Multi Program Manager logo

Multi Program Manager

Organization
Affitor
multi-program-manager

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".

Overview

PublisherAffitor
Repositoryaffiliate-skills
Skill namemulti-program-manager
Stars
705
Forks
209
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Affitor on GitHub. Read the source before you install it.

Installation

Install the Multi Program Manager AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Affitor/affiliate-skills/tree/main/skills/automation/multi-program-manager
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Affitor/affiliate-skills.git /tmp/affiliate-skills
mkdir -p .claude/skills
cp -r /tmp/affiliate-skills/skills/automation/multi-program-manager .claude/skills/multi-program-manager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multi Program Manager in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Multi Program Manager on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Multi Program Manager is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

yaml
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

yaml
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

weekly_plan:
  total_hours: number
  allocation:
    - program: string
      hours: number
      tasks: string[]

Output Format

  1. Portfolio Dashboard — table with all programs, revenue, EPC, revenue share
  2. Portfolio Health — concentration risk, diversification, stability assessment
  3. Program Scorecards — per-program action (double down / maintain / optimize / phase out) with reason
  4. Strategic Recommendations — prioritized list of actions with Affitor skill references
  5. Weekly Action Plan — hour-by-hour allocation with specific tasks

Error Handling

  • Only one program: "You have a single program. That's 100% concentration risk. I'll analyze it and recommend 2-3 complementary programs using S1 (affiliate-program-search)."
  • No revenue data: "Without revenue data, I'll analyze based on commission structure and niche overlap. For deeper analysis, run S6.3 (performance-report) first to get your numbers."
  • All programs in same niche: "All your programs are in [niche]. You're diversified by product but not by market. If [niche] declines, all your income is at risk. Consider adding programs in adjacent niches."

Examples

Example 1: Portfolio with clear winner

User: "I promote HeyGen ($450/mo), Semrush ($320/mo), Notion ($125/mo), Canva ($80/mo). Which should I focus on?" Action: HeyGen is the star (46% revenue, likely highest EPC). Recommend: Double down on HeyGen (more blog content, S7 content-repurposer). Maintain Semrush. Optimize Notion (high conversion rate potential). Evaluate Canva (low revenue, is it worth the effort?). Weekly plan: 5h HeyGen, 2h Semrush, 2h Notion, 1h research.

Example 2: Diversification analysis

User: "I make $2K/month from 3 SaaS tools. How do I reduce risk?" Action: All income from one niche (SaaS) = moderate risk. Recommend: Add 1-2 programs in adjacent niches (e.g., online courses, hosting). Check commission types — if all one-time, recommend adding recurring programs. Use S1 to research programs in new niches.

Example 3: Program switching decision

User: "Should I drop Canva ($80/mo, 500 clicks) and replace it with Jasper?" Action: Canva EPC = $0.16 (low). Calculate opportunity cost: 500 clicks redirected to a $0.50+ EPC program = $250/mo potential. Research Jasper commission (likely $100+ per sale). Recommend: Yes, switch. Use S1 to evaluate Jasper, then S3 for a comparison blog post.

References

  • shared/references/affiliate-glossary.md — Portfolio and commission terminology. Referenced in Step 2.
  • shared/references/flywheel-connections.md — master flywheel connection map

Flywheel Connections

Feeds Into

  • commission-calculator (S1) — managed programs for portfolio calculation
  • funnel-planner (S8) — portfolio data for funnel planning

Fed By

  • affiliate-program-search (S1) — new programs to add to portfolio
  • conversion-tracker (S6) — performance data per program
  • performance-report (S6) — portfolio performance trends

Feedback Loop

  • performance-report (S6) reveals underperforming programs → recommend swaps or investment reallocation
yaml
chain_metadata:
  skill_slug: "multi-program-manager"
  stage: "automation"
  timestamp: string
  suggested_next:
    - "commission-calculator"
    - "performance-report"
    - "affiliate-program-search"

Frequently asked questions

What does the Multi Program Manager AI skill do?

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".

Why use Multi Program Manager on TypingMind?

Because you install it once and use it with any model. Multi Program Manager is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Multi Program Manager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Affitor/affiliate-skills/tree/main/skills/automation/multi-program-manager. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Multi Program Manager?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Multi Program Manager?

As many as you like. As long as a model supports skills, you can use Multi Program Manager with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Multi Program Manager AI skill free?

Yes. It is published on GitHub by Affitor under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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