| 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
}
}
