/multi-program-manager

name: multi-program-manager
description: >
  Manage and compare multiple affiliate programs as a portfolio. Triggers on:
  "manage my affiliate programs", "compare my programs", "portfolio overview",
  "which program should I focus on", "diversify my affiliate income",
  "program switching", "affiliate portfolio", "program comparison",
  "revenue allocation", "which programs to drop", "add new programs",
  "affiliate program strategy".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "automation", "scaling", "workflow", "portfolio", "multi-program"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S7-Automation

Multi-Program Manager

Manage and compare multiple affiliate programs as a portfolio — overview, performance comparison, diversification strategy, program switching decisions, and revenue allocation. Output is a portfolio dashboard with strategic recommendations and a weekly action plan.

Stage

S7: Automation — Most affiliates either promote too few programs (concentration risk) or too many (effort dilution). This skill applies portfolio thinking to affiliate marketing: analyze your programs like investments, identify which to double down on, maintain, or drop, and allocate your limited time for maximum ROI.

When to Use

• User manages multiple affiliate programs and wants a strategic overview
• User asks "which program should I focus on?" or "should I drop this program?"
• User wants to diversify their affiliate income
• User says "compare my programs", "portfolio review", "program strategy"
• User is deciding whether to add or remove programs
• Chaining from S6.3 (performance-report): take performance data and make strategic decisions

Input Schema

programs:
  - name: string               # REQUIRED — program name
    affiliate_url: string      # OPTIONAL — affiliate link
    reward_value: string       # OPTIONAL — commission (e.g., "30% recurring")
    reward_type: string        # OPTIONAL — "cps_recurring" | "cps_one_time" | "cpl" | "cpc"
    monthly_revenue: number    # OPTIONAL — avg monthly revenue ($)
    monthly_clicks: number     # OPTIONAL — avg monthly clicks
    niche: string              # OPTIONAL — product category
    status: string             # OPTIONAL — "active" | "paused" | "new" | "considering"

goal: string                   # OPTIONAL — "maximize_revenue" | "diversify"
                               # | "reduce_risk" | "find_gaps"
                               # Default: "maximize_revenue"

budget_hours: number           # OPTIONAL — weekly hours available for content
                               # Default: 10

**Chaining context**: If S1 program research or S6.3 performance data exists in conversation, pull program details and metrics automatically.

Workflow

Step 1: Build Portfolio Overview

Compile all programs into a dashboard:

• Program name, niche, commission type, commission value
• Monthly revenue, clicks, EPC
• Status (active/paused/new)
• Revenue share (% of total)

Step 2: Calculate Per-Program Metrics

For each program with data:

• **EPC**: revenue / clicks
• **Revenue Share**: program revenue / total revenue × 100
• **Effort-to-Revenue Ratio**: estimated hours spent / revenue generated
• **Commission Quality Score**: recurring > one-time > per-lead > per-click

Step 3: Apply Portfolio Analysis

**Concentration Risk**:

• If top program > 50% of revenue → HIGH RISK
• If top 2 programs > 80% → MODERATE RISK
• If no program > 30% → WELL DIVERSIFIED

**Niche Overlap**:

• Multiple programs in same niche → competing for same audience
• Different niches → healthy diversification

**Revenue Stability**:

• Recurring commissions → stable
• One-time commissions → volatile (need constant new traffic)

Step 4: Generate Recommendations

For each program, assign an action:

• **Double Down**: High EPC, room to grow → create more content, scale traffic
• **Maintain**: Solid performer, no changes needed → keep existing content fresh
• **Optimize**: High traffic but low conversion → improve CTAs, landing pages, test variants
• **Phase Out**: Low EPC, low growth potential → redirect effort to better programs
• **Add**: Gap identified → research new programs with S1

Step 5: Create Action Plan

Based on `budget_hours`, allocate weekly time:

• Double-down programs get 50% of time
• Maintain programs get 20%
• Optimize programs get 20%
• New program research gets 10%

Provide specific weekly tasks tied to Affitor skills.

Step 6: Self-Validation

Before presenting output, verify:

• [ ] Revenue share percentages sum to ~100%
• [ ] EPC calculations correct (revenue ÷ clicks per program)
• [ ] Concentration risk accurate (flag if top program >50% of revenue)
• [ ] Actions match performance: double_down (Star), maintain (Cash Cow), optimize (Question Mark), phase_out (Dog)
• [ ] Weekly time allocation sums to user's stated hours budget

If any check fails, fix the output before delivering. Do not flag the checklist to the user — just ensure the output passes.

Output Schema

output_schema_version: "1.0.0"  # Semver — bump major on breaking changes
portfolio:
  total_programs: number
  active_programs: number
  total_monthly_revenue: number
  concentration_risk: string   # "high" | "moderate" | "low"
  niche_diversification: string # "good" | "overlapping" | "single_niche"
  revenue_stability: string    # "stable" | "moderate" | "volatile"

programs:
  - name: string
    niche: string
    reward_type: string
    monthly_revenue: number
    epc: number
    revenue_share: number
    action: string             # "double_down" | "maintain" | "optimize" | "phase_out"
    reason: string

recommendations:
  - action: string
    program: string
    skill: string              # which Affitor skill to use
    task: string               # specific task
    priority: number           # 1 = highest

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