// Optimize environment system prompts with GEPA through prime gepa run. Use when asked to improve prompt performance without gradient training, compare baseline versus optimized prompts, run GEPA from CLI or TOML configs, or interpret GEPA outputs before deployment.
Create or migrate verifiers environments for the Prime Lab ecosystem. Use when asked to build a new environment from scratch, port an eval or benchmark from papers or other libraries, start from an environment on the Hub, or convert existing tasks into a package that exposes load_environment and installs cleanly with prime env install.
Review verifiers environments for correctness, robustness, and ecosystem compatibility. Use when asked for environment code review, quality audit, migration validation, or release readiness checks for local environments or environments pulled from the Hub.
Discover and inspect verifiers environments through the Prime ecosystem. Use when asked to find environments on the Hub, compare options, inspect metadata, check action status, pull local copies for inspection, or choose environment starting points before evaluation, training, or migration work.
Train models with verifiers environments using hosted RL or prime-rl. Use when asked to configure RL runs, tune key hyperparameters, diagnose instability, set up difficulty filtering, or create practical train and eval loops for new environments.
Run and analyze evaluations for verifiers environments using prime eval. Use when asked to smoke-test environments, run benchmark sweeps, resume interrupted evaluations, compare models, inspect sample-level outputs, or produce evaluation summaries suitable for deciding next steps.
Run interactive brainstorming across verifiers environments, evaluations, GEPA, and RL training. Use when the user wants ideation, literature scanning, concept teaching, roadmap planning, or research program design grounded in local CLI sources, verifiers, and RL trainer code.
| name | optimize-with-environments |
| description | Optimize environment system prompts with GEPA through prime gepa run. Use when asked to improve prompt performance without gradient training, compare baseline versus optimized prompts, run GEPA from CLI or TOML configs, or interpret GEPA outputs before deployment. |
Use GEPA to optimize system prompts in a controlled, reproducible loop.
Current GEPA path is for system prompt optimization. If user asks for unsupported optimization targets, stop and clarify before proceeding.
configs/endpoints.toml.gpt-4.1 series, qwen3 instruct series.gpt-5 series, qwen3 thinking series, glm series.headers (or extra_headers) for custom HTTP headers. GEPA inherits these from the registry for both the main model and the reflection model:[[endpoint]]
endpoint_id = "my-proxy"
model = "gpt-4.1-mini"
url = "https://api.example/v1"
key = "OPENAI_API_KEY"
headers = { "X-Custom-Header" = "value" }
prime eval run. Keep the default save behavior and do not add --skip-upload unless the user explicitly requests that deviation:prime eval run my-env -m openai/gpt-4.1-mini -n 50 -r 3 -s
MultiTurnEnv examples.prime gepa run my-env -m openai/gpt-4.1-mini -M openai/gpt-4.1-mini -B 500 -n 100 -N 50
taskset and harness config sections:[[env]]
id = "my-env"
[env.taskset]
split = "train"
[env.harness]
max_turns = 8
prime gepa run configs/gepa/qwen-3-5.toml
-B/--max-calls: total optimization budget.-n/--num-train and -N/--num-val: train/validation split sizes.--minibatch-size: reflection granularity.--perfect-score: skip already-solved minibatches when max score is known.--state-columns: include environment-specific context in reflection data.Expect and inspect:
system_prompt.txtpareto_frontier.jsonlmetadata.json
Load optimized prompts with vf.SystemMessage.from_path("/path/to/system_prompt.txt") so the saved prompt is used verbatim.Return: