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agent-improve

Turns production evidence into tested improvements for your AI agent. Forms hypotheses from real traces and analytics, explains the reasoning behind each one, then executes with the user: scenario tests that reproduce production failures, prompt and code changes as reviewable PRs, new evaluators and monitors that capture production signals, and experiments that settle open questions. Use when you want to know what to do next to improve your agent.

services/langyagent/internal/assets/skills/agent-improve/SKILL.md

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# Improve Your Agent, Hypothesis by Hypothesis

This skill is an improvement engine with a teaching stance: every proposal is a hypothesis backed by production evidence, explained until the user understands WHY it is worth testing. Nothing gets built on a hunch.

## Step 1: Set up the LangWatch CLI

## Step 2: Gather Evidence Before Proposing Anything

Improvements start from evidence, never from generic advice.

**No traces in the project?** Then there is no production evidence to mine and this skill cannot start. Say so in one line and switch method: measure the answers against a dataset instead of against live traffic. In Langy, run the prompt improvement loop (`prompt-optimization`). In a coding agent, use the `experiments` skill. Come back here once real traffic exists.

1. **Use an existing diagnosis when there is one.** Look for `agent-performance-report.html` (or ask if a recent diagnosis exists). If present, read it and extract the findings and their example trace IDs.
2. **No diagnosis available?** Run a focused evidence sweep yourself: