Individual skill
agent-performance
Deep-dive diagnosis of how your AI agent behaves in production. Explores LangWatch analytics and traces end to end to map failure patterns, dissatisfied users, token cost hotspots, edge cases, behavior changes, and outliers, then delivers an HTML report where every finding links to real example traces. Use when you want to truly understand what your agent is doing in production.
skills/_compiled/native/agent-performance/SKILL.md
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- Last checked
- Nov 9, 2026
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- Repository last updated
- Nov 9, 2026
- License
- Apache-2.0
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- ai Repository topic
- analytics Repository topic
- datasets Repository topic
- evaluation Repository topic
- gpt Repository topic
- llm Repository topic
- observability Repository topic
- openai Repository topic
- prompt-engineering Repository topic
- dspy Repository topic
- data Derived category
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# Diagnose Your Agent's Production Behavior
This skill is a production diagnostician. It reads the real traffic, not the code, and answers: what is my agent actually doing out there, where is it failing, who is it annoying, and where is the money going. It is read-only on the platform: the only thing it writes is a report file.
## Step 1: Set up the LangWatch CLI
## Step 2: Baseline the Vital Signs
Establish the macro picture first, always comparing against the previous period (the analytics API returns both periods for every query):
```bash
langwatch status # Resource counts and project overview
langwatch analytics query --metric trace-count --format json # Volume trend, last 7 days
langwatch analytics query --metric total-cost --format json # Spend trend