evaluate

Evaluate recent changes — run the full before/after evaluation loop and recommend keep or undo

Run the universal evaluation loop on recent production changes.

  1. Check capability state — get_capability_state to see what evaluation modes are available:

    • normal — full measured evaluation with analyzer data
    • measured_degraded — analyzer online but stale data
    • judgment_only — no analyzer, parameter-level heuristics only
    • read_only — session disconnected

  2. Ensure analyzer — if mode is judgment_only, try to get full perception:

    • find_and_load_device(track_index=-1000, device_name="LivePilot_Analyzer")
    • Wait 2s, then get_master_spectrum to test the bridge
    • If bridge disconnected: reconnect_bridge
    • If still unavailable: proceed with judgment_only but tell the user

  3. Get the last move — get_last_move to understand what was changed. If no recent move, get_recent_actions for history.

  4. Ask what the goal was — what were they trying to achieve? More clarity? Wider stereo? Punchier drums?

  5. Compile the goal — compile_goal_vector(goal_description, mode="improve")

  6. Capture current state — full perception snapshot:

    • get_master_spectrum + get_master_rms (if analyzer available)
    • get_track_meters(include_stereo=true) — verify all tracks producing audio
    • get_mix_snapshot — full volume/pan/send state
    • Optionally: capture_audio + analyze_loudness + analyze_spectrum_offline for ground truth

  7. Undo the change — undo() to restore the before state

  8. Capture before state — same reads as step 6

  9. Redo the change — redo() to restore the after state

  10. Evaluate — evaluate_move(before_snapshot, after_snapshot, goal) or use engine-specific:

    • Mix changes: evaluate_mix_move
    • Composition changes: evaluate_composition_move
    • Multi-dimensional: evaluate_with_fabric

  11. Report results — show: score (0-1), keep_change recommendation, goal_progress, collateral_damage, dimension changes

  12. Act on recommendation:

    • If keep_change=true — keep, suggest memory_learn if score > 0.7
    • If keep_change=false — undo(), explain why (collateral damage, goal regression)

Use the livepilot-evaluation skill for the full evaluation loop details.