients.base_lm` | | `metrics` | `dict` | Cost, latency, token counts from callbacks | ## Workflow ### Phase 1: Basic Inspection with inspect_history() The simplest debugging approach: ```python i
- Goal
- When to Use
- Related Skills
- Inputs
- Outputs
- Workflow
- Phase 1: Basic Inspection with inspecthistory()
- Phase 2: MLflow Tracing
- Phase 3: Custom Callbacks for Production
- Phase 4: Sampling for High-Volume Production
- Best Practices
- Limitations
- Official Documentation
What does the dspy-debugging-observability skill do?
This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill dspy-debugging-observability --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.