plan-adjuster

When Claude 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

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.

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, v0/lib/planAdaptationEngine.ts.
• UI: Plan/Today screens (badge adjusted sessions) and notifications via v0/lib/email.ts.

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.

Telemetry

• Emit ai_skill_invoked and ai_adjustment_applied with adjustments_count, confidence, safety_flags.