Phira Baseline Parity

Use this skill when implementing a feature that should be opt-in, when defaults must remain identical, or when reviewers ask "did you change baseline behavior?".

This skill is intended to be lazy-loaded on demand: only run parity A/B commands if the researcher approved running them.

Goal: demonstrate parity between baseline and modified code under the same configuration.

Core principle

If the acceptance criteria does not explicitly change defaults, your implementation should preserve baseline behavior by default.

Parity checklist

1. Feature gating

• Ensure the new behavior is behind a config/flag.
• Default should match baseline behavior unless the task explicitly changes defaults.

2. Controlled A/B

• Pick one minimal config that exercises the relevant path.
• Run A: baseline behavior (flag off / old path).
• Run B: same config with flag off (in the new codebase) to prove unchanged defaults.
• Run C (optional): flag on to prove the feature does something.

3. Fix confounders

• Same seed(s), same data split, same batch size, same precision settings.
• If determinism is not guaranteed, compare invariants and qualitative traces (e.g., loss decreases, shapes, number of steps, identical config resolution).

4. What to compare (choose what exists)

• Config resolution output / logged hyperparameters
• First-step loss (or first N steps) within tolerance
• Model parameter count / module tree
• Output tensor shapes and dtypes
• Checkpoint metadata

Reporting (copy/paste)

Baseline parity
- Default behavior preserved: <yes/no/unknown>
- Evidence:
  - A/B commands: <commands>
  - Compared: <what you compared>
  - Outcome: <match/tolerance/diff>
- If not preserved: <why + where documented + migration notes>