Plan Generator

Output schema (JSON)

Plan & { rationale: string; safetyFlags?: SafetyFlag[] }

Integration points

• API: v0/app/api/generate-plan/route.ts - Main plan generation endpoint
• Logic:
  • v0/lib/planGenerator.ts - Core plan generation logic
  • v0/lib/plan-templates.ts - Fallback template plans
  • v0/lib/periodization.ts - Training load periodization
  • v0/lib/plan-complexity-engine.ts - Safety caps and thresholds
• UI: Today and Plan screens (Dexie data via v0/lib/db.ts)
• Database: Plans stored in IndexedDB plans table with workouts in workouts table

Safety & guardrails

• No medical diagnosis; if pain/dizziness flags are present, reduce load and advise consulting a professional.
• Hard-cap weekly volume deltas via plan-complexity-engine.ts:
  • Weekly volume: max +20-30% increase
  • Long run: max +10-15% increase
  • Beginner cap: 30km/week maximum
• Emit SafetyFlag for load spikes or missing critical data.
• Always include at least one rest day per week.
• Progressive loading: easier first week to establish baseline.

Deterministic rules to enforce

• Rest distribution: minimum 1 rest day per week, prefer 2 for beginners
• Long run placement: Sunday or Saturday preferred
• Easy run proportion: 70-80% of weekly volume should be easy pace
• Interval frequency: max 2 hard sessions per week
• Recovery time: minimum 48h between hard sessions

Telemetry

• Emit ai_skill_invoked and ai_plan_generated with:
  • plan_version
  • fallback_used (true if template was used instead of AI)
  • safety_flags (any warnings or caps applied)
  • workouts_count
  • user_id (hashed)
  • latency_ms

Common edge cases

• No training history: Generate conservative beginner plan, emit SafetyFlag for missing_data
• Inconsistent history: Reduce volume target, add extra recovery
• Injury flags: Lower intensity, increase rest days, emit SafetyFlag for injury_signal
• Race goal without date: Request race date before generating plan
• Unrealistic goal: Adjust timeline or volume, explain rationale in plan

Testing considerations

• Mock UserProfile with various experience levels
• Test with empty/sparse TrainingHistory
• Verify load caps are enforced
• Check SafetyFlag emission for edge cases
• Validate JSON schema compliance
• Test fallback to template plans when AI fails