conversational-goal-discovery

name: conversational-goal-discovery description: Chat-based goal classification (habit/distance/speed/race) with constraint clarification. Use during onboarding or when user wants to update their running goals through conversation with weekly commitment discovery. metadata: short-description: Chat-based goal classification with commitments and starter plan suggestions. agent: cursor

When Cursor should use this skill

• Early chat sessions or onboarding when the user's goal is ambiguous
• When the user asks for help choosing a plan or habit
• When user wants to clarify or update their running goals
• When implementing conversational onboarding features

Invocation guidance

1. Provide the last N ConversationTurn entries and any partial onboarding answers.
2. Classify goal (habit | distance | speed | race) with confidence and blockers.
3. Return a CoachMessage summary plus structured GoalDiscoveryResult.
4. Ask clarifying questions if confidence < 0.7.
5. Suggest weekly commitment (3-4 runs for beginners, 4-5 for intermediate, 5-6 for advanced).

Input schema (JSON)

{
  "conversation": ConversationTurn[],
  "profile": UserProfile,
  "partialOnboarding"?: Record<string, unknown>
}

Output schema (JSON)

{
  "goalDiscovery": {
    "goal": Goal,
    "confidence": number,
    "blockers": string[],
    "weeklyCommitment": number,
    "preferredDays"?: string[],
    "starterPlanId"?: string,
    "summaryCard": string,
    "safetyFlags"?: SafetyFlag[]
  },
  "coachMessage": CoachMessage
}

Integration points

• Chat API: v0/app/api/chat/route.ts - Conversational interface
• Prompt context:
  • v0/lib/conversationStorage.ts - Conversation history
  • v0/lib/onboardingPromptBuilder.ts - Onboarding prompts
• Handoff: trigger plan generation via v0/app/api/generate-plan/route.ts when confidence ≥0.7
• UI: Chat screen and onboarding wizard
• Database: Store conversation turns in chat_messages table

Safety & guardrails

• Avoid medical advice; if user mentions pain/injury, advise pause and professional consult.
• Keep responses concise (<120 words) and supportive.
• Emit SafetyFlag on harmful intents or ambiguous data.
• If user has injury history, recommend starting conservatively.
• Never promise specific performance outcomes or weight loss guarantees.

Conversation flow patterns

Goal discovery sequence

1. Initial question: "What brings you to running?" or "What are you hoping to achieve?"
2. Clarify constraints: "How many days per week can you commit?" "Any time restrictions?"
3. Assess experience: "What's your recent running history?"
4. Confirm goal: "So it sounds like [goal]. Is that right?"
5. Suggest next step: "Let me create a plan for you" or "Tell me more about..."

Goal types and indicators

• Habit: "consistency", "build routine", "just want to run"
• Distance: "5K", "10K", "half marathon", "marathon", specific distance target
• Speed: "get faster", "PR", "improve time", pace goals
• Race: mentions specific race, date, or event

Telemetry

• Emit ai_skill_invoked with:
  • goal (classified)
  • confidence
  • turns_count (conversation length)
  • latency_ms
• Emit ai_user_feedback when user responds to suggestions

Common edge cases

• Multiple goals: Ask user to prioritize primary goal
• Vague responses: Ask specific follow-up questions
• Unrealistic goals: Gently adjust expectations with rationale
• Injury mentions: Prioritize recovery, recommend professional consultation
• Low confidence: Continue conversation, don't force goal classification

Testing considerations

• Test with various conversation patterns (short, long, meandering)
• Verify confidence scoring accuracy
• Test with ambiguous or conflicting statements
• Validate SafetyFlag emission for injury mentions
• Test handoff to plan generation at confidence threshold