Plan Adjuster

When Cursor should use this skill

• Nightly job or immediately after a run is logged
• When the user reports fatigue/injury or requests easier/harder weeks
• When performance data indicates plan adjustment is needed
• When implementing adaptive training features or debugging adjustment logic

Invocation guidance

1. Load Plan, Workout, TrainingHistory, and RecentRunTelemetry[].
2. Apply deterministic ceilings from v0/lib/planAdaptationEngine.ts and v0/lib/plan-complexity-engine.ts before calling the model.
3. Return Adjustment[], optional RecoveryRecommendation, and confidence.
4. Only adjust future workouts - never modify completed runs.
5. Maintain weekly volume within safe limits (±20-30%).

Input schema (JSON)

{
  "profile": UserProfile,
  "currentPlan": Plan,
  "trainingHistory": TrainingHistory,
  "feedback": { "rpeTrend"?: number, "soreness"?: string, "sleepQuality"?: string }
}

Output schema (JSON)

{
  "appliedAt": string,
  "updates": Adjustment[],
  "recovery"?: RecoveryRecommendation,
  "confidence": "low" | "medium" | "high",
  "safetyFlags"?: SafetyFlag[]
}

Integration points

• API: v0/app/api/plan/adjust (to add), or chat-triggered adjustments
• Logic:
  • v0/lib/planAdjustmentService.ts - Adjustment orchestration
  • v0/lib/planAdaptationEngine.ts - Adaptive algorithms
  • v0/lib/plan-complexity-engine.ts - Safety caps
• UI: Plan/Today screens (badge adjusted sessions)
• Notifications: v0/lib/email.ts - Email user about significant adjustments
• Database: Update workouts table, log adjustments in plan_adjustments (if added)

Safety & guardrails

• Never rewrite completed history; adjust only future sessions.
• If fatigue/injury signals present, lower intensity/volume and consider rest-day insertion.
• Emit SafetyFlag on unsafe load proposals; clamp to deterministic caps.
• Maintain at least one rest day per week.
• If multiple negative signals, reduce load by at least one level.
• Hard stop on pain/injury mentions - recommend rest and professional consultation.

Adjustment types and triggers

Intensity adjustments

• Trigger: High RPE trend (>7 for easy runs), poor sleep, soreness
• Action: Reduce pace target by 15-30 seconds/km, lower HR zone
• Example: Tempo → Easy, Intervals → Tempo

Volume adjustments

• Trigger: Missed runs, fatigue, low consistency
• Action: Reduce duration by 10-30%, maintain intensity
• Example: 60min easy → 45min easy

Session swaps

• Trigger: Schedule conflicts, weather, preferences
• Action: Reschedule within same week, maintain weekly pattern
• Example: Tuesday intervals ↔ Thursday tempo

Rest day insertion

• Trigger: Multiple fatigue signals, injury risk, poor recovery
• Action: Replace easy run with rest or cross-training
• Example: Easy run → Rest day

Cross-training substitution

• Trigger: Soreness, minor injury, surface limitations
• Action: Replace easy run with cycling/swimming/walking
• Example: Easy run → 45min cycling

Telemetry

• Emit ai_skill_invoked and ai_adjustment_applied with:
  • adjustments_count
  • confidence
  • safety_flags
  • adjustment_types (array of change types)
  • user_id (hashed)
  • latency_ms

Common edge cases

• No adjustment needed: Return empty adjustments array, confirm plan is on track
• Conflicting signals: Prioritize safety, default to more conservative option
• Major deviation: Consider full plan regeneration instead of adjustments
• Peak training: Allow slightly higher load if race is imminent and recovery is adequate
• Taper period: Protect taper, only minor adjustments allowed

Testing considerations

• Test with various feedback combinations (RPE, soreness, sleep)
• Verify load caps are enforced
• Test that completed runs are never modified
• Validate adjustment rationale clarity
• Test with missing data (partial feedback)
• Verify SafetyFlag emission for risky adjustments