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physique forecasting and timeline expectations

v1

Provides data-driven projections for muscle accrual and fat loss based on starting metrics and training age.

Available
forecastingmuscle-gainfat-losstimelines

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FitnessGrid is an AI coach that plans your week and adapts as you go. Install physique forecasting and timeline expectations and your coach will follow this protocol every week, learn from what you actually do, and adjust on the fly.

  • Your coach builds the week from this skill
  • Adapts to your actual progress, not a static template
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Procedure

  1. Calculate Metric Baselines

    • Determine the user's starting state by identifying Total Body Mass (TBM) and Body Fat Percentage.
    • Use compute_lean_body_mass to establish the metabolically active tissue baseline.
    • Use compute_ffmi to assess the user's proximity to their genetic ceiling (referenced at 25 for natural males).

  2. Audit Training Age

    • Categorize the user's training status based on experience:
      • Novice: <1 year
      • Intermediate: 1–3 years
      • Advanced: 3+ years
    • Inform the user that the "slope" of their growth curve depends on this status.

  3. Forecast Muscle Accrual (The McDonald Model)

    • Apply the following potential rates of gain based on status:
      • Novice: ~2 lbs (0.9 kg) per month.
      • Intermediate: ~1 lb (0.45 kg) per month.
      • Advanced: ~0.5 lb (0.2 kg) per month.
      • Elite: Negligible.
    • Clarify that these figures assume optimized nutrition, stimulus, and recovery.

  4. Forecast Fat Loss Kinetics

    • Estimate fat loss at a standard rate of 0.5% to 1.0% of total body weight per week using estimate_weight_loss_timeline.
    • Apply LBM preservation constraints: If body fat is below 10% (men) or 18% (women), recommend slowing loss to <0.5% per week.
    • Explain the "Paper Towel Effect," where visual changes become more pronounced at lower body fat percentages even if weight loss speed remains constant.

  5. Synthesize the Timeline

    • Calculate the specific LBM gain and fat loss required to reach the target physique.
    • Sequence the timeline: muscle gain phases (bulking) and fat loss phases (cutting).
    • Apply an Adherence Coefficient: Multiply the theoretical timeline by 1.2x to 1.5x to account for real-world variables like illness or lifestyle interruptions.

  6. Communicate Genetic and Biological Limits

    • Advise that body recomposition (gaining muscle and losing fat simultaneously) is possible for novices but results in a slower timeline than sequenced phases.
    • Note that individual genetic variation (hormonal profiles, bone density) can shift these projections by ±20%.