content-agent

name content-agent description Generates personalized marketing content for Unite-Hub. Creates followup emails, proposals, and case studies based on contact data and interaction history. Uses Claude AI for high-quality, contextual content generation. Content Generation Agent Skill Overview The Content Agent creates personalized, high-converting marketing content by: Reading contact profiles and interaction history Analyzing engagement patterns and sentiment Generating contextually relevant content using Claude Storing drafts for human review/approval Tracking performance metrics Content Types 1. Followup Email When to generate: Contact received email 7+ days ago (nextFollowUp date passed) Status is "lead" or "prospect" AI score > 60 (engaged) Context to include: Reference their last interaction Mention their company/industry Highlight relevant case study or service Include clear CTA Example prompt: Generate a professional followup email for: - Name: John Smith - Company: TechStartup Inc - Job Title: CEO - Last interaction: "Interested in Q4 marketing services" - Sentiment: positive - Industry: Technology The email should: 1. Reference their interest in partnership 2. Mention 1 specific success story relevant to tech startups 3. Propose a 15-minute strategy call 4. Be warm but professional 5. Keep under 150 words 2. Proposal Email When to generate: Contact has shown high engagement (AI score > 80) Status is "prospect" Multiple positive interactions Context to include: Personalized value proposition Estimated ROI/results Timeline and deliverables Investment/pricing range Next steps Example prompt: Generate a proposal email for: - Name: Lisa Johnson - Company: eCommerce Solutions - Pain point: "Revamping marketing strategy" - Budget indicator: Mid-market (medium budget) - Timeline: Q4 2024 The proposal should: 1. Address their specific pain point 2. Outline 3-4 key deliverables 3. Mention expected metrics (e.g., "35% revenue increase") 4. Suggest 60-day engagement 5. Request a call to discuss 3. Case Study Reference When to generate: Contact from specific industry AI score indicates readiness Relevant success story exists Context to include: Similar company/industry case study Key metrics and results How it applies to their situation How the Agent Works Step 1: Identify Target Contacts Query contacts where: status = "prospect" OR "lead" aiScore > 60 nextFollowUp <= NOW Step 2: For Each Contact A. Load Contact History GET contact details GET contact's emails (interaction history) GET any previous generated content for this contact B. Build Context Object { name: "John Smith", company: "TechStartup Inc", jobTitle: "CEO", industry: "Technology", aiScore: 78, sentiment: "positive", lastInteraction: "Interested in Q4 partnership", emailsSent: 2, engagementDays: 15, hasProposalBefore: false } C. Determine Content Type Logic: IF aiScore > 80 AND !hasProposalBefore → Generate "proposal" ELSE IF aiScore > 60 AND lastInteraction > 7 days ago → Generate "followup" ELSE IF industry has matching case study → Generate "case_study_reference" ELSE → Generate "general_followup" D. Build Claude Prompt Template: You are a professional B2B marketing copywriter for a marketing agency. Generate a [CONTENT_TYPE] email for: - Name: [NAME] - Company: [COMPANY] - Job Title: [JOB_TITLE] - Industry: [INDUSTRY] - Last interaction: [LAST_INTERACTION] - Sentiment of previous emails: [SENTIMENT] - Our success with similar companies: [CASE_STUDY_BRIEF] Requirements: 1. Personalized to their specific situation 2. Reference their industry/company when possible 3. Include specific, measurable outcomes (if proposal) 4. Professional but warm tone 5. Clear call-to-action 6. [TYPE_SPECIFIC_REQUIREMENTS] Keep under [WORD_LIMIT] words. Generate the email body only (no "Subject:" or greeting). E. Call Claude API POST https://api.anthropic.com/v1/messages { "model": "claude-sonnet-4-5-20250929", "max_tokens": 1000, "system": "You are an expert B2B marketing copywriter...", "messages": [ { "role": "user", "content": "[BUILT_PROMPT]" } ] } F. Parse Response Extract text from response: response.content[0].text G. Store as Draft Call Convex mutation: POST convex mutation content.store({ orgId: "...", workspaceId: "...", contactId: "[CONTACT_ID]", contentType: "[TYPE]", title: "[AUTO_GENERATED_TITLE]", prompt: "[USED_PROMPT]", text: "[CLAUDE_RESPONSE]", aiModel: "sonnet", htmlVersion: null // Optional HTML formatting }) H. Log Audit Event POST convex mutation system.logAudit({ orgId: "...", action: "content_generated", resource: "generatedContent", agent: "content-agent", details: { contactId: "...", contentType: "[TYPE]", aiScore: 78, tokensUsed: 234 }, status: "success" }) Step 3: Summary Report Output: ✅ Content Generation Complete Total generated: X Followup emails: X Proposals: X Case studies: X Drafts awaiting approval: X By AI score: - High priority (>80): X contacts - Medium priority (60-80): X contacts Sample generated content: - John Smith (TechStartup): Followup email - Lisa Johnson (eCommerce): Proposal Next steps: 1. Review drafts in dashboard 2. Approve/edit content 3. Schedule for sending 4. Track performance metrics Error Handling If Claude API call fails: Log audit event with status: "error" Try fallback: Use template-based content Continue to next contact If contact data incomplete: Skip contact with warning Log as skipped in audit trail Performance Tracking After content is approved and sent: Track: - Opens (if integration available) - Clicks - Replies - Conversions Update generatedContent record with metrics: { status: "sent", sentAt: timestamp, performanceMetrics: { opens: 0, clicks: 0, replies: 0 } }