
Arize Prompt Optimization
Improves LLM prompts using production traces, evals and annotations via the ax CLI
- Repository
- github/awesome-copilot
- Stars
- 40,703
- Licence
- MIT
- Files
- 3 files
What it does
Once installed, the agent can pull real LLM call records (spans) from an Arize project, extract the prompts that were actually sent to a model, and look at performance signals such as human grades and automated eval explanations. It then runs a data-driven loop to suggest and test prompt improvements instead of guessing. It relies on the ax command-line tool to talk to Arize.
Great for
- Engineers maintaining an LLM app who already log traces to Arize
- Prompt engineers who want evidence from production failures rather than trial and error
- Teams with human annotations or LLM-as-judge evals they want to act on
Skip it if
- People without an Arize account or existing trace data, since the skill has nothing to analyse (requires setup first)
- Quick one-off wording tweaks to a prompt with no production data, where a plain chat is simpler
- Anyone expecting it to work without the ax CLI and API credentials configured
Try asking
- “Optimize the system prompt for my support bot in the my-workspace space using the failing traces from last week”
- “Extract the current prompt from the LLM spans in our summarizer project and reconstruct it as a messages array”
- “Find LLM spans with low eval scores and explain why the prompt is underperforming”
- “Improve the prompt for our ChatCompletion call based on human annotation labels”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 3 original files, the MIT licence and an attribution note.
arize-prompt-optimization.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip arize-prompt-optimization.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip arize-prompt-optimization.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Run ax spaces list and ax projects list first so the agent has the exact space and project names to target
- Provide annotated or eval-scored traces; the eval explanation fields are the most useful signal for improvements
- Review every proposed prompt change before deploying it to production, since the loop is data-driven but not validated by you
What's inside
- references/ax-profiles.md
- references/ax-setup.md
- SKILL.md
Redistributed unmodified from github/awesome-copilot under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.