@nadavyigal/plan-adjuster

---
name: plan-adjuster
description: Recomputes upcoming workouts based on recent runs, feedback, and safety limits.
metadata:
  short-description: Adaptive adjustment of future sessions with change logs and recovery tips.
---

## When Codex should use it
- Nightly job or immediately after a run is logged.
- When the user reports fatigue/injury or requests easier/harder weeks.

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

## Output schema (JSON)
```ts
{
  "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`.