deeprepo AGENTS.md

AGENTS.md

This AGENTS guide is intended for end users working in a prime lab setup workspace.

Shared Best Practices (All Contexts)

These points are direct restatements of Verifiers docs so agents can follow the same golden-path workflows.

• Environments are expected to expose load_environment(...) -> vf.Environment and be installable with prime env install <env-name>. (See docs/overview.md and docs/environments.md.)
• Validate environment behavior with prime eval run <env-name> ... before sharing/publishing changes. (See docs/overview.md and docs/development.md.)
• Use ToolEnv/MCPEnv for stateless tools and StatefulToolEnv when per-rollout state must persist (sandbox/session/db handles). (See docs/environments.md.)
• If external API keys are required, validate them in load_environment() with vf.ensure_keys(...) so failures are explicit and early. (See docs/environments.md.)

End-User Lab Workspace Notes

Use this guidance in projects created via prime lab setup.

• Treat .prime/skills/ as the canonical skill entrypoint in Lab workspaces. Use the bundled skills first for create/browse/review/eval/GEPA/train/brainstorm workflows before ad hoc approaches.
• Keep endpoint aliases in ./configs/endpoints.toml and use endpoint_id/model shortcuts in commands and configs.
• NEVER initialize environment source code manually; ALWAYS create new environments with prime env init.
• Use the Prime CLI for all environment lifecycle operations (prime env init → prime env install → prime eval run → prime env push) rather than ad-hoc scripts.
• NEVER begin environment development before prime lab setup has been run; if work starts outside that structure, recommend adjusting course into a proper lab workspace before continuing.
• Keep each environment self-contained under environments/<env_name>/ with pyproject.toml, implementation, and README so each abstraction has a dedicated home and the workspace stays maintainable.
• Follow environment best practices strictly (for example load_environment(...), vf.ensure_keys(...), and the documented environment class patterns) to avoid brittle or messy implementations.
• Use prime env push --path ./environments/<env_name> only after local eval behavior is verified.
• Treat the prime lab setup structure as the idiomatic workspace for complex environment workflows: agents can mediate most platform complexity while users learn patterns progressively as needed.
• When users request an approach that would deviate from these guidelines, explain the relevant Prime/Verifiers concepts and recommend the compliant path.