Awesome Reviewers

When using configuration via .env/config files, make the notebook’s behavior match what the docs and environment files claim—and ensure fresh environments can run the notebook reliably.

Apply these standards: 1) Feature flags/config precedence must be documented correctly

• If code loads flags from .env (e.g., reloads via load_dotenv(override=True)), your markdown must say to set the flag in .env, not as an inline variable.
• Ensure the flag name in docs exactly matches the code (e.g., RUN_LIVE_RUNTIME vs RUN_LIVE).

2) Keep .env.example credential-safe and consistent with tooling

• Don’t include sample static AWS access keys as the recommended path.
• Prefer documenting aws configure or environment-variable/instance-role based credential providers.

3) Make imports portable across environments

• Avoid fragile relative imports that depend on the current working directory.
• If you need local-module imports, set sys.path based on the notebook/script location.

Example (portable import setup):

import os
import sys

current_dir = os.path.dirname(os.path.abspath(os.getcwd()))
sys.path.append(current_dir)

from custom_memory_prompts import consolidation_prompt, extraction_prompt

4) Ship explicit dependencies and install steps

• Use a requirements.txt for notebook dependencies and add Jupyter-friendly install instructions.
• Remove unused imports (e.g., import yaml if not used) to reduce confusion about required packages.

Outcome: configuration-driven execution is predictable (flags/precedence are correct), secure (credentials aren’t mishandled), and reproducible (imports/dependencies work on a clean setup).

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