Affiliate Program Search

Help affiliate marketers research, evaluate, and pick winning programs to promote. Data source: list.affitor.com — Affitor's community-driven affiliate program directory.

Stage

This skill belongs to Stage S1: Research

When to Use

• User wants to find affiliate programs to promote
• User wants to compare two or more affiliate programs
• User asks about commission rates, cookie duration, or earning potential
• User mentions list.affitor.com
• User is new to affiliate marketing and needs a starting point

Input Schema

{
  niche: string             # (optional, default: "AI/SaaS tools") Category or niche interest
  commission_pref: string   # (optional, default: "recurring, 20%+") Commission preference
  audience: string          # (optional, default: "content creators") Target audience type
  platform: string          # (optional, default: "any") Platform they'll promote on
  compare: string[]         # (optional) Specific programs to compare head-to-head
}

Workflow

Step 1: Understand What the User Wants

Ask (if not clear from context):

• Niche/category interest? (AI tools, SEO, video, writing, automation...)
• Commission preference? (recurring vs one-time, minimum %)
• Audience type? (developers, marketers, beginners, enterprise...)
• Platform they'll promote on? (blog, LinkedIn, YouTube, X...)

If user says "just find me something good" → default to: AI/SaaS tools, recurring commission, 20%+, content creator audience.

Step 2: Search list.affitor.com

See references/list-affitor-api.md for integration methods.

Two methods available:

• API (preferred): GET /api/v1/programs with API key auth — structured data, filterable
• Web fetch (fallback): web_search "site:list.affitor.com [category]" then web_fetch the page

Extract for each program: name, reward_value, reward_type, cookie_days, stars_count, tags, description.

Step 3: Score Programs

Apply the scoring framework from references/scoring-criteria.md.

Score each program on 5 dimensions (1-10 scale):

1. Earning Potential (30%) — commission %, recurring vs one-time, product price
2. Content Potential (25%) — visual demo, free tier, content angles
3. Market Demand (20%) — search volume, trend direction, market size
4. Competition Level (15%) — fewer affiliates promoting = higher score
5. Trust Factor (10%) — product quality, reputation, stars on list.affitor.com

Overall = weighted average. Verdict: 7.5+ "Strong Pick" / 5.5-7.4 "Worth Testing" / <5.5 "Skip".

For dimensions that require external data (Market Demand, Competition Level), use web_search to check Google results count for "[product] review" and "[product] affiliate" queries.

Step 4: Present Recommendation

Output Schema

Other skills (viral-post-writer, affiliate-blog-builder, etc.) consume these fields from conversation context:

{
  recommended_program: {
    name: string              # "HeyGen"
    slug: string              # "heygen"
    reward_value: string      # "30%"
    reward_type: string       # "cps_recurring"
    reward_duration: string   # "12 months"
    cookie_days: number       # 60
    description: string       # Short product description
    tags: string[]            # ["ai", "video"]
    url: string               # Product website
  }
  score: {
    overall: number           # 8.2
    verdict: string           # "Strong Pick"
    reasoning: string         # Why this is the top pick
  }
  runner_up: Program | null   # Same structure, second choice
  all_scored: ProgramScore[]  # Full list of scored programs
}

Output Format

## Programs Found

| Program | Commission | Type | Cookie | Stars | Score |
|---------|-----------|------|--------|-------|-------|
| HeyGen  | 30%       | Recurring | 60d | ⭐ 42 | 8.2/10 |
| ...     | ...       | ...  | ...    | ...   | .../10 |

## Top Pick: [Program Name]

**Why:** [2-3 sentences explaining why this is the best fit]

| Dimension | Score | Note |
|-----------|-------|------|
| Earning Potential | 8/10 | 30% recurring on $24-48/mo |
| Content Potential | 9/10 | Visual AI video, easy to demo |
| Market Demand | 8/10 | AI video trending, high search volume |
| Competition | 6/10 | Growing number of affiliates |
| Trust Factor | 8/10 | Strong brand, 42 stars on list.affitor.com |
| **Overall** | **8.2/10** | **Strong Pick** |

## Runner-up: [Program Name]

**Why:** [1-2 sentences]

## Next Steps

1. Sign up for [Program] affiliate program → [search for signup page]
2. Run `viral-post-writer` to create content for this product
3. Run `affiliate-blog-builder` to write a review post

Error Handling

• API unavailable: Fall back to web_fetch method (see references/list-affitor-api.md Method 2)
• No programs match criteria: Broaden search (remove strictest filter first), explain to user what was relaxed
• Stale data (program updated_at > 6 months): Flag with "Data may be outdated, verify on product website"
• User gives no criteria: Use defaults (AI/SaaS, recurring, 20%+, content creator audience)
• Program not on list.affitor.com: Use web_search to find program details directly, still apply scoring framework

Examples

Example 1: User: "I want to promote AI video tools, commission recurring, at least 20%" → Search list.affitor.com for programs tagged "ai" or "video" → Filter: reward_type = cps_recurring, reward_value ≥ 20% → Score and rank: HeyGen, Synthesia, ElevenLabs, InVideo AI... → Recommend top pick with full scorecard

Example 2: User: "Compare HeyGen vs Synthesia for my LinkedIn audience" → Fetch both from list.affitor.com → Score both, emphasize Content Potential for LinkedIn → Side-by-side comparison table + recommendation → Note: LinkedIn audience = B2B, weight higher-price products

Example 3: User: "I'm a beginner, what should I promote first?" → Default criteria: AI/SaaS, recurring, easy-to-demo products → Weight beginner-friendly factors: free tier, low payout threshold, strong brand → Recommend program with easiest path to first commission

References

• references/scoring-criteria.md — the 5-dimension scoring framework with rubrics
• references/list-affitor-api.md — how to fetch data from list.affitor.com (API + fallback)
• references/platform-rules.md — platform-specific considerations when recommending programs