Social Engagement Responder

Phase 1: Classification

Classify each engagement into one of these categories:

Classification output:

{
  "category": "LEAD_SIGNAL",
  "confidence": "HIGH",
  "reasoning": "Asked about Shopify development pricing and mentioned they need help with their store",
  "suggested_action": "Reply with value, DM to continue conversation",
  "crm_flag": true
}

Phase 2: Reply Generation

LEAD_SIGNAL replies

• Acknowledge their need specifically
• Provide one piece of genuine value (tip, insight, quick observation)
• Soft pivot to conversation ("Happy to share more, DM me?" or "We actually helped a brand with exactly this")
• Never hard pitch in a public reply
• If DM: be more direct, offer a quick call or audit

CUSTOMER_QUESTION replies

• Answer directly and helpfully
• If complex, acknowledge + offer to follow up via DM/email
• Reference their specific situation if known
• Keep professional but warm

PARTNERSHIP_OPPORTUNITY replies

• Express interest without over-committing
• Ask a qualifying question ("What kind of collaboration are you thinking?")
• Offer to take it to DM/email

GENUINE_ENGAGEMENT replies

• Match their energy level
• Add value to the conversation (expand on a point, share a related insight)
• Ask a follow-up question to deepen engagement
• Keep it conversational, not corporate

SIMPLE_REACTION replies

• Short acknowledgment: "Thanks!", "Appreciate that", "Glad it resonated"
• Or skip entirely if volume is high (flag as "no reply needed")

TROLL_SPAM replies

• Do not reply
• Flag for manual review
• If borderline, respond once with facts, then disengage

Phase 3: Platform-Specific Formatting

Phase 4: Quality Check

1. Banned phrase scan: Check against outreach.banned_phrases
2. Self-promo check: Public replies should not read as ads. Lead with value.
3. Tone match: Reply tone should match the engagement's tone (serious reply to serious question, casual to casual)
4. Platform limits: Twitter replies under 280 chars
5. No emoji overload: Max 1-2 per reply

Phase 5: Output

Return structured JSON:

{
  "engagements": [
    {
      "platform": "linkedin",
      "type": "comment",
      "author": "Priya Mehta",
      "original_text": "This is really insightful. We've been struggling with our Shopify product pages for months.",
      "classification": {
        "category": "LEAD_SIGNAL",
        "confidence": "HIGH",
        "reasoning": "Mentions struggling with Shopify product pages, matches our CRO service",
        "crm_flag": true
      },
      "reply": "Priya, product pages are where most D2C brands leave the most revenue on the table. One quick win: make sure your above-the-fold shows social proof + a clear size/variant selector without scrolling. Happy to share a few more specifics if useful, feel free to DM.",
      "follow_up_action": "If she DMs, share Kibi Sports case study and offer a free CRO audit",
      "word_count": 47
    }
  ],
  "summary": {
    "total_processed": 12,
    "lead_signals": 2,
    "customer_questions": 1,
    "partnership": 0,
    "genuine_engagement": 5,
    "simple_reactions": 3,
    "troll_spam": 1,
    "replies_generated": 8,
    "skipped": 4
  },
  "generated_at": "2026-03-13T10:00:00Z"
}

Phase 6: CRM Handoff

For LEAD_SIGNAL engagements:

• Write to CRM via crm-writer with: author name, platform, signal text, classification, reply sent, follow-up action
• Set lead stage = NEW if not already in CRM
• Tag source = social_engagement

Example Usage

Trigger phrases:

• "Process today's LinkedIn comments and mentions"
• "Classify and reply to these Instagram DMs"
• "Handle the comments on my latest LinkedIn post"
• "Check for lead signals in this week's social engagement"
• "Draft replies for these Reddit mentions"
• "Triage my social media inbox"