plan-adjuster

---

name: plan-adjuster description: Recomputes upcoming workouts based on recent runs and user feedback. Use when recent performance deviates from plan, user provides negative feedback, or recovery signals indicate adjustment needed with deterministic safety caps. metadata: short-description: Adaptive adjustment of future sessions with change logs and recovery tips.

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)

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

Output schema (JSON)

Copy{
  "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.