Write Java code for GEPA optimization with reflection clients and Pareto tradeoffs.
npx skills add https://github.com/ax-llm/ax --skill ax-java-gepa
Please help me install this Agent Skill. Skill: ax-java-gepa Source: https://github.com/ax-llm/ax/tree/main/website/static/java/.well-known/agent-skills/ax-java-gepa Command: npx skills add https://github.com/ax-llm/ax --skill ax-java-gepa
This Skill helps Java developers use Ax GEPA to optimize prompts, candidates, and reflection-driven workflows without guessing the correct generated-package API.
Use the ax-java-gepa skill to write Java code that creates an AxGEPA engine, runs optimization with a reflection client, and returns the optimization result.
High-intent search queries and answers about installing and using this skill.
To optimize LLM workflows in Java with GEPA, build optimization loops around reflection clients, metric budgets, and optimizer state to generate reproducible tuning results from the Ax engine.
Pareto tradeoff analysis in Java LLM optimization evaluates competing metrics simultaneously, enabling developers to inspect optimizer artifacts and replay candidates to identify optimal prompt configurations.
To seed optimization with BootstrapFewShot before running GEPA, initialize few-shot examples on the generated dev.axllm:ax package, then execute the GEPA engine to refine prompts and track candidate states.
Java LLM optimization supports both offline and real-network runs, allowing developers to choose between isolated testing and live execution based on their metric budgeting requirements.
Inspect optimizer artifacts for reproducible prompt tuning by examining generated docs, capability manifests, and candidate state tracking outputs produced by the Ax GEPA engine during optimization loops.
A reflection client drives iterative improvement in Java LLM prompt optimization by evaluating metric budgets and guiding the GEPA engine to generate package-accurate API usage with reproducible optimizer behavior.
ax-llmGenerate Python code for Ax GEPA optimization workflows in axllm.
ax-llmImplement Rust code for Ax GEPA optimization with axllm.
ax-llmGenerate Go guidance for Ax GEPA optimization workflows and artifact handling.
ax-llmGenerate evaluator-driven optimization workflows for Python Ax agents with axllm.
